comparative case study software

What is Comparative Analysis and How to Conduct It? (+ Examples)

Appinio Research · 30.10.2023 · 36min read

Have you ever faced a complex decision, wondering how to make the best choice among multiple options? In a world filled with data and possibilities, the art of comparative analysis holds the key to unlocking clarity amidst the chaos.

In this guide, we'll demystify the power of comparative analysis, revealing its practical applications, methodologies, and best practices. Whether you're a business leader, researcher, or simply someone seeking to make more informed decisions, join us as we explore the intricacies of comparative analysis and equip you with the tools to chart your course with confidence.

What is Comparative Analysis?

Comparative analysis is a systematic approach used to evaluate and compare two or more entities, variables, or options to identify similarities, differences, and patterns. It involves assessing the strengths, weaknesses, opportunities, and threats associated with each entity or option to make informed decisions.

The primary purpose of comparative analysis is to provide a structured framework for decision-making by:

• Facilitating Informed Choices: Comparative analysis equips decision-makers with data-driven insights, enabling them to make well-informed choices among multiple options.
• Identifying Trends and Patterns: It helps identify recurring trends, patterns, and relationships among entities or variables, shedding light on underlying factors influencing outcomes.
• Supporting Problem Solving: Comparative analysis aids in solving complex problems by systematically breaking them down into manageable components and evaluating potential solutions.
• Enhancing Transparency: By comparing multiple options, comparative analysis promotes transparency in decision-making processes, allowing stakeholders to understand the rationale behind choices.
• Mitigating Risks : It helps assess the risks associated with each option, allowing organizations to develop risk mitigation strategies and make risk-aware decisions.
• Optimizing Resource Allocation: Comparative analysis assists in allocating resources efficiently by identifying areas where resources can be optimized for maximum impact.
• Driving Continuous Improvement: By comparing current performance with historical data or benchmarks, organizations can identify improvement areas and implement growth strategies.

Importance of Comparative Analysis in Decision-Making

• Data-Driven Decision-Making: Comparative analysis relies on empirical data and objective evaluation, reducing the influence of biases and subjective judgments in decision-making. It ensures decisions are based on facts and evidence.
• Objective Assessment: It provides an objective and structured framework for evaluating options, allowing decision-makers to focus on key criteria and avoid making decisions solely based on intuition or preferences.
• Risk Assessment: Comparative analysis helps assess and quantify risks associated with different options. This risk awareness enables organizations to make proactive risk management decisions.
• Prioritization: By ranking options based on predefined criteria, comparative analysis enables decision-makers to prioritize actions or investments, directing resources to areas with the most significant impact.
• Strategic Planning: It is integral to strategic planning, helping organizations align their decisions with overarching goals and objectives. Comparative analysis ensures decisions are consistent with long-term strategies.
• Resource Allocation: Organizations often have limited resources. Comparative analysis assists in allocating these resources effectively, ensuring they are directed toward initiatives with the highest potential returns.
• Continuous Improvement: Comparative analysis supports a culture of continuous improvement by identifying areas for enhancement and guiding iterative decision-making processes.
• Stakeholder Communication: It enhances transparency in decision-making, making it easier to communicate decisions to stakeholders. Stakeholders can better understand the rationale behind choices when supported by comparative analysis.
• Competitive Advantage: In business and competitive environments , comparative analysis can provide a competitive edge by identifying opportunities to outperform competitors or address weaknesses.
• Informed Innovation: When evaluating new products , technologies, or strategies, comparative analysis guides the selection of the most promising options, reducing the risk of investing in unsuccessful ventures.

In summary, comparative analysis is a valuable tool that empowers decision-makers across various domains to make informed, data-driven choices, manage risks, allocate resources effectively, and drive continuous improvement. Its structured approach enhances decision quality and transparency, contributing to the success and competitiveness of organizations and research endeavors.

How to Prepare for Comparative Analysis?

1. define objectives and scope.

Before you begin your comparative analysis, clearly defining your objectives and the scope of your analysis is essential. This step lays the foundation for the entire process. Here's how to approach it:

• Identify Your Goals: Start by asking yourself what you aim to achieve with your comparative analysis. Are you trying to choose between two products for your business? Are you evaluating potential investment opportunities? Knowing your objectives will help you stay focused throughout the analysis.
• Define Scope: Determine the boundaries of your comparison. What will you include, and what will you exclude? For example, if you're analyzing market entry strategies for a new product, specify whether you're looking at a specific geographic region or a particular target audience.
• Stakeholder Alignment: Ensure that all stakeholders involved in the analysis understand and agree on the objectives and scope. This alignment will prevent misunderstandings and ensure the analysis meets everyone's expectations.

2. Gather Relevant Data and Information

The quality of your comparative analysis heavily depends on the data and information you gather. Here's how to approach this crucial step:

• Data Sources: Identify where you'll obtain the necessary data. Will you rely on primary sources , such as surveys and interviews, to collect original data? Or will you use secondary sources, like published research and industry reports, to access existing data? Consider the advantages and disadvantages of each source.
• Data Collection Plan: Develop a plan for collecting data. This should include details about the methods you'll use, the timeline for data collection, and who will be responsible for gathering the data.
• Data Relevance: Ensure that the data you collect is directly relevant to your objectives. Irrelevant or extraneous data can lead to confusion and distract from the core analysis.

3. Select Appropriate Criteria for Comparison

Choosing the right criteria for comparison is critical to a successful comparative analysis. Here's how to go about it:

• Relevance to Objectives: Your chosen criteria should align closely with your analysis objectives. For example, if you're comparing job candidates, your criteria might include skills, experience, and cultural fit.
• Measurability: Consider whether you can quantify the criteria. Measurable criteria are easier to analyze. If you're comparing marketing campaigns, you might measure criteria like click-through rates, conversion rates, and return on investment.
• Weighting Criteria : Not all criteria are equally important. You'll need to assign weights to each criterion based on its relative importance. Weighting helps ensure that the most critical factors have a more significant impact on the final decision.

4. Establish a Clear Framework

Once you have your objectives, data, and criteria in place, it's time to establish a clear framework for your comparative analysis. This framework will guide your process and ensure consistency. Here's how to do it:

• Comparative Matrix: Consider using a comparative matrix or spreadsheet to organize your data. Each row in the matrix represents an option or entity you're comparing, and each column corresponds to a criterion. This visual representation makes it easy to compare and contrast data.
• Timeline: Determine the time frame for your analysis. Is it a one-time comparison, or will you conduct ongoing analyses? Having a defined timeline helps you manage the analysis process efficiently.
• Define Metrics: Specify the metrics or scoring system you'll use to evaluate each criterion. For example, if you're comparing potential office locations, you might use a scoring system from 1 to 5 for factors like cost, accessibility, and amenities.

With your objectives, data, criteria, and framework established, you're ready to move on to the next phase of comparative analysis: data collection and organization.

Comparative Analysis Data Collection

Data collection and organization are critical steps in the comparative analysis process. We'll explore how to gather and structure the data you need for a successful analysis.

1. Utilize Primary Data Sources

Primary data sources involve gathering original data directly from the source. This approach offers unique advantages, allowing you to tailor your data collection to your specific research needs.

Some popular primary data sources include:

• Surveys and Questionnaires: Design surveys or questionnaires and distribute them to collect specific information from individuals or groups. This method is ideal for obtaining firsthand insights, such as customer preferences or employee feedback.
• Interviews: Conduct structured interviews with relevant stakeholders or experts. Interviews provide an opportunity to delve deeper into subjects and gather qualitative data, making them valuable for in-depth analysis.
• Observations: Directly observe and record data from real-world events or settings. Observational data can be instrumental in fields like anthropology, ethnography, and environmental studies.
• Experiments: In controlled environments, experiments allow you to manipulate variables and measure their effects. This method is common in scientific research and product testing.

When using primary data sources, consider factors like sample size , survey design, and data collection methods to ensure the reliability and validity of your data.

2. Harness Secondary Data Sources

Secondary data sources involve using existing data collected by others. These sources can provide a wealth of information and save time and resources compared to primary data collection.

Here are common types of secondary data sources:

• Public Records: Government publications, census data, and official reports offer valuable information on demographics, economic trends, and public policies. They are often free and readily accessible.
• Academic Journals: Scholarly articles provide in-depth research findings across various disciplines. They are helpful for accessing peer-reviewed studies and staying current with academic discourse.
• Industry Reports: Industry-specific reports and market research publications offer insights into market trends, consumer behavior, and competitive landscapes. They are essential for businesses making strategic decisions.
• Online Databases: Online platforms like Statista , PubMed , and Google Scholar provide a vast repository of data and research articles. They offer search capabilities and access to a wide range of data sets.

When using secondary data sources, critically assess the credibility, relevance, and timeliness of the data. Ensure that it aligns with your research objectives.

3. Ensure and Validate Data Quality

Data quality is paramount in comparative analysis. Poor-quality data can lead to inaccurate conclusions and flawed decision-making. Here's how to ensure data validation and reliability:

• Cross-Verification: Whenever possible, cross-verify data from multiple sources. Consistency among different sources enhances the reliability of the data.
• Sample Size : Ensure that your data sample size is statistically significant for meaningful analysis. A small sample may not accurately represent the population.
• Data Integrity: Check for data integrity issues, such as missing values, outliers, or duplicate entries. Address these issues before analysis to maintain data quality.
• Data Source Reliability: Assess the reliability and credibility of the data sources themselves. Consider factors like the reputation of the institution or organization providing the data.

4. Organize Data Effectively

Structuring your data for comparison is a critical step in the analysis process. Organized data makes it easier to draw insights and make informed decisions. Here's how to structure data effectively:

• Data Cleaning: Before analysis, clean your data to remove inconsistencies, errors, and irrelevant information. Data cleaning may involve data transformation, imputation of missing values, and removing outliers.
• Normalization: Standardize data to ensure fair comparisons. Normalization adjusts data to a standard scale, making comparing variables with different units or ranges possible.
• Variable Labeling: Clearly label variables and data points for easy identification. Proper labeling enhances the transparency and understandability of your analysis.
• Data Organization: Organize data into a format that suits your analysis methods. For quantitative analysis, this might mean creating a matrix, while qualitative analysis may involve categorizing data into themes.

By paying careful attention to data collection, validation, and organization, you'll set the stage for a robust and insightful comparative analysis. Next, we'll explore various methodologies you can employ in your analysis, ranging from qualitative approaches to quantitative methods and examples.

Comparative Analysis Methods

When it comes to comparative analysis, various methodologies are available, each suited to different research goals and data types. In this section, we'll explore five prominent methodologies in detail.

Qualitative Comparative Analysis (QCA)

Qualitative Comparative Analysis (QCA) is a methodology often used when dealing with complex, non-linear relationships among variables. It seeks to identify patterns and configurations among factors that lead to specific outcomes.

• Case-by-Case Analysis: QCA involves evaluating individual cases (e.g., organizations, regions, or events) rather than analyzing aggregate data. Each case's unique characteristics are considered.
• Boolean Logic: QCA employs Boolean algebra to analyze data. Variables are categorized as either present or absent, allowing for the examination of different combinations and logical relationships.
• Necessary and Sufficient Conditions: QCA aims to identify necessary and sufficient conditions for a specific outcome to occur. It helps answer questions like, "What conditions are necessary for a successful product launch?"
• Fuzzy Set Theory: In some cases, QCA may use fuzzy set theory to account for degrees of membership in a category, allowing for more nuanced analysis.

QCA is particularly useful in fields such as sociology, political science, and organizational studies, where understanding complex interactions is essential.

Quantitative Comparative Analysis

Quantitative Comparative Analysis involves the use of numerical data and statistical techniques to compare and analyze variables. It's suitable for situations where data is quantitative, and relationships can be expressed numerically.

• Statistical Tools: Quantitative comparative analysis relies on statistical methods like regression analysis, correlation, and hypothesis testing. These tools help identify relationships, dependencies, and trends within datasets.
• Data Measurement: Ensure that variables are measured consistently using appropriate scales (e.g., ordinal, interval, ratio) for meaningful analysis. Variables may include numerical values like revenue, customer satisfaction scores, or product performance metrics.
• Data Visualization: Create visual representations of data using charts, graphs, and plots. Visualization aids in understanding complex relationships and presenting findings effectively.
• Statistical Significance: Assess the statistical significance of relationships. Statistical significance indicates whether observed differences or relationships are likely to be real rather than due to chance.

Quantitative comparative analysis is commonly applied in economics, social sciences, and market research to draw empirical conclusions from numerical data.

Case Studies

Case studies involve in-depth examinations of specific instances or cases to gain insights into real-world scenarios. Comparative case studies allow researchers to compare and contrast multiple cases to identify patterns, differences, and lessons.

• Narrative Analysis: Case studies often involve narrative analysis, where researchers construct detailed narratives of each case, including context, events, and outcomes.
• Contextual Understanding: In comparative case studies, it's crucial to consider the context within which each case operates. Understanding the context helps interpret findings accurately.
• Cross-Case Analysis: Researchers conduct cross-case analysis to identify commonalities and differences across cases. This process can lead to the discovery of factors that influence outcomes.
• Triangulation: To enhance the validity of findings, researchers may use multiple data sources and methods to triangulate information and ensure reliability.

Case studies are prevalent in fields like psychology, business, and sociology, where deep insights into specific situations are valuable.

SWOT Analysis

SWOT Analysis is a strategic tool used to assess the Strengths, Weaknesses, Opportunities, and Threats associated with a particular entity or situation. While it's commonly used in business, it can be adapted for various comparative analyses.

• Internal and External Factors: SWOT Analysis examines both internal factors (Strengths and Weaknesses), such as organizational capabilities, and external factors (Opportunities and Threats), such as market conditions and competition.
• Strategic Planning: The insights from SWOT Analysis inform strategic decision-making. By identifying strengths and opportunities, organizations can leverage their advantages. Likewise, addressing weaknesses and threats helps mitigate risks.
• Visual Representation: SWOT Analysis is often presented as a matrix or a 2x2 grid, making it visually accessible and easy to communicate to stakeholders.
• Continuous Monitoring: SWOT Analysis is not a one-time exercise. Organizations use it periodically to adapt to changing circumstances and make informed decisions.

SWOT Analysis is versatile and can be applied in business, healthcare, education, and any context where a structured assessment of factors is needed.

Benchmarking

Benchmarking involves comparing an entity's performance, processes, or practices to those of industry leaders or best-in-class organizations. It's a powerful tool for continuous improvement and competitive analysis.

• Identify Performance Gaps: Benchmarking helps identify areas where an entity lags behind its peers or industry standards. These performance gaps highlight opportunities for improvement.
• Data Collection: Gather data on key performance metrics from both internal and external sources. This data collection phase is crucial for meaningful comparisons.
• Comparative Analysis: Compare your organization's performance data with that of benchmark organizations. This analysis can reveal where you excel and where adjustments are needed.
• Continuous Improvement: Benchmarking is a dynamic process that encourages continuous improvement. Organizations use benchmarking findings to set performance goals and refine their strategies.

Benchmarking is widely used in business, manufacturing, healthcare, and customer service to drive excellence and competitiveness.

Each of these methodologies brings a unique perspective to comparative analysis, allowing you to choose the one that best aligns with your research objectives and the nature of your data. The choice between qualitative and quantitative methods, or a combination of both, depends on the complexity of the analysis and the questions you seek to answer.

How to Conduct Comparative Analysis?

Once you've prepared your data and chosen an appropriate methodology, it's time to dive into the process of conducting a comparative analysis. We will guide you through the essential steps to extract meaningful insights from your data.

1. Identify Key Variables and Metrics

Identifying key variables and metrics is the first crucial step in conducting a comparative analysis. These are the factors or indicators you'll use to assess and compare your options.

• Relevance to Objectives: Ensure the chosen variables and metrics align closely with your analysis objectives. When comparing marketing strategies, relevant metrics might include customer acquisition cost, conversion rate, and retention.
• Quantitative vs. Qualitative : Decide whether your analysis will focus on quantitative data (numbers) or qualitative data (descriptive information). In some cases, a combination of both may be appropriate.
• Data Availability: Consider the availability of data. Ensure you can access reliable and up-to-date data for all selected variables and metrics.
• KPIs: Key Performance Indicators (KPIs) are often used as the primary metrics in comparative analysis. These are metrics that directly relate to your goals and objectives.

2. Visualize Data for Clarity

Data visualization techniques play a vital role in making complex information more accessible and understandable. Effective data visualization allows you to convey insights and patterns to stakeholders. Consider the following approaches:

• Charts and Graphs: Use various types of charts, such as bar charts, line graphs, and pie charts, to represent data. For example, a line graph can illustrate trends over time, while a bar chart can compare values across categories.
• Heatmaps: Heatmaps are particularly useful for visualizing large datasets and identifying patterns through color-coding. They can reveal correlations, concentrations, and outliers.
• Scatter Plots: Scatter plots help visualize relationships between two variables. They are especially useful for identifying trends, clusters, or outliers.
• Dashboards: Create interactive dashboards that allow users to explore data and customize views. Dashboards are valuable for ongoing analysis and reporting.
• Infographics: For presentations and reports, consider using infographics to summarize key findings in a visually engaging format.

Effective data visualization not only enhances understanding but also aids in decision-making by providing clear insights at a glance.

3. Establish Clear Comparative Frameworks

A well-structured comparative framework provides a systematic approach to your analysis. It ensures consistency and enables you to make meaningful comparisons. Here's how to create one:

• Comparison Matrices: Consider using matrices or spreadsheets to organize your data. Each row represents an option or entity, and each column corresponds to a variable or metric. This matrix format allows for side-by-side comparisons.
• Decision Trees: In complex decision-making scenarios, decision trees help map out possible outcomes based on different criteria and variables. They visualize the decision-making process.
• Scenario Analysis: Explore different scenarios by altering variables or criteria to understand how changes impact outcomes. Scenario analysis is valuable for risk assessment and planning.
• Checklists: Develop checklists or scoring sheets to systematically evaluate each option against predefined criteria. Checklists ensure that no essential factors are overlooked.

A well-structured comparative framework simplifies the analysis process, making it easier to draw meaningful conclusions and make informed decisions.

4. Evaluate and Score Criteria

Evaluating and scoring criteria is a critical step in comparative analysis, as it quantifies the performance of each option against the chosen criteria.

• Scoring System: Define a scoring system that assigns values to each criterion for every option. Common scoring systems include numerical scales, percentage scores, or qualitative ratings (e.g., high, medium, low).
• Consistency: Ensure consistency in scoring by defining clear guidelines for each score. Provide examples or descriptions to help evaluators understand what each score represents.
• Data Collection: Collect data or information relevant to each criterion for all options. This may involve quantitative data (e.g., sales figures) or qualitative data (e.g., customer feedback).
• Aggregation: Aggregate the scores for each option to obtain an overall evaluation. This can be done by summing the individual criterion scores or applying weighted averages.
• Normalization: If your criteria have different measurement scales or units, consider normalizing the scores to create a level playing field for comparison.

5. Assign Importance to Criteria

Not all criteria are equally important in a comparative analysis. Weighting criteria allows you to reflect their relative significance in the final decision-making process.

• Relative Importance: Assess the importance of each criterion in achieving your objectives. Criteria directly aligned with your goals may receive higher weights.
• Weighting Methods: Choose a weighting method that suits your analysis. Common methods include expert judgment, analytic hierarchy process (AHP), or data-driven approaches based on historical performance.
• Impact Analysis: Consider how changes in the weights assigned to criteria would affect the final outcome. This sensitivity analysis helps you understand the robustness of your decisions.
• Stakeholder Input: Involve relevant stakeholders or decision-makers in the weighting process. Their input can provide valuable insights and ensure alignment with organizational goals.
• Transparency: Clearly document the rationale behind the assigned weights to maintain transparency in your analysis.

By weighting criteria, you ensure that the most critical factors have a more significant influence on the final evaluation, aligning the analysis more closely with your objectives and priorities.

With these steps in place, you're well-prepared to conduct a comprehensive comparative analysis. The next phase involves interpreting your findings, drawing conclusions, and making informed decisions based on the insights you've gained.

Comparative Analysis Interpretation

Interpreting the results of your comparative analysis is a crucial phase that transforms data into actionable insights. We'll delve into various aspects of interpretation and how to make sense of your findings.

• Contextual Understanding: Before diving into the data, consider the broader context of your analysis. Understand the industry trends, market conditions, and any external factors that may have influenced your results.
• Drawing Conclusions: Summarize your findings clearly and concisely. Identify trends, patterns, and significant differences among the options or variables you've compared.
• Quantitative vs. Qualitative Analysis: Depending on the nature of your data and analysis, you may need to balance both quantitative and qualitative interpretations. Qualitative insights can provide context and nuance to quantitative findings.
• Comparative Visualization: Visual aids such as charts, graphs, and tables can help convey your conclusions effectively. Choose visual representations that align with the nature of your data and the key points you want to emphasize.
• Outliers and Anomalies: Identify and explain any outliers or anomalies in your data. Understanding these exceptions can provide valuable insights into unusual cases or factors affecting your analysis.
• Cross-Validation: Validate your conclusions by comparing them with external benchmarks, industry standards, or expert opinions. Cross-validation helps ensure the reliability of your findings.
• Implications for Decision-Making: Discuss how your analysis informs decision-making. Clearly articulate the practical implications of your findings and their relevance to your initial objectives.
• Actionable Insights: Emphasize actionable insights that can guide future strategies, policies, or actions. Make recommendations based on your analysis, highlighting the steps needed to capitalize on strengths or address weaknesses.
• Continuous Improvement: Encourage a culture of continuous improvement by using your analysis as a feedback mechanism. Suggest ways to monitor and adapt strategies over time based on evolving circumstances.

Comparative Analysis Applications

Comparative analysis is a versatile methodology that finds application in various fields and scenarios. Let's explore some of the most common and impactful applications.

Business Decision-Making

Comparative analysis is widely employed in business to inform strategic decisions and drive success. Key applications include:

Market Research and Competitive Analysis

• Objective: To assess market opportunities and evaluate competitors.
• Methods: Analyzing market trends, customer preferences, competitor strengths and weaknesses, and market share.
• Outcome: Informed product development, pricing strategies, and market entry decisions.

Product Comparison and Benchmarking

• Objective: To compare the performance and features of products or services.
• Methods: Evaluating product specifications, customer reviews, and pricing.
• Outcome: Identifying strengths and weaknesses, improving product quality, and setting competitive pricing.

Financial Analysis

• Objective: To evaluate financial performance and make investment decisions.
• Methods: Comparing financial statements, ratios, and performance indicators of companies.
• Outcome: Informed investment choices, risk assessment, and portfolio management.

Healthcare and Medical Research

In the healthcare and medical research fields, comparative analysis is instrumental in understanding diseases, treatment options, and healthcare systems.

Clinical Trials and Drug Development

• Objective: To compare the effectiveness of different treatments or drugs.
• Methods: Analyzing clinical trial data, patient outcomes, and side effects.
• Outcome: Informed decisions about drug approvals, treatment protocols, and patient care.

Health Outcomes Research

• Objective: To assess the impact of healthcare interventions.
• Methods: Comparing patient health outcomes before and after treatment or between different treatment approaches.
• Outcome: Improved healthcare guidelines, cost-effectiveness analysis, and patient care plans.

Healthcare Systems Evaluation

• Objective: To assess the performance of healthcare systems.
• Methods: Comparing healthcare delivery models, patient satisfaction, and healthcare costs.
• Outcome: Informed healthcare policy decisions, resource allocation, and system improvements.

Social Sciences and Policy Analysis

Comparative analysis is a fundamental tool in social sciences and policy analysis, aiding in understanding complex societal issues.

Educational Research

• Objective: To compare educational systems and practices.
• Methods: Analyzing student performance, curriculum effectiveness, and teaching methods.
• Outcome: Informed educational policies, curriculum development, and school improvement strategies.

Political Science

• Objective: To study political systems, elections, and governance.
• Methods: Comparing election outcomes, policy impacts, and government structures.
• Outcome: Insights into political behavior, policy effectiveness, and governance reforms.

Social Welfare and Poverty Analysis

• Objective: To evaluate the impact of social programs and policies.
• Methods: Comparing the well-being of individuals or communities with and without access to social assistance.
• Outcome: Informed policymaking, poverty reduction strategies, and social program improvements.

Environmental Science and Sustainability

Comparative analysis plays a pivotal role in understanding environmental issues and promoting sustainability.

Environmental Impact Assessment

• Objective: To assess the environmental consequences of projects or policies.
• Methods: Comparing ecological data, resource use, and pollution levels.
• Outcome: Informed environmental mitigation strategies, sustainable development plans, and regulatory decisions.

Climate Change Analysis

• Objective: To study climate patterns and their impacts.
• Methods: Comparing historical climate data, temperature trends, and greenhouse gas emissions.
• Outcome: Insights into climate change causes, adaptation strategies, and policy recommendations.

Ecosystem Health Assessment

• Objective: To evaluate the health and resilience of ecosystems.
• Methods: Comparing biodiversity, habitat conditions, and ecosystem services.
• Outcome: Conservation efforts, restoration plans, and ecological sustainability measures.

Technology and Innovation

Comparative analysis is crucial in the fast-paced world of technology and innovation.

Product Development and Innovation

• Objective: To assess the competitiveness and innovation potential of products or technologies.
• Methods: Comparing research and development investments, technology features, and market demand.
• Outcome: Informed innovation strategies, product roadmaps, and patent decisions.

User Experience and Usability Testing

• Objective: To evaluate the user-friendliness of software applications or digital products.
• Methods: Comparing user feedback, usability metrics, and user interface designs.
• Outcome: Improved user experiences, interface redesigns, and product enhancements.

Technology Adoption and Market Entry

• Objective: To analyze market readiness and risks for new technologies.
• Methods: Comparing market conditions, regulatory landscapes, and potential barriers.
• Outcome: Informed market entry strategies, risk assessments, and investment decisions.

These diverse applications of comparative analysis highlight its flexibility and importance in decision-making across various domains. Whether in business, healthcare, social sciences, environmental studies, or technology, comparative analysis empowers researchers and decision-makers to make informed choices and drive positive outcomes.

Comparative Analysis Best Practices

Successful comparative analysis relies on following best practices and avoiding common pitfalls. Implementing these practices enhances the effectiveness and reliability of your analysis.

• Clearly Defined Objectives: Start with well-defined objectives that outline what you aim to achieve through the analysis. Clear objectives provide focus and direction.
• Data Quality Assurance: Ensure data quality by validating, cleaning, and normalizing your data. Poor-quality data can lead to inaccurate conclusions.
• Transparent Methodologies: Clearly explain the methodologies and techniques you've used for analysis. Transparency builds trust and allows others to assess the validity of your approach.
• Consistent Criteria: Maintain consistency in your criteria and metrics across all options or variables. Inconsistent criteria can lead to biased results.
• Sensitivity Analysis: Conduct sensitivity analysis by varying key parameters, such as weights or assumptions, to assess the robustness of your conclusions.
• Stakeholder Involvement: Involve relevant stakeholders throughout the analysis process. Their input can provide valuable perspectives and ensure alignment with organizational goals.
• Critical Evaluation of Assumptions: Identify and critically evaluate any assumptions made during the analysis. Assumptions should be explicit and justifiable.
• Holistic View: Take a holistic view of the analysis by considering both short-term and long-term implications. Avoid focusing solely on immediate outcomes.
• Documentation: Maintain thorough documentation of your analysis, including data sources, calculations, and decision criteria. Documentation supports transparency and facilitates reproducibility.
• Continuous Learning: Stay updated with the latest analytical techniques, tools, and industry trends. Continuous learning helps you adapt your analysis to changing circumstances.
• Peer Review: Seek peer review or expert feedback on your analysis. External perspectives can identify blind spots and enhance the quality of your work.
• Ethical Considerations: Address ethical considerations, such as privacy and data protection, especially when dealing with sensitive or personal data.

By adhering to these best practices, you'll not only improve the rigor of your comparative analysis but also ensure that your findings are reliable, actionable, and aligned with your objectives.

Comparative Analysis Examples

To illustrate the practical application and benefits of comparative analysis, let's explore several real-world examples across different domains. These examples showcase how organizations and researchers leverage comparative analysis to make informed decisions, solve complex problems, and drive improvements:

Retail Industry - Price Competitiveness Analysis

Objective: A retail chain aims to assess its price competitiveness against competitors in the same market.

Methodology:

• Collect pricing data for a range of products offered by the retail chain and its competitors.
• Organize the data into a comparative framework, categorizing products by type and price range.
• Calculate price differentials, averages, and percentiles for each product category.
• Analyze the findings to identify areas where the retail chain's prices are higher or lower than competitors.

Outcome: The analysis reveals that the retail chain's prices are consistently lower in certain product categories but higher in others. This insight informs pricing strategies, allowing the retailer to adjust prices to remain competitive in the market.

Healthcare - Comparative Effectiveness Research

Objective: Researchers aim to compare the effectiveness of two different treatment methods for a specific medical condition.

• Recruit patients with the medical condition and randomly assign them to two treatment groups.
• Collect data on treatment outcomes, including symptom relief, side effects, and recovery times.
• Analyze the data using statistical methods to compare the treatment groups.
• Consider factors like patient demographics and baseline health status as potential confounding variables.

Outcome: The comparative analysis reveals that one treatment method is statistically more effective than the other in relieving symptoms and has fewer side effects. This information guides medical professionals in recommending the more effective treatment to patients.

Environmental Science - Carbon Emission Analysis

Objective: An environmental organization seeks to compare carbon emissions from various transportation modes in a metropolitan area.

• Collect data on the number of vehicles, their types (e.g., cars, buses, bicycles), and fuel consumption for each mode of transportation.
• Calculate the total carbon emissions for each mode based on fuel consumption and emission factors.
• Create visualizations such as bar charts and pie charts to represent the emissions from each transportation mode.
• Consider factors like travel distance, occupancy rates, and the availability of alternative fuels.

Outcome: The comparative analysis reveals that public transportation generates significantly lower carbon emissions per passenger mile compared to individual car travel. This information supports advocacy for increased public transit usage to reduce carbon footprint.

Technology Industry - Feature Comparison for Software Development Tools

Objective: A software development team needs to choose the most suitable development tool for an upcoming project.

• Create a list of essential features and capabilities required for the project.
• Research and compile information on available development tools in the market.
• Develop a comparative matrix or scoring system to evaluate each tool's features against the project requirements.
• Assign weights to features based on their importance to the project.

Outcome: The comparative analysis highlights that Tool A excels in essential features critical to the project, such as version control integration and debugging capabilities. The development team selects Tool A as the preferred choice for the project.

Educational Research - Comparative Study of Teaching Methods

Objective: A school district aims to improve student performance by comparing the effectiveness of traditional classroom teaching with online learning.

• Randomly assign students to two groups: one taught using traditional methods and the other through online courses.
• Administer pre- and post-course assessments to measure knowledge gain.
• Collect feedback from students and teachers on the learning experiences.
• Analyze assessment scores and feedback to compare the effectiveness and satisfaction levels of both teaching methods.

Outcome: The comparative analysis reveals that online learning leads to similar knowledge gains as traditional classroom teaching. However, students report higher satisfaction and flexibility with the online approach. The school district considers incorporating online elements into its curriculum.

These examples illustrate the diverse applications of comparative analysis across industries and research domains. Whether optimizing pricing strategies in retail, evaluating treatment effectiveness in healthcare, assessing environmental impacts, choosing the right software tool, or improving educational methods, comparative analysis empowers decision-makers with valuable insights for informed choices and positive outcomes.

Conclusion for Comparative Analysis

Comparative analysis is your compass in the world of decision-making. It helps you see the bigger picture, spot opportunities, and navigate challenges. By defining your objectives, gathering data, applying methodologies, and following best practices, you can harness the power of Comparative Analysis to make informed choices and drive positive outcomes.

Remember, Comparative analysis is not just a tool; it's a mindset that empowers you to transform data into insights and uncertainty into clarity. So, whether you're steering a business, conducting research, or facing life's choices, embrace Comparative Analysis as your trusted guide on the journey to better decisions. With it, you can chart your course, make impactful choices, and set sail toward success.

How to Conduct Comparative Analysis in Minutes?

Are you ready to revolutionize your approach to market research and comparative analysis? Appinio , a real-time market research platform, empowers you to harness the power of real-time consumer insights for swift, data-driven decisions. Here's why you should choose Appinio:

• Speedy Insights:  Get from questions to insights in minutes, enabling you to conduct comparative analysis without delay.
• User-Friendly:  No need for a PhD in research – our intuitive platform is designed for everyone, making it easy to collect and analyze data.
• Global Reach:  With access to over 90 countries and the ability to define your target group from 1200+ characteristics, Appinio provides a worldwide perspective for your comparative analysis

Get free access to the platform!

Join the loop 💌

Be the first to hear about new updates, product news, and data insights. We'll send it all straight to your inbox.

Get the latest market research news straight to your inbox! 💌

Wait, there's more

07.05.2024 | 29min read

Interval Scale: Definition, Characteristics, Examples

03.05.2024 | 29min read

What is Qualitative Observation? Definition, Types, Examples

02.05.2024 | 32min read

What is a Perceptual Map and How to Make One? (+ Template)

• AI Content Shield
• AI KW Research
• AI Assistant
• SEO Optimizer
• AI KW Clustering
• Customer reviews
• The NLO Revolution
• Press Center
• Help Center
• Content Resources
• Facebook Group

Writing a Comparative Case Study: Effective Guide

Table of Contents

As a researcher or student, you may be required to write a comparative case study at some point in your academic journey. A comparative study is an analysis of two or more cases. Where the aim is to compare and contrast them based on specific criteria. We created this guide to help you understand how to write a comparative case study . This article will discuss what a comparative study is, the elements of a comparative study, and how to write an effective one. We also include samples to help you get started.

What is a Comparative Case Study?

A comparative study is a research method that involves comparing two or more cases to analyze their similarities and differences . These cases can be individuals, organizations, events, or any other unit of analysis. A comparative study aims to gain a deeper understanding of the subject matter by exploring the differences and similarities between the cases.

Elements of a Comparative Study

Before diving into the writing process, it’s essential to understand the key elements that make up a comparative study. These elements include:

• Research Question : This is the central question the study seeks to answer. It should be specific and clear, and the basis of the comparison.
• Cases : The cases being compared should be chosen based on their significance to the research question. They should also be similar in some ways to facilitate comparison.
• Data Collection : Data collection should be comprehensive and systematic. Data collected can be qualitative, quantitative, or both.
• Analysis : The analysis should be based on the research question and collected data. The data should be analyzed for similarities and differences between the cases.
• Conclusion : The conclusion should summarize the findings and answer the research question. It should also provide recommendations for future research.

How to Write a Comparative Study

Now that we have established the elements of a comparative study, let’s dive into the writing process. Here is a detailed approach on how to write a comparative study:

Choose a Topic

The first step in writing a comparative study is to choose a topic relevant to your field of study. It should be a topic that you are familiar with and interested in.

Define the Research Question

Once you have chosen a topic, define your research question. The research question should be specific and clear.

Choose Cases

The next step is to choose the cases you will compare. The cases should be relevant to your research question and have similarities to facilitate comparison.

Collect Data

Collect data on each case using qualitative, quantitative, or both methods. The data collected should be comprehensive and systematic.

Analyze Data

Analyze the data collected for each case. Look for similarities and differences between the cases. The analysis should be based on the research question.

Write the Introduction

The introduction should provide background information on the topic and state the research question.

Write the Literature Review

The literature review should give a summary of the research that has been conducted on the topic.

Write the Methodology

The methodology should describe the data collection and analysis methods used.

Present Findings

Present the findings of the analysis. The results should be organized based on the research question.

Conclusion and Recommendations

Summarize the findings and answer the research question. Provide recommendations for future research.

Sample of Comparative Case Study

To provide a better understanding of how to write a comparative study , here is an example: Comparative Study of Two Leading Airlines: ABC and XYZ

Introduction

The airline industry is highly competitive, with companies constantly seeking new ways to improve customer experiences and increase profits. ABC and XYZ are two of the world’s leading airlines, each with a distinct approach to business. This comparative case study will examine the similarities and differences between the two airlines. And provide insights into what works well in the airline industry.

Research Questions

What are the similarities and differences between ABC and XYZ regarding their approach to business, customer experience, and profitability?

Data Collection and Analysis

To collect data for this comparative study, we will use a combination of primary and secondary sources. Primary sources will include interviews with customers and employees of both airlines, while secondary sources will include financial reports, marketing materials, and industry research. After collecting the data, we will use a systematic and comprehensive approach to data analysis. We will use a framework to compare and contrast the data, looking for similarities and differences between the two airlines. We will then organize the data into categories: customer experience, revenue streams, and operational efficiency.

After analyzing the data, we found several similarities and differences between ABC and XYZ. Similarities Both airlines offer a high level of customer service, with attentive flight attendants, comfortable seating, and in-flight entertainment. They also strongly focus on safety, with rigorous training and maintenance protocols in place. Differences ABC has a reputation for luxury, with features such as private suites and shower spas in first class. On the other hand, XYZ has a reputation for reliability and efficiency, with a strong emphasis on on-time departures and arrivals. In terms of revenue streams, ABC derives a significant portion of its revenue from international travel. At the same time, XYZ has a more diverse revenue stream, focusing on domestic and international travel. ABC also has a more centralized management structure, with decision-making authority concentrated at the top. On the other hand, XYZ has a more decentralized management structure, with decision-making authority distributed throughout the organization.

This comparative case study provides valuable insights into the airline industry and the approaches taken by two leading airlines, ABC and Delta. By comparing and contrasting the two airlines, we can see the strengths and weaknesses of each method. And identify potential strategies for improving the airline industry as a whole. Ultimately, this study shows that there is no one-size-fits-all approach to doing business in the airline industry. And that success depends on a combination of factors, including customer experience, operational efficiency, and revenue streams.

Wrapping Up

A comparative study is an effective research method for analyzing case similarities and differences. Writing a comparative study can be daunting, but proper planning and organization can be an effective research method. Define your research question, choose relevant cases, collect and analyze comprehensive data, and present the findings. The steps detailed in this blog post will help you create a compelling comparative study that provides valuable insights into your research topic . Remember to stay focused on your research question. And use the data collected to provide a clear and concise analysis of the cases being compared.

Abir Ghenaiet

Abir is a data analyst and researcher. Among her interests are artificial intelligence, machine learning, and natural language processing. As a humanitarian and educator, she actively supports women in tech and promotes diversity.

Explore All Write A Case Study Articles

How to write a leadership case study (sample) .

Writing a case study isn’t as straightforward as writing essays. But it has proven to be an effective way of…

• Write A Case Study

Top 5 Online Expert Case Study Writing Services 

It’s a few hours to your deadline — and your case study college assignment is still a mystery to you.…

Examples Of Business Case Study In Research

A business case study can prevent an imminent mistake in business. How? It’s an effective teaching technique that teaches students…

How to Write a Multiple Case Study Effectively

Have you ever been assigned to write a multiple case study but don’t know where to begin? Are you intimidated…

How to Write a Case Study Presentation: 6 Key Steps

Case studies are an essential element of the business world. Understanding how to write a case study presentation will give…

How to Write a Case Study for Your Portfolio

Are you ready to showcase your design skills and move your career to the next level? Crafting a compelling case…

This website may not work correctly because your browser is out of date. Please update your browser .

• Comparative case studies
• Comparative case studies File type PDF File size 510.74 KB

This guide, written by Delwyn Goodrick for UNICEF, focuses on the use of comparative case studies in impact evaluation.

The paper gives a brief discussion of their use and then outlines when it is appropriate to use them. It then provides step by step guidance on their use for an impact evaluation.

"A case study is an in-depth examination, often undertaken over time, of a single case – such as a policy, programme, intervention site, implementation process or participant. Comparative case studies cover two or more cases in a way that produces more generalizable knowledge about causal questions – how and why particular programmes or policies work or fail to work.

Comparative case studies are undertaken over time and emphasize comparison within and across contexts. Comparative case studies may be selected when it is not feasible to undertake an experimental design and/or when there is a need to understand and explain how features within the context influence the success of programme or policy initiatives. This information is valuable in tailoring interventions to support the achievement of intended outcomes."

• Comparative case studies: a brief description
• When is it appropriate to use this method?
• How to conduct comparative case studies
• Ethical issues and practical limitations
• Which other methods work well with this one?
• Presentation of results and analysis
• Example of good practices
• Examples of challenges

Goodrick, D., (2014), Comparative Case Studies, UNICEF. Retrieved from: http://devinfolive.info/impact_evaluation/img/downloads/Comparative_Case_Studies_ENG.pdf

What does a non-experimental evaluation look like? How can we evaluate interventions implemented across multiple contexts, where constructing a control group is not feasible?

This is part of a series

• UNICEF Impact Evaluation series
• Overview of impact evaluation
• Overview: Strategies for causal attribution
• Overview: Data collection and analysis methods in impact evaluation
• Theory of change
• Evaluative criteria
• Evaluative reasoning
• Participatory approaches
• Randomized controlled trials (RCTs)
• Randomized controlled trials (RCTs) video guide
• Quasi-experimental design and methods
• Developing and selecting measures of child well-being
• Interviewing
• UNICEF webinar: Overview of impact evaluation
• UNICEF webinar: Overview of data collection and analysis methods in Impact Evaluation
• UNICEF webinar: Theory of change
• UNICEF webinar: Overview: strategies for causal inference
• UNICEF webinar: Participatory approaches in impact evaluation
• UNICEF webinar: Randomized controlled trials
• UNICEF webinar: Comparative case studies
• UNICEF webinar: Quasi-experimental design and methods

'Comparative case studies ' is referenced in:

• Developing a research agenda for impact evaluation
• Impact evaluation

Back to top

© 2022 BetterEvaluation. All right reserved.

Help | Advanced Search

Computer Science > Software Engineering

Title: a comparative case study on the impact of test-driven development on program design and test coverage.

Abstract: Test-driven development (TDD) is a programming technique in which the tests are written prior to the source code. It is proposed that TDD is one of the most fundamental practices enabling the development of software in an agile and iterative manner. Both the literature and practice suggest that TDD practice yields several benefits. Essentially, it is claimed that TDD leads to an improved software design, which has a dramatic impact on the maintainability and further development of the system. The impact of TDD on program design has seldom come under the researchers' focus. This paper reports the results from a comparative case study of three software development projects where the effect of TDD on program design was measured using object-oriented metrics. The results show that the effect of TDD on program design was not as evident as expected, but the test coverage was significantly superior to iterative test-last development.

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

DBLP - CS Bibliography

Bibtex formatted citation.

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

• Help & FAQ

Comparative case studies of open source software peer review practices

• College of Information Sciences and Technology

Research output : Contribution to journal › Article › peer-review

Context The power of open source software peer review lies in the involvement of virtual communities, especially users who typically do not have a formal role in the development process. As communities grow to a certain extent, how to organize and support the peer review process becomes increasingly challenging. A universal solution is likely to fail for communities with varying characteristics. Objective This paper investigates differences of peer review practices across different open source software communities, especially the ones engage distinct types of users, in order to offer contextualized guidance for developing open source software projects. Method Comparative case studies were conducted in two well-established large open source communities, Mozilla and Python, which engage extremely different types of users. Bug reports from their bug tracking systems were examined primarily, complemented by secondary sources such as meeting notes, blog posts, messages from mailing lists, and online documentations. Results The two communities differ in the key activities of peer review processes, including different characteristics with respect to bug reporting, design decision making, to patch development and review. Their variances also involve the designs of supporting technology. The results highlight the emerging role of triagers, who bridge the core and peripheral contributors and facilitate the peer review process. The two communities demonstrate alternative designs of open source software peer review and their tradeoffs were discussed. Conclusion It is concluded that contextualized designs of social and technological solutions to open source software peer review practices are important. The two cases can serve as learning resources for open source software projects, or other types of large software projects in general, to cope with challenges of leveraging enormous contributions and coordinating core developers. It is also important to improve support for triagers, who have not received much research effort yet.

All Science Journal Classification (ASJC) codes

• Information Systems
• Computer Science Applications

Access to Document

• 10.1016/j.infsof.2015.06.002

Other files and links

• Link to publication in Scopus
• Link to the citations in Scopus

Fingerprint

• Open source software Engineering & Materials Science 100%
• Blogs Engineering & Materials Science 13%
• Decision making Engineering & Materials Science 8%

T1 - Comparative case studies of open source software peer review practices

AU - Wang, Jing

AU - Shih, Patrick C.

AU - Wu, Yu

AU - Carroll, John M.

N1 - Funding Information: This work is supported by the US NSF ( 0943023 ). We thank our partners, the Mozilla and Python organizations for sharing their practices, and also all the reviewers of this article for their suggestions that helped improve it. Publisher Copyright: © 2015 Elsevier B.V.

PY - 2015/11/1

Y1 - 2015/11/1

N2 - Context The power of open source software peer review lies in the involvement of virtual communities, especially users who typically do not have a formal role in the development process. As communities grow to a certain extent, how to organize and support the peer review process becomes increasingly challenging. A universal solution is likely to fail for communities with varying characteristics. Objective This paper investigates differences of peer review practices across different open source software communities, especially the ones engage distinct types of users, in order to offer contextualized guidance for developing open source software projects. Method Comparative case studies were conducted in two well-established large open source communities, Mozilla and Python, which engage extremely different types of users. Bug reports from their bug tracking systems were examined primarily, complemented by secondary sources such as meeting notes, blog posts, messages from mailing lists, and online documentations. Results The two communities differ in the key activities of peer review processes, including different characteristics with respect to bug reporting, design decision making, to patch development and review. Their variances also involve the designs of supporting technology. The results highlight the emerging role of triagers, who bridge the core and peripheral contributors and facilitate the peer review process. The two communities demonstrate alternative designs of open source software peer review and their tradeoffs were discussed. Conclusion It is concluded that contextualized designs of social and technological solutions to open source software peer review practices are important. The two cases can serve as learning resources for open source software projects, or other types of large software projects in general, to cope with challenges of leveraging enormous contributions and coordinating core developers. It is also important to improve support for triagers, who have not received much research effort yet.

AB - Context The power of open source software peer review lies in the involvement of virtual communities, especially users who typically do not have a formal role in the development process. As communities grow to a certain extent, how to organize and support the peer review process becomes increasingly challenging. A universal solution is likely to fail for communities with varying characteristics. Objective This paper investigates differences of peer review practices across different open source software communities, especially the ones engage distinct types of users, in order to offer contextualized guidance for developing open source software projects. Method Comparative case studies were conducted in two well-established large open source communities, Mozilla and Python, which engage extremely different types of users. Bug reports from their bug tracking systems were examined primarily, complemented by secondary sources such as meeting notes, blog posts, messages from mailing lists, and online documentations. Results The two communities differ in the key activities of peer review processes, including different characteristics with respect to bug reporting, design decision making, to patch development and review. Their variances also involve the designs of supporting technology. The results highlight the emerging role of triagers, who bridge the core and peripheral contributors and facilitate the peer review process. The two communities demonstrate alternative designs of open source software peer review and their tradeoffs were discussed. Conclusion It is concluded that contextualized designs of social and technological solutions to open source software peer review practices are important. The two cases can serve as learning resources for open source software projects, or other types of large software projects in general, to cope with challenges of leveraging enormous contributions and coordinating core developers. It is also important to improve support for triagers, who have not received much research effort yet.

UR - http://www.scopus.com/inward/record.url?scp=84942011818&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84942011818&partnerID=8YFLogxK

U2 - 10.1016/j.infsof.2015.06.002

DO - 10.1016/j.infsof.2015.06.002

M3 - Article

AN - SCOPUS:84942011818

SN - 0950-5849

JO - Information and Software Technology

JF - Information and Software Technology

Synth: An R Package for Synthetic Control Methods in Comparative Case Studies

Main article content, article details, article sidebar, article meta.

Scaling agile development in mechatronic organizations - a comparative case study

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

A Comparative Case Study on Tools for Internal Software Quality Measures

Internal software quality is measured using quality metrics, which are implemented in static software analysis tools. There is no current research on which tool is the best suited to improve internal software quality, i.e. implements scientifically validated metrics, has sufficient features and consistent measurement results. The approach to solve this problem was to find academic papers that have validated software metrics and then find tools that support these metrics, additionally these tools were evaluated for consistency of results and other user relevant characteristics. An evaluation of the criteria above resulted in a recommendation for the Java/C/C++ tool Understand and the C/C++ tool QAC.

Related Papers

Dimitris Stavrinoudis

International Journal of Engineering Research and Technology (IJERT)

IJERT Journal

https://www.ijert.org/taxonomy-of-metrics-for-assessing-software-quality https://www.ijert.org/research/taxonomy-of-metrics-for-assessing-software-quality-IJERTV1IS6139.pdf As object oriented paradigm is gaining popularity, software metrics play an important role in ensuring software quality. In this paper we first introduce the theoretical concept of object oriented metrics, specifically of CK metrics suite. Then a case study of analyzing Java based open source software using CK metrics to evaluate quality is presented. The results are interpreted to help the software developers and researchers in improving the quality of the software during the development of the software.

International Journal on Computational Science & Applications

ermiyas birhanu

Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis's software metrics used to measure the software product and process. In this Study, the researcher used a collection of literatures from various electronic databases, available since 2008 to understand and know the software metrics. Finally, in this study, the researcher has been identified software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from others for this reason it is better to apply the software metrics to measure the quality of software and the current most common software metrics tools to reduce the subjectivity of faults during the assessment of software quality. The central contribution of this study is an overview about software metrics that can illustrate us the development in this area, and a critical analysis about the main metrics founded on the various literatures.

International Journal

Arpita Mittal

Fabrice Bellingard

Jannik Laval , Hani Abdeen

Proceedings of the 2008 international symposium on Software testing and analysis - ISSTA '08

Rüdiger Lincke

Gordana Rakic

Thanwadee Sunetnanta

RELATED PAPERS

Claudio Sapelli

Revista Publicaciones

Revista Ensaios de Geografia

Ginno Perez

Chemical Engineering Science

M. van Sint Annaland

Anders Bonde

Noémie LAGO

Plant Foods for Human Nutrition ( …

Joanna Klepacka

Yuka Kitano

Fredy González

Reports on …

edelcio souza

Logistics Thought

نشریه علمی اندیشه آماد

Parasitology Research

Hiroshi Tachibana

Physics of Wave Processes and Radio Systems

Nikolay A Arkhipov

Politická ekonomie

INTEGER: Journal of Information Technology

Tutuk Indriyani

Journal of Bodywork and Movement Therapies

Journal of Creative Student Research

mhd akbar perdana

Transportation Research Part C: Emerging Technologies

Shenhao Wang

Journal of Physics: Conference Series

Miguel Piedrahita

Global Regional Review

Dr. Fareeha Javed

Applied biochemistry and biotechnology

Jornal Brasileiro de Pneumologia

diana penha

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

To read this content please select one of the options below:

Please note you do not have access to teaching notes, marketing for survival: a comparative case study of sme software firms.

Journal of Small Business and Enterprise Development

ISSN : 1462-6004

Article publication date: 26 October 2012

This study seeks to explore the success and failure of two similar small software technology firms from a marketing perspective. Using a dyadic approach, the research aims to compare the degree of customer orientation and innovativeness exhibited in both firms and to understand contributing factors for success and failure.

Design/methodology/approach

A two‐case comparative case study was employed as the primary method of investigation. Participant‐observation in both firms and 22 semi‐structured interviews with owner‐managers, employees and customers provided a holistic approach to how these firms perceived and prioritised marketing and innovation.

There is a need for small software firms to strike a balance between customer orientation and innovativeness in order to survive. In terms of customer orientation, the findings show that it is not only related to customer contacts and relationships, but is also about delivering on the promise. The small firm's ability to achieve this is highly dependent on managerial style, communication between the firms and their customers, business planning, market research, promotion and firm culture.

Practical implications

The benefits of this study, which demonstrates the stark contrast between successful and unsuccessful behaviour, can act as a useful guide for small to medium‐sized enterprise (SME) managers who often have technical but less managerial competencies.

Originality/value

This is a unique study comparing two software SMEs, particularly one which failed and one which succeeded under similar conditions, thus illustrating good practice by contrasting with bad practice. It also contributes to the literature on how SMEs conduct marketing in the software industry and how to secure small firm sustainability and growth in developing regions.

• Small to medium‐sized enterprises
• Software industry
• Case study research
• Success and failure
• Critical success factors
• Business performance
• Business failures

Parry, S. , Jones, R. , Rowley, J. and Kupiec‐Teahan, B. (2012), "Marketing for survival: a comparative case study of SME software firms", Journal of Small Business and Enterprise Development , Vol. 19 No. 4, pp. 712-728. https://doi.org/10.1108/14626001211277488

Emerald Group Publishing Limited

Copyright © 2012, Emerald Group Publishing Limited

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 13 May 2024

Urban traffic-parking system dynamics model with macroscopic properties: a comparative study between Shanghai and Zurich

• Biruk Gebremedhin Mesfin 1 , 2 ,
• Zihao Li 3 ,
• Daniel (Jian) Sun   ORCID: orcid.org/0000-0002-4331-6502 3 , 4 ,
• Deming Chen 5 &
• Yueting Xi 6  

Humanities and Social Sciences Communications volume  11 , Article number:  616 ( 2024 ) Cite this article

Metrics details

• Development studies
• Science, technology and society
• Social policy

Analyzing the dynamics of parking traffic can better represent the real dynamic states of road networks, thereby allowing for a deeper analysis of the parking system’s impact. This paper comparatively investigates the impact of parking policies on two traffic networks with different infrastructure, socio-economic, and policy characteristics. Parking space, average parking duration, and parking fee policies were analyzed as a function of cruising distances and cruising time with indirect effects on traffic emissions. Empirically, the system dynamics model application is tested and validated with the macroscopic data from two central business districts (CBDs) in Shanghai (Xujiahui area) and Zurich (Bahnhofstrasse area). Results showed Bahnhofstrasse CBD is more sensitive against the policy shifts with relatively higher elasticity and indicated greater responsiveness in aggregating traffic emissions when compared with Xujiahui CBD. The findings of this study may provide an overall framework to empirically assess the performance of different traffic conditions and strategies on urban parking systems.

Introduction

Urban parking has been acknowledged for a considerable period as an essential aspect of urban transportation systems. The effectiveness of parking systems largely impacts the dynamics of traffic flow within a network, as demonstrated by Shoup ( 1997 ), who underscored the significance of parking in urban traffic congestion. More importantly, the act of searching for parking spaces significantly aggravates on-road congestion, with approximately 30% of road demand in specific central business districts (CBDs) being due to vehicles seeking for parking (Shoup, 1997 ). The rapid growth in vehicle ownership contributes to the negative effects of parking systems, as noted by Wen et al. ( 2019 ) and Dong et al. ( 2021 ). These negative effects include travel delays caused by the time spent cruising for parking, increased costs for drivers, and local environmental impacts due to vehicle emissions (Cao & Menendez, 2015 ; Cao et al., 2019 ; Jakob et al., 2020 ; Jakob, 2021 ). Issues such as insufficient allocation of parking space, impractical fees, and unrealistic time limits often arise from a lack of understanding about how adjacent systems interact, improper land use plans, or inaccurate assessments of parking demand, which further complicates the situation (Shoup, 1997 ; Chen et al., 2016 ; Sun et al., 2016 ; Shen et al., 2020 ).

Some analyses have been conducted on the interaction between parking systems and urban transportation from various viewpoints. Assessments at the city level have primarily concentrated on socio-economic aspects, including income and vehicle possession, as well as their association with the need for parking (Shoup, 1997 ; Jakob, 2021 ; Ni and Sun, 2017 ; Zhang et al., 2019 ). These investigations provide a valuable understanding of the wider consequences of socio-economic patterns on the dynamics of urban parking. On the other hand, at the network level, scholars have explored the operational structure of parking systems. Pioneering research in this area, utilizing empirical-microscopic methods (Axhausen and Polak, 1991 ; Kladeftiras and Antoniou, 2013 ; Pierce and Shoup, 2013 ; Shoup, 2006 ; Weinberger et al., 2012 ), have played a crucial role in comprehending the complexities of parking behavior. Traffic assignment models (Boyles et al., 2015 ; Gallo et al., 2011 ; Gu et al., 2021 ; Levy et al., 2013 ; Qian et al., 2012 ; Kang et al., 2022 ) and agent-based simulations (Balac et al., 2017 ; Benenson et al., 2008 ; Horni et al., 2013 ; Marki et al., 2014 ; Ni and Sun, 2018 ; Ni et al., 2024 ; Sun et al., 2016 ; Waraich and Axhausen, 2012 ; Zhao et al., 2018 ) have further enriched this field by offering nuanced perspectives on parking operations.

Furthermore, the economic aspects of parking have been extensively investigated, such as metrics for measuring effectiveness and cost functions, including the studies conducted by Arnott and Rowse ( 1999 ), Arnott and Inci ( 2010 ), Cavadas and Antunes ( 2019 ), as well as Xiao et al. ( 2019 ). Nevertheless, these research efforts often assume static speed and time conditions and require detailed data, resulting in discrete outcomes that do not fully capture real-life situations and require significant resources. Consequently, there remains a knowledge gap regarding the dynamic and practical aspects of searching for available parking spaces.

The adoption of aggregate macroscopic fundamental diagrams (MFD) and collective network traffic analysis offers a comprehensive and cost-efficient solution to address this gap. Seminal works by Daganzo and Geroliminis ( 2008 ), Gu et al. ( 2020 ), Geroliminis and Daganzo ( 2008 ), Huang et al. ( 2021 , 2022a , 2022b ), Loder et al. ( 2017 ), Zhao et al. ( 2021 ), and Zhao et al. ( 2022 ) have formulated and tested this paradigm shift, demonstrating its potential in effectively representing traffic system dynamics with fewer data needs and analytical complications. Recently, Cao and Menendez ( 2015 ) and Cao et al. ( 2019 ) introduced a macroscopic model that incorporates MFD with dynamic phases, drawing inspiration from parking equilibrium concepts proposed by Anderson and De Palma ( 2004 ), Arnott et al. ( 1991 ), and Arnott et al. ( 1993 ). The unique aspect of this approach lies in the ability to analyze the interaction between parking and traffic, providing valuable insights for policy implications on both temporal and spatial scales. However, the application of this model in a small network located in downtown Zurich raises concerns about its applicability to other urban settings with different parking and traffic system characteristics. Hence, further studies are necessary to evaluate the universality of this model by testing it in diverse urban contexts.

While there has been significant advancement in comprehending the relationship between urban parking and traffic systems, the field is still developing. The move towards macroscopic models represents noteworthy progress, which however still needs to be confirmed in different urban settings and to connect with practical applications to specific cities. This study conducted a comparative analysis to evaluate the performance of parking systems in two different networks: Xujiahui CBD, Shanghai, and Bahnhofstrasse CBD, Zurich. In contrast to previous research, the dynamic macroscopic evaluation model developed by Cao and Menendez ( 2015 ) was comparatively applied to the two particular urban contexts. The unique aspect of the proposed approach lies in the incorporation of more practical inputs into the model, making it more applicable and reliable. The model’s universality and effectiveness have been rigorously tested by extending its validation beyond the original scope.

In addition, this study contributes to the field by quantitatively evaluating the effects of policy changes on environmental factors. This is accomplished through the measurement of the aggregate cruising distance and hot emission factor, resulting in a nuanced comprehension of how policy modifications may impact environmental outcomes within urban areas (Mesfin et al., 2022 ; Sun and Ding, 2019 ). By focusing on both performance and environmental consequences, a comprehensive assessment of parking system efficiency was carried out, representing a substantial advancement in the methodology and application of parking system analysis. The findings not only validate the model developed by Cao and Menendez ( 2015 ) in new urban contexts but also expand the usefulness for policymakers and urban planners seeking to balance efficiency and environmental concerns in megacity parking management.

The remainder of this paper is organized as follows. Section “Methods” introduces the analytical methods and models that were chosen, while section “Case study” showcases the real-world application of the model through an empirical case study. In the section “Results and comparisons”, the parking and traffic performance indicators from two CBD cases were compared and analyzed, followed by an examination of how the parking systems respond to different policies. Finally, research findings conclusions, and future study directions are provided in section “Conclusions”.

The dynamic parking model with macroscopic traffic properties is favored due to two features. First, the model explores the traffic system macroscopically, rather than analyzing it microscopically, or using a multi-agent simulation model, which requires little data and relatively simple calculations. Second, the model performs under time-varying conditions, thus capturing the dynamic nature of parking systems (Cao and Menendez, 2015 ). A conceptual design of the model is presented below, followed by an analytical formulation of the design parameters.

Model definition

In this section, we review and define the basic elements of the system blocks, their dynamic relationships, as well as the necessary input parameters as follows.

The dynamic blocks and the transition matrix : The parking and traffic systems, together with the transition matrix, form the dynamic circle of the model. As shown in Fig. 1 , the parking system block is composed of parameters that describe the characteristics of parking. From the traffic system blocks, key properties of traffic flow are derived in accordance with the road system and the nature of vehicle movement. The transition matrix between the two blocks represents the dynamic relationship between the three parking-related states and the five transition events. By analyzing and quantifying the number of vehicles at each parking state and parking-related transition event using the following input parameters, the model estimates the number of vehicles at each parking state and parking-related transition event on each sliced time t:

Illustration for dynamic interactions within urban traffic-parking systems entities.

Basic information about the area : The radius of the network, the total length of the roads, the total length of the network, and the number of parking spaces in the area. Data inputs are based on a dense urban area with a relatively homogeneous network. Traffic and parking conditions change dynamically over multiple time slices over the course of the entire time horizon, which is divided into small time slices (1-min).

Initial conditions and traffic demand : The daily traffic inflows, the initial condition of the parking states, and the percentage of traffic demand associated with parking. As urban networks may be symbolized as one ring road with cars driving in a single direction, it has been found that this method is suitable for small, homogeneous traffic networks (Cao and Menendez, 2015 ). Travel demand and distance-driven variables can be estimated using historical data, e.g., traffic data entering and exiting the network, and distance variables based on the radius of the network.

Traffic properties of the area : The macroscopic fundamental diagram is used here to illustrate the traffic properties of the area. A network’s MFD is defined by its average free-flow travel speed, critical traffic density, maximum flow, and jam density. Due to the homogeneous nature of the network, parking searchers and parking garages are assumed to be evenly distributed within the driving traffic.

Traveling distance : Includes the length defined as the length required for the vehicle to shift from non-searching to searching state as lns/s , the length needed for the vehicle to leave the area from parking as lp/ , and the through vehicle travel length from inlet to outlet as l/ . Since the case study primarily involves small CBD areas with standard parking policies, we do not have to pay close attention to individual car locations and parking spaces throughout the dynamic process, i.e., only the average numbers of vehicles and total/average search times and distances are tracked during a time slice.

Parking duration : The distribution of the parking duration histogram (parking duration to frequency graph) and represented by an approximate probability distribution function with the average parking duration. This model utilizes an approximate distribution function based on the parameters of shape and scale to represent a distribution of desired parking durations. There are a variety of possible distributions that can be used, such as Gamma, Poisson, negative binomials, etc. It is assumed that drivers do not cancel their trips while searching for parking. Input parameters specific to parking duration and parking pricing can be approximated based on field measurements.

Analytical formulation for the number of vehicles

Parking-related traffic states.

The parking-related traffic states form the basis of the parking-state-based matrix, which is updated as a function of the number of vehicles passing through the various transition events over the course of the time slice. All transition events are modeled using a deterministic approach. Despite this, the model as a whole is not deterministic with respect to parking locations, travel times, and parking durations.

The model is not thoroughly stochastic either, as there are no random values involved in the computation of each transition event. Furthermore, since the model is based on probability functions, it does not require repeated runs to account for its stochasticity. All traffic states and transition parking events are summarized in Table 1 . Model input variables are the initial conditions of all traffic state variables, which are either measured, assumed, or simulated.

Based on information regarding the transition events involved in parking-related states, these parking-related states were determined. Equations ( 1 )–( 3 ) update the number of “non-searching”, “searching for parking”, and “parking” vehicles, respectively.

Let ( i ) represent the time slice of the dynamic model analysis. As presented in Table 1 , the number of vehicles at each state and transition event at time slice ( i ) was defined as follows: parked state, N i p ; searching state, N i s ; non-searching state, N i ns ; entering the area, n i /ns ; starting to search, n i ns/s ; accessing parking, n i s/p ; departing parking, n i p/ns ; and leaving the area , n i ns/ . Thus, the number of vehicles in the parking state at time ( i ) can be calculated based on the previous state at time ( i -1) and the neighboring states at time ( i ).

Equation ( 1 ) continually updates the number of “non-searching” vehicles at (i  +  1 ) as a function of vehicles entering the area (i.e., n i /ns ), vehicles that depart parking (i.e., n i p/ns ), vehicles that are on transition from non-searching to searching (i.e., n i ns/s ), vehicles previously in the non-searching state (i.e., N i ns ), and vehicles leaving the area (i.e., n i ns/ ).

Equation ( 2 ) continually updates the number of “searching” vehicles at ( i  + 1) as a function of vehicles on the transition from non-searching to searching (i.e., n i ns/s ), vehicles that are on the transition from searching to parking (i.e., n i s/p ), and vehicles previously in the searching state (i.e., N i s ).

Equation ( 3 ) continually updates the number of “parked” vehicles at ( i  + 1) as a function of vehicles on the transition from parking to non-searching (i.e., n i p/ns ), vehicles that are on the transition from searching to parking (i.e., n i s/p ), and vehicles previously in the parked state (i.e., N i p ).

Transition events

Based on the principles of probability and traffic flow theories, the number of vehicles in the transition events has been formulated. Assigning β% to indicate the percentage of vehicles that choose not to use the parking system; whereas (1–β%) are those expected to use the parking system. Figure 2 displays the proportion of vehicles that are engaged in each transition event and parking state.

Number of vehicles in each parking state and transition event as well as the proportion of through vehicles and vehicles involved in parking.

Based on the transition events discussed in Table 1 and the dynamic matrix above, a traffic demand or inflow of vehicles to the system n i /ns may be obtained as an input to the model, which can be approximated using a probability distribution or extracted from an agent-based model (e.g., MATSim). In contrast, all the other transition events, n i ns/s , n i s/p , n i p/ns , and n i ns/ are deterministic and relate to average values.

The number of vehicles in transition from non-searching to searching, ( n i ns/s )

This can be calculated by considering the total inflow, through-traffic, and the distance required to shift state from non-searching to searching (l ns/s ) :

\({\gamma }_{ns/s}^{i^{\prime} }\) : is a binary variable (0 or 1) that indicates whether the vehicle is ready to begin searching at time slice (i) . Two conditions must be met for the value to equal 1: the vehicles must have driven at least a distance of l ns/s before starting the search, and they must not have previously started the search.

At any given time slice i , the total number of vehicles that start searching for parking may include vehicles that entered the area prior to the time slice, i’є [1, i-1] .

The first part of the equation of Eq. ( 4 ), \({\sum }_{i^{\prime} =1}^{i-1}(1-{\beta }^{i^{\prime} })\cdot {n}_{/ns}^{i^{\prime} }\) represents the portion of vehicles that need to be parked (i.e., total demand minus through traffic ( β% )).

The number of vehicles in transition from searching to parking (n i s/p ) : Calculated as a function of the number of available parking spaces A i , the number of vehicles looking for parking spaces N i s , and the distance that an average searcher may cover during that time slice according to the length of the network d i /L . The formulation is based on three possible contrary scenarios between ( d i ), (s i ), and (L); in which ( d i ) is the average cruising distance, ( s i ) is the spacing between adjacent vehicles, and ( L ) is the length of the network.

Scenario 1 : If d i є [ 0, s i ]

Scenario 2 : If d i є ( s i , L ]

Scenario 3 : If d i є [ L, ∞ )

Scenario 2 can be further subdivided into three sub-scenarios: m i  >  A i , m i  =  A i , and m i  <  A i , respectively; where m i is the maximum number of vehicles passing at a specific point in time slice ( i) , and ( A i ) is the available parking space at the beginning of time slice (i) . The formula is simplified as Eq. ( 5 ) with respect to the time slice ( t) range, and additional details can be obtained (Cao and Menendez, 2015 ).

The number of vehicles in transition from parking to non-searching state ( n i p/ns )

Derived from the distribution of parking duration;

where, f(td) represents the probability density function of parking duration, and the integral part indicates whether or not vehicles are ready to depart between previous time slices.

As with n i ns/s , at any given time slice i , the total number of vehicles departing parking may include vehicles that accessed parking in the previous time slices, i’ є [1, i -1]. The probability that these vehicles depart parking during time slice i is equal to the probability of the parking duration being between ( i- i’ ) *t and ( i  + 1 – i’ ) *t , the integral expression in Eq. ( 6 ).

The number of vehicles that leave the area in each time slice ( n i ns/ )

The number of departing vehicles calculated by adding up the number of through vehicles and the number of vehicles that reached the required distance to leave the system from parking at that time slice t , Eq. ( 7 ):

γ i / ′ and γ ′ p/ : are binary variables (0 or 1) that indicate whether these vehicles leave the area within time slice i or not. Two conditions must be met for each case for the value to be equal to 1: the vehicles must have driven at least a distance of l / or l p/ to leave the area, and they have not left the area previously.

At each time slice, the transition matrix features of the system continuously update the number of parking spaces ( A i ), the density ( K I ), and speed ( v i ) dynamically, Eqs. ( 8 ), ( 9 ) and ( 10 ), respectively:

Graphically, a queueing diagram (cumulative number of vehicles versus time graph) is used to estimate the total travel time and the average cruising time. Figure 3 illustrates a time graph of the cumulative number of vehicles transitioning between each transition event.

Theoretical queuing diagram of vehicles on parking—urban networks.

A validation of the model implemented performance is conducted by examining the aggregate cruising distance and time, formulated as Eqs. ( 11 ) and ( 12 ) below:

where, T s ( a , b ) and D s ( a , b ) are the commutative cruising time and the distance from a to b , respectively.

Studying area

Two contrasting but significant central business district (CBD) networks, Xujiahui in Shanghai and Bahnhofstrasse in Zurich, were selected for empirical analysis, as illustrated in Fig. 4 .

a Xujiahui CBD, Shanghai and b Bahnhofstrasse, Zurich.

Xujiahui, a lively business district in Shanghai, symbolizes the vibrant economic expansion and urban progress of mainland China. It is renowned for its densely packed array of shopping centers, corporate offices, and cultural attractions, presenting a miniature representation of modern Chinese city life. In contrast, Bahnhofstrasse in Zurich exemplifies the stability and prosperity of Central Europe. It enjoys international recognition as one of the most prestigious shopping streets globally, adorned with upscale boutique stores, financial establishments, and historical landmarks that merge traditional European architecture with present-day economic endeavors. The deliberate choice of these two CBD regions was due to their significant portrayal of the socio-economic, political, and financial hubs in the respective areas. A large portion of their central business districts is dedicated to markets and stores, providing an excellent environment for examining the dynamics of parking systems in urban commercial zones.

This comparative analysis encompasses three primary evaluations. Initially, we assessed the present effectiveness of the prevailing parking systems within these central business districts. Subsequently, the responsiveness of these systems to parking policy modifications was investigated by employing an elasticity methodology, allowing for a more nuanced comprehension of the potential consequences resulting from minor policy adjustments. Finally, the ramifications of these policy changes on traffic emissions were quantified, offering a comprehensive understanding of the environmental implications arising from parking policies in prominent urban areas.

The estimated and real input data of the two CBDs: Bahnhofstrasse and Xujiahui, are mainly from the two cited literatures (Cao et al., 2019 ; Sun et al., 2016 ), respectively.

Bahnhofstrasse CBD

The location is situated in Zone A, in which only 10% of the parking lots are free to park (Cao et al., 2019 ). In Zurich’s parking zoning scheme, Zone A corresponds to the central city region with the strictest parking regulations and highest charges. The purpose of these regulations and fees is to control traffic congestion and promote frequent turnover of parking spaces. Zone B covers urban areas that are not as heavily regulated, offering a balance between accessibility for residents and visitors and turnover of parking spots. On the other hand, Zone C includes peripheral or residential areas where parking restrictions are minimal, often free or inexpensive. These areas are intended for longer-term parking and use by residents.

Basic information

The area is around 0.3  km², with a total length (L) of 7.7 km and 15.4 in lane–km . The total number of existing parking spots (A ) in the network is 539, including 207 on-street parking spaces and two garages named Jelmoli and Talgarten (See Table 2 below), with the capacity of 222 and 110, and charging 3 and 4 CHF/h, respectively.

Initial conditions and traffic demand

The initial condition for non-searching and searching states is zero, but for the number of vehicles in parked states, N (0) p is 183. The proportion of new arrivals that correspond to through traffic is estimated as 23%.

Parking duration

The average parking duration is 227 min, and the parking duration Vs. frequency graph is approximated to the shape of the Gamma probabilistic distribution function of f(td)~Gamma (1.6,142) (see Fig. 5 ).

Probability distribution function of parking duration (f (td)) .

Traffic properties of the area

Free-flow speed, critical, and min speed are given as 19.64 km/h., 12.5 km/h., and 4.54 km/h., respectively. The maximal traffic demand of the network is 250 veh/h/lane, and the critical and jam densities are set as 20 veh/km/lane and 55 veh/km/lane, respectively.

Traveling distance

All distance inputs of the model kept being uniformly distributed between 0.1 and 0.7, which means ~U (1/3 r , (2 + 1/3) r ) for the given radius(r) = 0.3 km.

Xujiahui CBD

The area is located in Xuhui district in Shanghai and is historically referred to as the area of commerce and culture (Sun et al, 2016 ).

The entire area is around 4.04  km ² , with a total length (L) 10.155 km and 34.21 in lane–km. The total number of the existing parking spots (A) (for public use) in the network is 2768 parking spaces (see Table 3 . below).

Initial Conditions and traffic demand

The initial condition for the non-searching and searching states is zero, but for the number of vehicles in parked states assumed to be 940 , to neglect the difference between the initial conditions of the two systems, the same proportion as Bahnhofstrasse is considered. The proportion of new arrivals that corresponds to through traffic is estimated at 70% (Ni and Sun, 2018 ) due to the nature of the traffic infrastructure around the network.

The average parking duration is 127-min (Sun et al., 2016 ), and the parking duration vs. frequency graph is approximated to the shape of Gamma probabilistic distribution function of f ( td ) ~Gamma (4.98, 25.46) (see Fig. 5 ).

MFD data from April 2015, March 18–31, 2016, and August 1–14, 2016 : Free-flow speed, critical and min speed given as 34.9 km/h, 19.46 km/h, and 8.59 km/h, respectively. The maximum Traffic demand of the network is 1275 veh/h/lane. Critical and jam density are set as 65.5 veh/km/lane, and 148.44 veh/km/lane, respectively.

All travel distances kept being uniformly distributed between 0.38 and 2.646 km, which means ~ U (1/3 r , (2 + 1/3) r ) for the given radius(r) = 1.134 km.

Results and comparisons

Parking occupancy : As illustrated (Fig. 6 ), the Bahnhofstrasse appeared to be at the saturation level near the peak hour demand, whereas the Xujiahui maximum peak state is estimated at around 55% occupancy percentage. The saturation state in which there are more searching vehicles than available parking spaces, is recorded around the two peak sections between 9 a.m. to 11 a.m. and then from 7 p.m. to 9 p.m. At this saturation period in Bahnhofstrasse CBD, around 16 vehicles enter and leave the area without meeting their interest in parking.

Parking occupancy variation in 24 h in the two CBDs.

Cruising time and distance : The system dynamics model output for the aggregate daily cruising time and distance for Bahnhofstrasse is 213.19 h and 4178.27 km, respectively, but that of Xujiahui is 60 h and 2090.8 km, respectively. (Refer to Tables 3 and 4 in “A” row). The results were obtained by summing each dynamic time slice from 0 to 1440, of Eqs. ( 11 ) and ( 12 ).

Parking supply adjustment

Accordingly, the parking performance behavior of CBDs was examined during the introduction of parking policies of ±10% up to ±50% and then the corresponding performance indicators were obtained. ±10% up to ±50% of parking lots are used to assess the impact of changes introduced to the parking and traffic systems on the indicators. Sensitivity, elasticity, and indirect impact on the environment are quantified.

Sensitivity analysis

Sensitivity analysis was conducted to demonstrate how much the parking performance would be affected when certain parking-related policies were applied to the system. By introducing ±10% up to ±50% on the two CBDs, the cruising distance and time changes were obtained and are presented in Table 4 due to the parking space plus–minus from the existing number of parking spaces (A).

The results clearly demonstrate that the cruising time and distance of Xujiahui CBD are more stable than the Bahnhofstrasse CBD against the percentage change in parking space supply. The Xujiahui CBD experienced changes in parking performance after (‒45%) parking spaces were added from the base 2768 number of parking spaces. As a result, the Bahnhofstrasse CBD observed unstable and dramatic changes in parking performance between +20% and ‒50% from the base 539 parking spaces.

Elasticity analysis

Elasticity quantifies how much percentage of cruising distance will change as 1% of parking space change is introduced by Eq. ( 13 ). The purpose is to demonstrate how changes in parking policies affect the performance of the parking-traffic system. As shown in Fig. 7 , Bahnhofstrasse CBD is less stable, both at plus or minus 1%, with a range of up to 8% elasticity. On the other hand, Xujiahui CBD changes begin at ‒40% space introduced, and rise to 3% at ‒50%, demonstrating better stability.

a Bahnhofstrasse and b Xujiahui CBD.

Traffic emission analysis

Traffic emission analysis is an extension of the parking performance-policy comparison, which discusses the effects of parking policy adjustments on the environment. The transportation sector accounted for approximately 20% of global greenhouse gas emissions in 2017 without considering international aviation and maritime emissions (EEA, 2018 ). From the most significant pollutants emitted by on-road vehicles (EEA, 2018 ), CO (Carbon Monoxide), VOCs (Volatile Organic Compound), NOx (Nitrogen Oxides) and PM (Particle Matter) have been regulated in legislation, as well as FC (Floro Carbons) and CH 4 (Methane), which have a significant impact on Ozone depletion and greenhouse gases emissions, have been selected for this traffic emission comparison (Sun et al., 2020 ; Sun et al., 2021a ; Sun et al., 2021b ). A particular type of vehicle used for the carriage of passengers with no more than eight seats in addition to the driver’s seat and vehicles used for the carriage of goods with a maximum weight not exceeding 3.5 tons were selected due to the heavy-duty vehicle restrictions in both CBDs.

The average speed approach of the COPERT emission model (Ntziachristos et al., 2009 ) was used to calculate overall traffic emissions as a function of the emission factors and activity data. As this study mainly concentrated on the impact of cruising distance on the environment, only hot emissions were considered. Exhaust hot traffic emissions are described by emission factors (EFs), which describe the mass (unit: gm) of a compound emitted per driven distance (Colberg et al., 2005 ), as in Eqs. ( 14 ) and ( 15 ):

Thus, parameters were derived from the nature of a vehicle’s average travel speed (or road type), age, engine size and weight, and detail emission factors of the COPERT model (Ntziachristos and Samaras, 2019 ); and the VKT, vehicle kilometer travel were obtained to assess the impact of parking policy changes on the cruising distance.

Traffic emission : The aggregate yearly VKT is obtained from the daily cruising distance (neglecting seasonal variation for simplicity). Then the emission is weighted by each selected pollutant (Fig. 8 ). The result demonstrates that the Bahnhofstrasse parking system is highly sensitive to the policy change of reducing parking space.

Parking duration policy changing

During the second parking policy adaptation, the average parking duration was adjusted by ±10% up to ±50% from the existing mean value. After examining the impact of parking duration shifts on the two CBDs, traffic emissions are quantified as a function of the resulting aggregate annual cruising distance and traffic nature (Chen et al., 2020 ; Sun et al., 2018 ).

The sensitivity analysis was conducted to determine the effects of the fixed plus–minus of the average parking duration on the performance of the two parking systems. The impact of the average parking duration shift on the cruising nature of the parking systems is presented in Table 5 . The Xujiahui CBD shows no change and maintains the system’s independence from parking duration; on the other hand, the Bahnhofstrasse CBD continues to be sensitive to shifts in parking duration.

To illustrate the property of the parking systems, the average parking duration elasticity on cruising distance was calculated. As shown in Fig. 9 , the elasticity in Bahnhofstrasse extends up to more than 6%, while in Xujiahui CBD, a shift in parking duration doesn’t have an impact with zero elasticity.

The environmental impacts for the two cases were also assessed, and are shown in Fig. 10 . The Bahnhofstrasse CBD was found to have an increase in traffic emissions due to its longer cruising distance, while Xujiahui CBD does not show any significant impacts.

a Bahnhofstrasse CBD and b Xujiahui CBD.

Parking fee adjustment

The parking occupancy results in Fig. 6 show that only Bahnhofstrasse recorded saturation intervals. To increase the efficiency of the parking system, parking fees were adjusted in the network (Culjkovic, 2018 ). The saturation intervals were recorded between 9:00 and 11:00, and between 19:00 and 21:00, during which 16 vehicles searched for parking, then had to leave the area without getting a spot. Considering these unsatisfied demands for parking, adjustment of parking fees may be introduced as an option, as in Eq. ( 16 ):

From the simulation model, the overall cruising time is 213 h, among which the peak load is 2156 (4 space hrs. * 539), and the average value of time is 22.6 CHF per hour (Francesca, 2019 ), with an additional parking fee calculated as 2.23 CHF. This means the demand at this peak time must be subjected to 5.23 CHF and 6.23 CHF per hour for the Jelmoli garage and Talgarten garage, respectively. Meanwhile, pricing in reality is complex, and even its antecedent shadow could have sociopolitical consequences. A further investigation of price elasticity to demand and cross-relationships between different factors may help to provide insights into the behavioral economics of certain transportation networks.

Conclusions

This study models urban parking system dynamics using macroscopic network data, instead of individual microscopic data, which saves and minimizes data collection and computational costs significantly. Moreover, the model doesn’t require complex simulations and can be solved with a comparable simple numerical solver or Excel spreadsheet. The main objective is to determine how parking-traffic systems change as a result of parking policy changes, including cruising time, cruising distance, and parking occupancy variation. The sensibility and elasticity results on ±10 to ±50 adjustments of policy changes were examined. Then, by adopting a straightforward emission model, the paper articulated how different parking policy changes impact the environment in different CBDs. The emissions of different pollutants were calculated as a function of the cruising distance from policy changes. Additionally, it was found that parallel peak hours and initial conditions of parking state proportion offered a clear picture of the performance of the parking system with policy changes. The comparative study summarizes that Bahnhofstrasse CBD is more responsive to parking policy changes than Xujiahui CBD. By investigating policy impacts on both parking and traffic indicators, the results were found to be dependent on the aggregated daily cruising distances, which may be indirectly reflected in the nature of the infrastructural development, the proportion of through-traffic, the culture of vehicle ownership, and driver behavior of the two cities. Therefore, one possible explanation is that Xujiahui CBD in Shanghai was largely considered as a bypass rather than a destination or trip ending compared to Bahnhofstrasse CBD. This deductive reasoning may be related to the varying percentage of through vehicles, which also has a domino effect on the resulting aggregate daily cruising distance.

The research employed a model that utilizes the macroscopic fundamental diagram to examine parking and traffic flow more efficiently and with reduced computational expenses. In contrast to studies based on microscopic analysis, which necessitate extensive data and intricate calculations, this paper employs typical values and fundamental principles of probability and traffic flow theories. The methodology not only simplifies the analysis but also improves user-friendliness for a broader audience, including urban planners and traffic managers without specialized knowledge in traffic modeling.

Combined data at the network level over time were utilized to offer a notable benefit for practical uses, which allow for a more efficient and affordable examination of traffic and parking trends and are especially important for cities with limited resources. Additionally, the dynamic or continuous-time modeling and calculations provide valuable information about the real-time operation of parking and traffic systems. This aspect is particularly advantageous for urban managers and policymakers as it empowers them to make better-informed choices regarding traffic management and urban development. The framework can be useful for city planners and traffic authorities to improve parking policies and manage traffic flow. By comprehending larger patterns and changes, managers may stipulate better regulations for parking, thus decreasing traffic congestion and enhancing urban mobility. Furthermore, the modeling capability in various policy scenario simulations may also serve as a valuable tool in strategic planning, allowing cities to anticipate and address potential problems in the transportation systems.

Overall, this paper offers a structure for a pragmatic method to evaluate parking and traffic that has the potential to assist with urban planning and administration. The simplicity, affordability, and ability to analyze in real-time make it an essential resource for city officials and decision-makers to improve urban mobility and sustainability (Ma et al., 2023 ; Nian et al., 2024 ). The research holds great potential for enhancing our comprehension of parking system effectiveness and its ecological consequences. Nonetheless, it is imperative to recognize the limitations of the study, as they open up opportunities for future research directions. First, the model configuration utilized, although effective for conducting comparative analysis, is mainly based on a number of simplified assumptions. The assumption that parking locations are uniformly distributed fails to acknowledge the intricate spatial dynamics of urban areas, where parking availability varies due to various local factors. Future models should incorporate spatial variations to accurately reflect the complexities inherent in urban landscapes. Moreover, the use of a fixed peak hour time and a constant demand distribution does not capture the variable nature of urban traffic. Traffic conditions in urban areas are subject to fluctuations influenced by diverse factors, which may be modeled through a dynamic approach. For example, the interaction between local and transit traffic in urban settings is a dynamic phenomenon that requires further exploration in subsequent studies to fully understand its impact on traffic flow and parking demand. In terms of estimating traffic emissions, applying uniform emission factors across different cities may not accurately represent the unique environmental contexts of each location. A finer understanding of environmental impacts may be obtained by incorporating city-specific emission factors and pollutant types in future studies. Using the European traffic emission assessment model without adaptation poses limitations when applied to the Chinese context. Considering regional differences in vehicle types, driving behaviors, and urban layouts is crucial for obtaining emission factors that are more accurate and specific to each region.

While this study offers valuable insights into Xujiahui in Shanghai and Bahnhofstrasse in Zurich, it is important to recognize that these specific areas may not fully represent the diverse range of urban settings. Therefore, including a more varied selection of urban areas would allow for assessing the broader applicability and adaptability of the developed model. To conclude, this research contributes to understanding of urban traffic and parking management by uncovering important relationships and patterns. However, the outlined limitations underscore the necessity for more comprehensive studies, which may be addressed to provide more accurate assessments of environmental impacts and support the development of effective urban planning and policy-making strategies aimed at enhancing urban mobility and sustainability. To optimize performance evaluation, future studies should explore how dynamic pricing influences parking demand and traffic indicators. Additionally, quantifying behavioral changes resulting from updated policies may assist in establishing a framework for optimizing cruising distance and time with efficient parking occupancy, which is crucial in developing future transportation applications such as smart parking systems, as well as designing continual evaluations of smart applications like smart garages, smart parking apps, smart parking reservations, and Parking Variable Message Signs (VMS). In fact, the transport bureau of Xujiahui district, Shanghai is planning for possible dynamic parking tolling in several selected parking lots, which would definitely affect the parking demand, and with some meaningful outputs, although the counterpart CBD, Bahnhofstrasse, may not have similar implementations. Furthermore, incorporating realistic assumptions about variations in the value of time (VOT) across different income groups may address model limitations that only consider car demand while neglecting the factors influencing on- and off-street parking decisions, which may be explored in future research efforts.

Data availability

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

Anderson SP, De Palma A (2004) The economics of pricing parking. J Urban Econ 55(1):1–20

Article   Google Scholar  

Arnott R, De Palma A, Lindsey R (1991) A temporal and spatial equilibrium analysis of commuter parking. J Public Econ 45(3):301–335

Arnott R, De Palma A, Lindsey R (1993) A structural model of peak-period congestion: A traffic bottleneck with elastic demand. Am Econ Rev 83(1):161–179

Google Scholar  

Arnott R, Rowse J (1999) Modeling parking. J Urban Econ 124:97–124

Arnott R, Inci E (2010) The stability of downtown parking and traffic congestion. J Urban Econ 68(3):260–276

Axhausen KW, Polak JW (1991) Choice of parking: stated preference approach. Transportation 18(1):59–81

Balac M, Ciari F, Axhausen KW (2017) Modeling the impact of parking price policy on free-floating carsharing: case study for Zurich, Switzerland. Transport Res Part C Emerg Technol 77:207–225

Benenson I, Martens K, Birfir S (2008) PARKAGENT: an agent-based model of parking in the city. Comput Environ Urban Syst 32(6):431–439

Boyles SD, Tang S, Unnikrishnan A (2015) Parking search equilibrium on a network. Transport Res. Part B Methodol 81:390–409

Cao J, Menendez M (2015) System dynamics of urban traffic based on its parking-related-states. Transport Res. Part B Methodol 81:718–736

Cao J, Menendez M, Waraich R (2019) Impacts of the urban parking system on cruising traffic and policy development: the case of Zurich downtown area, Switzerland. Transportation. 46:883–908

Cavadas J, Antunes AP (2019) An optimization model for integrated transit‑parking policy planning. Transportation. 46:1867–1891

Chen Q, Wang Y, Pan S (2016) Characteristics of parking in Central Shanghai. China. J Urban Plan Dev 142(3):05015012

Chen FX, Yin ZW, Ye Y, Sun DJ (2020) Taxi hailing choice behavior and economic benefit analysis of emission reduction based on multi-mode travel big data. Transport Pol. 97:73–84

Colberg CA, Tona B, Stahel WA, Meier M, Staehelin J (2005) Comparison of a road traffic emission model (HBEFA) with emissions derived from measurements in the Gubrist road tunnel, Switzerland. Atmos Environ 39(26):4703–4714

Article   ADS   CAS   Google Scholar  

Culjkovic V (2018) Influence of parking price on reducing energy consumption and CO2 emissions. Sust Cities Soc 41:706–710

Daganzo CF, Geroliminis N (2008) An analytical approximation for the macroscopic fundamental diagram of urban traffic. Transport Res Part B Methodol 42(9):771–781

Dong Y-H, Peng F-L, Qiao Y-K (2021) Identification of the spatial distribution pattern and driving forces of underground parking space based on multi-source data: a case study of Fuzhou City in China. Sust Cities Soc 72:103084

EEA (2018) Greenhouse gas emissions from transport—European Environment Agency. EEA https://www.eea.europa.eu/data-and-maps/indicators/transport-emissions-of-greenhouse-gases/transport-emissions-of-greenhouse-gases-11

Francesca L (2019) “The World’s Best-Paid Cities.” Resonance. Forbes https://www.forbes.com/2009/08/24/best-paid-cities-lifestyle-real-estate-worlds-income-salary.html#d191b2e29883

Gallo M, D’Acierno L, Montella B (2011) A multilayer model to simulate cruising for parking in urban areas. Transport Pol 18(5):735–744

Geroliminis N, Daganzo CF (2008) Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings. Transport Res Part B Methodol 42:759–770

Gu Z, Najmi A, Saberi M, Liu W, Rashidi TH (2020) Macroscopic parking dynamics modeling and optimal real-time pricing considering cruising-for-parking. Transport Res C Emerg Technol 118:102714

Gu Z, Safarighouzhdi F, Saberi M, Rashidi TH (2021) A macro-micro approach to modeling parking. Transport Res B Methodol 147:220–244

Horni A, Montini L, Waraich RA, Axhausen KW (2013) An agent-based cellular automaton cruising-for-parking simulation. Transportation Lett Int J Transport Eng 5(4):167–175

Huang Y, Sun D, Li A, Axhausen KW (2021) Impact of bicycle traffic on the macroscopic fundamental diagram: some empirical findings in Shanghai. Transportmetrica A 17(4):1122–1149

Huang Y, Sun D, Li A, Axhausen KW (2022a) Three-dimensional macroscopic fundamental diagram for car and bicycle heterogeneous traffic. Transportmetrica B 10(1):312–339

Huang Y, Sun D, Li A, Garrick N, Zhang S, Liu W (2022b) Spatiotemporal approach for evaluating the vehicle restriction policy with multi-sensor data, transport. Res Rec 2676(8):724–736

Jakob M, Menendez M, Cao J (2020) A dynamic macroscopic parking pricing and parking decision model. Transportmetrica B 8(1):307–331

Jakob M (2021) Parking policies and their impacts on urban networks, Doctoral dissertation, ETH Zurich

Kang D, Hu F, Levin MW (2022) Impact of automated vehicles on traffic assignment, mode split, and parking behavior. Transport Res D-TR E 104:103200

Kladeftiras M, Antoniou C (2013) Simulation-based assessment of double-parking impacts on traffic and environmental conditions. Transport Res Rec 2390(1):121–130

Levy N, Martens K, Benenson I (2013) Exploring cruising using agent-based and analytical models of parking. Transportmetrica A 9(9):773–797

Loder A, Ambühl L, Menendez M, Axhausen KW (2017) Empirics of multi-modal traffic networks: using the 3D macroscopic fundamental diagram. Transport Res Part C Emerg Technol 82:88–101

Ma W, Wang N, Li Y, Sun DJ (2023) 15-min pedestrian distance life circle and sustainable community development in Chinese metropolitan cities: a diagnosis. Humanit Soc Sci Communs 10(364):1–14

CAS   Google Scholar  

Marki F, Charypar D, Axhausen KW (2014) Agent-based model for continuous activity planning with an open planning horizon. Transportation 41(4):905–922

Mesfin BG, Sun DJ, Peng B (2022) Impact of COVID-19 on urban mobility and parking demand distribution: a global review with case study in Melbourne, Australia. Int J Environ Res Public Health 19(13):7665. https://doi.org/10.3390/ijerph19137665

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ni X-Y, Sun DJ (2017) Agent-based modelling and simulation to assess the impact of parking reservation system. J Adv Transport ume 2017:2576094

Ni X-Y, Sun DJ (2018) An agent-based simulation model for parking variable message sign location problem. Transport Res Rec 2672(19):135–144

Ni X-Y, Sun DJ, Zhao J, Chen Q (2024) Two-stage allocation model for parking robot systems using cellular automaton simulation. Transport Res Rec (2024) https://doi.org/10.1177/03611981241230530

Nian GY, Pan H, Huang J, Sun DJ (2024) Labor supply decisions of taxi drivers in megacities during COVID-19 pandemic period. Travel Behav Soc. 35:1–14. https://doi.org/10.1016/j.tbs.2024.100745

Ntziachristo L, Gkatzoflias D, Kouridis C, Samaras Z (2009) COPERT: a European road transport emission inventory model. Info Tech Environ Eng 2009(4):491–504

Ntziachristos L, Samaras Z (2019) COPERT documentation. EMISIA SA https://www.emisia.com/utilities/copert/documentation/ (2019)

Pierce G, Shoup D (2013) SFpark: pricing parking by demand. Access 43(Fall):20–28

Qian Z, Xiao F, Zhang HM (2012) Managing morning commute traffic with parking. Transport Res B Methodol 46(7):1346–1359

Shen T, Hong Y, Thompson MM, Liu J, Wu L (2020) How does parking availability interplay with the land use and affect traffic congestion in urban areas? The case study of Xi’an. China Sust Cities Soc 57:102126

Shoup DC (1997) The high cost of free parking. J Plan Educ Res 17(1):3–20

Shoup DC (2006) Cruising for parking. Transport Pol 13(6):479–486

Sun DJ, Ni X-Y, Zhang L (2016) A discriminated release strategy for parking variable message sign display problem using agent-based simulation. IEEE Trans Intell Transp Syst 17(1):38–47

Sun DJ, Zhang K, Shen S (2018) Analyzing spatiotemporal traffic line source emissions based on massive Didi online car-hailing service data. Transport Res D-TR E 62:699–714

Sun DJ, Ding X (2019) Spatiotemporal evolution of ridesourcing markets under the new restriction policy: a case study in Shanghai. Transport Res A-POL 130:227–239

Sun DJ, Yin Z, Cao P (2020) An improved CAL3QHC model and the application in vehicle emission mitigation schemes for urban signalized intersections. Build Environ 183:107213. Article ID

Sun DJ, Zhang Y, Zhang L (2021a) Spatiotemporal distribution of traffic emission based on wind tunnel experiment and computational fluid dynamics (CFD) simulation. J Clean Prod 281:124495

Sun DJ, Wu S, Shen S, Xu T (2021b) Simulation and assessment of traffic pollutant dispersion at an urban signalized intersection using multiple platforms. Atmos Pollut Res 12(7):1–13. 101087

Waraich RA, Axhausen KW (2012) Agent-based parking choice model. Transport Res Rec 2319:39–46

Weinberger R, Kaehny J, Rufo MUS (2012) Parking policies: an overview of management strategies institute for transportation and development policy. Institution for Transportation and Development Policy, https://www.itdp.org/publication/u-s-parking-policies-an-overview-of-management-strategies/

Wen L, Kenworthy J, Guo X, Marinova D, Kenworthy J (2019) Solving traffic congestion through street renaissance: a perspective from dense Asian cities. Urban Sci 3(1):18

Xiao LL, Liu TL, Huang HJ (2019) Tradable permit schemes for managing morning commute with carpool under parking space constraint. Transportation. 48:1563–1586

Zhang P, Chen Z, Liu H (2019) Study on the layout method of Urban underground parking system-a case of underground parking system in the Central Business District in Linping New City of Hangzhou. Sust Cities Soc 46:101404

Zhao C, Li S, Wang W, Li X, Du Y (2018) Advanced parking space management strategy design: an agent-based simulation optimization approach. Transport Res Rec 2672(8):901–910

Zhao C, Liao F, Li X, Du Y (2021) Macroscopic modeling and dynamic control of on-street cruising-for-parking of autonomous vehicles in a multi-region urban road network. Transport Res Part C Emerg Technol 128:103176

Zhao C, Cao J, Zhang X, Du Y (2022) From search-for-parking to dispatch-for-parking in an era of connected and automated vehicles: a macroscopic approach. J Transport Eng A: Syst 148(2):1–14

Download references

Acknowledgements

The research was funded in part by the National Nature Science Foundation of China [52172319, 71971138], the National Social Science Foundation of China (22XJY030), and the Xuzhou Industry-University-Research Cooperation Project [KC21340]. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors. Part of the manuscript has been accepted and presented in the 101st Annual Meeting of Transportation Research Board, Washington, DC, 2022.01.14, submitted as a long abstract with no more than 2000 words. The authors are grateful for the opportunity for the conference organizers.

Author information

Authors and affiliations.

Smart City and Intelligent Transportation Interdisciplinary Center, Chang’an University, Xi’an, 710064, Shaanxi Province, China

Biruk Gebremedhin Mesfin

School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

College of Future Transportation, Chang’an University, Xi’an, 710064, Shaanxi Province, China

Zihao Li & Daniel (Jian) Sun

Institute for Transport Planning and Systems (IVT), ETH Zurich, Stefano-Franscini-Platz 5, 8093, Zurich, Switzerland

Daniel (Jian) Sun

Jiangsu Ren’an High-tech Co., Ltd, Xuzhou, 221008, Jiangsu Province, China

Deming Chen

School of Economics and Management, Chang’an University, Xi’an, 710064, Shaanxi Province, China

You can also search for this author in PubMed   Google Scholar

Contributions

The authors confirm their contribution to the paper as follows. Biruk Gebremedhin Mesfin: literature search and review, experiment design and conduction, modeling, manuscript writing; Zihao Li: experiment design and conduction, manuscript review and editing; Daniel(Jian) Sun: modeling, scheme proposal, manuscript review and editing; Deming Chen: experiment design and conduction, manuscript review and editing; Yueting Xi: modeling, manuscript review and editing, conclusion.

Corresponding author

Correspondence to Yueting Xi .

Ethics declarations

Competing interests.

The authors declare no competing interests. The authors confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.

Ethical approval

Ethical approval is not applicable as this study did not involve human participants.

Informed consent

There are no human subjects involved in this article and informed consent is not applicable.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Mesfin, B.G., Li, Z., Sun, D.(. et al. Urban traffic-parking system dynamics model with macroscopic properties: a comparative study between Shanghai and Zurich. Humanit Soc Sci Commun 11 , 616 (2024). https://doi.org/10.1057/s41599-024-02959-w

Download citation

Received : 15 September 2023

Accepted : 08 March 2024

Published : 13 May 2024

DOI : https://doi.org/10.1057/s41599-024-02959-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

ORIGINAL RESEARCH article

Measuring and improving public space resilience to the covid-19 pandemic: chongqing-china as a case study provisionally accepted.

• 1 Chongqing University, China

The final, formatted version of the article will be published soon.

The COVID-19 pandemic emphasized the importance of public spaces. Accessing public spaces during the pandemic improves physical health, reduces feelings of loneliness, and lessens depression. However, not all public spaces can provide an effective response during the pandemic. The public spaces' ability to respond to the pandemic varies depending on their resilience level, which refers to the capability of those spaces to adapt to the challenges posed by the COVID-19 pandemic and maintain functionality to meet users' needs during this crisis. By investigating the response of existing public spaces to the COVID-19 pandemic and identifying and examining the criteria of pandemic resilience, this study aims to explore and improve public spaces' capability to respond effectively during the pandemic. 169 public spaces in three regions in Chongqing City in China are studied. Four main criteria involving 9 sub-criteria of pandemic resilience that can be integrated into public spaces' planning and design are studied. Three questionnaire surveys are used in this study to examine how public spaces adapt to the pandemic and evaluate the pandemic resilience criteria. The questionnaire data is analyzed using the Statistical Package for Social Sciences (SPSS) software. The pandemic resilience criteria are assessed and analyzed using a Geographic Information System (GIS). The study utilized the analytic hierarchy process (AHP) to assign weights to the criteria of pandemic resilience. Weighted overlay analysis (WOA) is applied to assess the pandemic resilience level in public spaces. Results indicate various possibilities for pandemic resilience depending on the characteristics of the area. However, these resilience levels are inadequate to respond effectively to the pandemic, resulting in diminished utilization of public spaces during the COVID-19 pandemic across all studied regions compared to the periods preceding the pandemic and after the complete reopening. This study presents a remarkable source for strengthening the resilience of cities against pandemic emergencies.

Keywords: Chongqing, COVID-19 pandemic, Planning and design, preparedness, public space, resilience, response, Sustainable cities

Received: 08 Feb 2024; Accepted: 13 May 2024.

Copyright: © 2024 ALAWI, Chu and Rui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Dongzhu Chu, Chongqing University, Chongqing, 400030, China

People also looked at

Numerical Evaluation of Pile Length, Lateral Bulging and Encasement Length: A Comparative Study on Ordinary and Encased Granular Piles

• Original Paper
• Published: 12 May 2024
• Volume 10 , article number  48 , ( 2024 )

Cite this article

• Shaid Yousuf   ORCID: orcid.org/0000-0002-3420-4162 1 &
• Narendra Kumar Samadhiya 1  

Granular piles, either ordinary or encased with geosynthetic materials are being extensively used as one of the ground improvement techniques, depending on the strength of the adjoining soil. The optimum granular pile (GP) length is still a matter of research, even though the approach is widely established in the literature. In the present study, a thorough and detailed parametric analysis has been carried out to ascertain the optimum length for ordinary and encased granular piles using a 2D axisymmetric finite element model. The soil behaviour has been modelled with the linearly elastic perfectly plastic Mohr–Coulomb failure criterion constitutive model. The parameters considered in this study are area replacement ratio, encasement stiffness, soil properties, infill material properties, and crust layer thickness. The findings revealed that the parameters with the greatest influence on the optimum length are the area replacement ratio, encasement stiffness, surrounding soil strength properties, and friction angle of the infill material. For encased granular piles, the optimum length was often found to be longer than ordinary granular piles. It was found that the optimum length for ordinary and encased GP ranges between 0.8–2.12 and 1–2.75 times of footing diameter ( D ), respectively. Through this study, an effort has also been made to investigate how the aforementioned parameters affect the radial bulging of the end-bearing GP. The upper section of 0.5–1.5 D showed excessive bulging in each case. Additionally, the optimum encasement length was determined, and it was found that increasing the encasement length beyond 1.5 D results in minimal improvement. Furthermore, a multiple regression analysis was employed to establish the correlation between the optimum length of GP and potential influencing factors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Data Availability

The authors affirm that the data supporting the findings of this study can be found within the article. Furthermore, upon a reasonable request, the corresponding author is willing to provide the raw data that underlie the findings of this study.

Abbreviations

• Granular pile

Ordinary granular pile

Encased granular pile

Soil bed thickness

Granular pile length

Optimum granular pile length

Encasement length

Optimum encasement length

Footing diameter

Normalized depth

Improvement factor

Poisson’s ratio

Saturated unit weight

Coefficient of earth pressure at rest

Soil cohesion

Internal frictional angle soil/granular pile

Dilation angle soil/granular pile

Soil/granular pile stiffness

Area replacement ratio

GP diameter

Granular bed thickness

Encasement stiffness

Barksdale RD, Bachus RC (1983) Design and construction of stone columns, vol. I (No. FHWA/RD-83/026; SCEGIT-83-104). Turner-Fairbank Highway Research Center

Ayadat T (2022) Geotechnical performance of encapsulated and stabilized stone columns in a collapsible soil. Int J Geomech 22(6):04022057

Article   Google Scholar  

Hajiazizi M, Nasiri M (2018) Experimental and numerical study of earth slope reinforcement using ordinary and rigid stone columns. Int J Min GeoEng 52(1):23–30

Google Scholar  

Pham TA, Dias D (2021) 3D numerical study of the performance of geosynthetic-reinforced and pile-supported embankments. Soils Found 61(5):1319–1342

Shahu JT, Madhav MR, Hayashi S (2000) Analysis of soft ground-granular pile-granular mat system. Comput Geotech 27(1):45–62

Schiosser F, Juran Y (1979) Parametres de conception pour sols artificiellement ameliores. In: Vol. 5 of Comptes Rendus clu 7eme Congres Europeen de Brighton. British Geotechnical Society, Brighton, UK, pp 227–252

Ambily AP, Gandhi SR (2007) Behavior of stone columns based on experimental and FEM analysis. J Geotech Geoenviron Eng 133(4):405–415

Chen JF, Li LY, Zhang Z, Zhang X, Xu C, Rajesh S, Feng SZ (2021) Centrifuge modeling of geosynthetic-encased stone column-supported embankment over soft clay. Geotext Geomembr 49(1):210–221

Dar LA, Shah MY (2023) Numerical study on the seismic behaviour of embankments on stone column-reinforced soft soils. Transp Infrastruct Geotechnol 10(2):239–258

Hughes JMO, Withers NJ, Greenwood DA (1975) A field trial of the reinforcing effect of a stone column in soil. Geotechnique 25(1):31–44

McKenna JM, Eyre WA, Wolstenholme DR (1975) Performance of an embankment supported by stone columns in soft ground. Geotechnique 25(1):51–59

McKelvey D, Sivakumar V, Bell A, Graham J (2004) Modelling vibrated stone columns in soft clay. Proc Inst Civ Eng Geotech Eng 157(3):137–149

Wehr J (2006) The undrained cohesion of the soil as criterion for the column installation with a depth vibrator. In: Proceedings of the international symposium on vibratory pile driving and deep soil vibratory compaction. TRANSVIB, Paris, pp 157–162

Almeida MSS, Hosseinpour I, Riccio M (2013) Performance of a geosynthetic-encased column (GEC) in soft ground: numerical and analytical studies. Geosynth Int 20(4):252–262

Fattah MY, Zabar BS, Hassan HA (2016) Experimental analysis of embankment on ordinary and encased stone columns. Int J Geomech 16(4):04015102

Miranda M, Da Costa A (2016) Laboratory analysis of encased stone columns. Geotext Geomembr 44(3):269–277

Dash SK, Bora MC (2013) Influence of geosynthetic encasement on the performance of stone columns floating in soft clay. Can Geotech J 50(7):754–765

Al-Taie ET, Al-Kalali HH, Fattah MY (2019) Evaluation of settlement and bearing capacity of embankment on soft soil with reinforced geogrids. Int J Eng Res Technol (Ahmedabad) 8(6):99–103

Hasan M, Samadhiya NK (2017) Performance of geosynthetic-reinforced granular piles in soft clays: model tests and numerical analysis. Comput Geotech 87:178–187

Rezaei MM, Lajevardi SH, Saba H, Ghalandarzadeh A, Zeighami E (2019) Laboratory study on single stone columns reinforced with steel bars and discs. Int J Geosynth Ground Eng 5:1–14

Thakur A, Rawat S, Gupta AK (2021) Experimental and numerical investigation of load carrying capacity of vertically and horizontally reinforced floating stone column group. Geotech Geol Eng 39:3003–3018

Dutta S, Padade AH, Mandal JN (2012) Experimental study on natural bamboo geogrid encased stone column. In: Proc. 5th ARC on Geosynth., Geosynth. Asia-2012, pp 417–426

Basu P, Samadhiya NK, De Dalal SS (2018) An experimental study on random fiber mixed granular pile. Int J Geotech Eng 12(1):1–12

Babu MRD, Dheerendra SR, Nayak S, Majeed JA (2010) Load settlement behavior of stone columns with circumferential nails. In: Indian geotechnical conference, pp 579–82

Fattah MY, Shlash KT, Al-Waily MJ (2013) Experimental evaluation of stress concentration ratio of model stone columns strengthened by additives. Int J Phys Model Geotech 13(3):79–98

Kang B, Wang J, Zhou Y, Huang S (2023) Study on bearing capacity and failure mode of multi-layer-encased geosynthetic-encased stone column under dynamic and static loading. Sustainability 15(6):5205

Mohamadi Merse M, Hosseinpour I, Payan M, Jamshidi Chenari R, Mohapatra SR (2023) Shear strength behavior of soft clay reinforced with ordinary and geotextile-encased granular columns. Int J Geosynth Ground Eng 9(6):79. https://doi.org/10.1007/s40891-023-00492-5

Shahu JT, Kumar S, Bhowmik R (2023) Ground improvement for transportation infrastructure: experimental investigations on cyclic behavior of a group of granular columns. Int J Geomech 23(3):04022309

Pradeep NM, Kumar S, Shukla SK (2023) Evaluation of strength behavior of aggregates mixed with tire chips in granular piles. Iran J Sci Technol Trans Civ Eng 2023:1–16

Pradeep N, Kumar S (2023) Soft soil improvement with encased granular piles composed of aggregates and tire chips mixture: experimental and numerical studies. Iran J Sci Technol Trans Civ Eng 2023:1–25

Ng KS, Tan SA (2015) Settlement prediction of stone column group. Int J Geosynth Ground Eng 1:1–13

Sivakumar V, McKelvey D, Graham J, Hughes D (2004) Triaxial tests on model sand columns in clay. Can Geotech J 41(2):299–312

Muir Wood D, Hu W, Nash DF (2000) Group effects in stone column foundations: model tests. Geotechnique 50(6):689–698

Narasimha Rao S, Prasad YVSN, Hanumanta Rao V (1992) Use of stone columns in soft marine clays. In: Proceedings of the 45th Canadian geotechnical conference, Toronto, Ont, vol 9

Miranda M, Fernández-Ruiz J, Castro J (2021) Critical length of encased stone columns. Geotext Geomembr 49(5):1312–1323

Najjar SS, Sadek S, Maakaroun T (2010) Effect of sand columns on the undrained load response of soft clays. J Geotech Geoenviron Eng 136(9):1263–1277

Remadna A, Benmebarek S, Benmebarek N (2020) Numerical analyses of the optimum length for stone column reinforced foundation. Int J Geosynth Ground Eng 6:1–12

Debnath P, Dey AK (2017) Bearing capacity of geogrid reinforced sand over encased stone columns in soft clay. Geotext Geomembr 45(6):653–664

Black JA, Sivakumar V, Bell A (2011) The settlement performance of stone column foundations. Géotechnique 61(11):909–922

Tan SA, Ng KS, Sun J (2014) Column group analyses for stone column reinforced foundation. In: From soil behavior fundamentals to innovations in geotechnical engineering: honoring Roy E. Olson, pp 597–608

Malarvizhi SN (2007) Comparative study on the behavior of encased stone column and conventional stone column. Soils Found 47(5):873–885

Al-Ani W, Grizi A, Wanatowski D (2021, November) Settlement analysis of column-like elements. In: Proceedings of the 20th international conference on soil mechanics and geotechnical engineering. International society for soil mechanics and geotechnical engineering.

Hamzh A, Mohamad H, Bin Yusof MF (2022) The effect of stone column geometry on soft soil bearing capacity. Int J Geotech Eng 16(2):200–210

Balaam NP, Brown PT (1977) Settlement analysis of soft clay reinforced with granular piles. In: Proc. Fifth Asian Conf. Soil Eng. Bangkok, Thailand, no. 81–92 (1978)

Castro J (2017) Modeling stone columns. Materials 10(7):782

Article   MathSciNet   Google Scholar  

Choobbasti AJ, Pichka H (2014) Improvement of soft clay using installation of geosynthetic-encased stone columns: numerical study. Arab J Geosci 7:597–607

Jaky J (1944) The coefficient of earth pressure at rest. J Soc Hung Archit Eng 1944:1

Dar LA, Shah MY (2021) Three dimensional numerical study on behavior of geosynthetic encased stone column placed in soft soil. Geotech Geol Eng 39(3):1901–1922

Hasan M, Samadhiya NK (2016) Experimental and numerical analysis of geosynthetic-reinforced floating granular piles in soft clays. Int J Geosynth Ground Eng 2:1–13

Kadhim ST, Parsons RL, Han J (2018) Three-dimensional numerical analysis of individual geotextile-encased sand columns with surrounding loose sand. Geotext Geomembr 46(6):836–847

Lo SR, Zhang R, Mak J (2010) Geosynthetic-encased stone columns in soft clay: a numerical study. Geotext Geomembr 28(3):292–302

Murugesan S, Rajagopal K (2006) Geosynthetic-encased stone columns: numerical evaluation. Geotext Geomembr 24(6):349–358

Tandel YK, Solanki CH, Desai AK (2013) 3D FE analysis of an embankment construction on GRSC and proposal of a design method. Int Schol Res Not 2013:1

Ng KS, Tan SA (2014) Design and analyses of floating stone columns. Soils Found 54(3):478–487

Debbabi IE, Saddek RM, Rashid ASA, Muhammed AS (2020) Numerical modeling of encased stone columns supporting embankments on sabkha soil. Civ Eng J 6(8):1593–1608

Hughes JMO, Withers NJ (1974) Reinforcing of soft cohesive soils with stone columns: 18F, 9R. Ground Engng. V7, N3, MAY, 1974, P42–49. Int J Rock Mech Min Sci Geomech Abstracts 11(11):A234

Aslani M, Nazariafshar J, Ganjian N (2019) Experimental study on shear strength of cohesive soils reinforced with stone columns. Geotech Geol Eng 37:2165–2188

Boumekik NEI, Labed M, Mellas M, Mabrouki A (2021) Optimization of the ultimate bearing capacity of reinforced soft soils through the concept of the critical length of stone columns. Civ Eng J 7(9):1472–1487

Basack S, Indraratna B, Rujikiatkamjorn C (2016) Modeling the performance of stone column–reinforced soft ground under static and cyclic loads. J Geotech Geoenviron Eng 142(2):04015067

Ng KS (2018) Numerical study on bearing capacity of single stone column. Int J GeoEng 9:1–10

Mugahed Sakr M, Azzam WR, Mohamed MK (2022) Behaviour of encased stone columns in soft clay. J Eng Res 6(3):71–75

Keykhosropur L, Soroush A, Imam R (2012) 3D numerical analyses of geosynthetic encased stone columns. Geotext Geomembr 35:61–68

Walton G, Diederichs MS, Alejano LR, Arzúa J (2014) Verification of a laboratory-based dilation model for in situ conditions using continuum models. J Rock Mech Geotech Eng 6(6):522–534

Mitchell JK (1981) Soil improvement-state of the art report. In: Proc., 11th Int. Conf. on SMFE, vol 4, pp 509–565

Gu M, Zhao M, Zhang L, Han J (2016) Effects of geogrid encasement on lateral and vertical deformations of stone columns in model tests. Geosynth Int 23(2):100–112

Yoo C, Lee D (2012) Performance of geogrid-encased stone columns in soft ground: full-scale load tests. Geosynth Int 19(6):480–490

Yoo C (2010) Performance of geosynthetic-encased stone columns in embankment construction: numerical investigation. J Geotech Geoenviron Eng 136(8):1148–1160

Yoo C (2015) Settlement behavior of embankment on geosynthetic-encased stone column installed soft ground–a numerical investigation. Geotext Geomembr 43(6):484–492

Muzammil SP, Varghese RM, Joseph J (2018) Numerical simulation of the response of geosynthetic encased stone columns under oil storage tank. Int J Geosynth Ground Eng 4:1–12

Xu Z, Zhang L, Zhou S (2021) Influence of encasement length and geosynthetic stiffness on the performance of stone column: 3D DEM-FDM coupled numerical investigation. Comput Geotech 132:103993

Gholaminejad A, Mahboubi A, Noorzad A (2020) Encased stone columns: coupled continuum—discrete modelling and observations. Geosynth Int 27(6):581–592

Download references

Author information

Authors and affiliations.

Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India

Shaid Yousuf & Narendra Kumar Samadhiya

You can also search for this author in PubMed   Google Scholar

Contributions

Shaid Yousuf performed the numerical modelling, analysed the data, and wrote the paper. N K Samadhiya reviewed and edited the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shaid Yousuf .

Ethics declarations

Conflict of interest.

The authors declare no competing financial or non-financial interests regarding the publication of this manuscript.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Yousuf, S., Samadhiya, N.K. Numerical Evaluation of Pile Length, Lateral Bulging and Encasement Length: A Comparative Study on Ordinary and Encased Granular Piles. Int. J. of Geosynth. and Ground Eng. 10 , 48 (2024). https://doi.org/10.1007/s40891-024-00556-0

Download citation

Received : 30 June 2023

Accepted : 24 April 2024

Published : 12 May 2024

DOI : https://doi.org/10.1007/s40891-024-00556-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Geosynthetic
• Optimum length
• Mohr–Coulomb failure criterion, Parametric analysis
• Multiple regression analysis
• Find a journal
• Publish with us
• Track your research