thesis upm plataforma

Login Account

Programa de Doctorado Transversal en Arquitectura y Urbanismo

Escuela técnica superior de arquitectura de madrid (etsam). universidad politécnica de madrid (upm), plataforma thesis.

En este curso 2021-2022 se pone en marcha la plataforma THESIS que, gracias al trabajo del VR de Estrategia y Transformación Digital y la colaboración del VR de Investigación, Innovación y Doctorado, permite la gestión integrada de doctorado. El acceso ya es posible para doctorandos y directores de tesis en el enlace https://www.upm.es/thesis/ .

La plataforma THESIS permite, de momento, los siguientes trámites:

· modificación de título,

· realización del informe anual (plan de investigación) y

· solicitud de prórroga

a los que en breve se irán sumando el resto de trámites y actividades del doctorado.

Se están preparando varias reuniones informativas para distintos colectivos (subdirectores de escuela, coordinadores de programas de doctorado, directores de tesis y estudiantes) para presentar la plataforma y revisar la forma de trabajo para estos trámites en marcha en estas semanas.

Leave a Reply Cancelar la respuesta

Tu dirección de correo electrónico no será publicada. Los campos obligatorios están marcados con *

Comentario *

Correo electrónico *

Guarda mi nombre, correo electrónico y web en este navegador para la próxima vez que comente.

• Resumen de privacidad
• Cookies estrictamente necesarias

Esta web utiliza cookies para que podamos ofrecerte la mejor experiencia de usuario posible. La información de las cookies se almacena en tu navegador y realiza funciones tales como reconocerte cuando vuelves a nuestra web o ayudar a nuestro equipo a comprender qué secciones de la web encuentras más interesantes y útiles.

Las cookies estrictamente necesarias tiene que activarse siempre para que podamos guardar tus preferencias de ajustes de cookies.

Si desactivas esta cookie no podremos guardar tus preferencias. Esto significa que cada vez que visites esta web tendrás que activar o desactivar las cookies de nuevo.

• Accesibilidad

Universidad Politécnica de Madrid

• Estudiantes
• / Estudios y Titulaciones
• / Estudios de Doctorado
• / Normas de redacción y deposito de la tesis
• Estudios Oficiales de Grado
• Programas Académicos con Recorridos Sucesivos (Grado + Máster Oficiales)
• Estudios Oficiales de Máster
• Estudios de Doctorado
• Estudios Propios de Grado
• Estudios Propios de Posgrado y Formación Continua
• Formación permanente
• Reconocimiento de Créditos
• Calendario Académico
• Carné Universitario
• Actividades Acreditables para Estudios de Grado
• Convenios con otras instituciones y universidades
• Devolución de Tasas
• 03. Trámites y solicitudes
• Plataforma de Tele-educación
• Punto de Inicio
• Puesta a Punto para Estudiantes
• OpenCourseWare
• Audiovisual
• Ayudas a alumnos de doble titulación
• Ayudas de viajes
• Bolsa de viaje
• Ayudas de viaje en Cooperación
• Ayudas del Consejo Social
• Ayudas para el viaje a Rusia dentro del proyecto "Maratón Estudiantil"
• Dobles titulaciones
• Programas Nacionales
• Programas Internacionales
• Centro de Lenguas UPM
• Coordinadores en los Centros
• Escuelas de Verano Internacionales
• Prácticas Académicas Universitarias
• Oportunidades de Empleo
• Orientación Laboral
• Revista TalentUPM. Feria de Empleo UPM
• Alojamiento para estudiantes
• Seguro de asistencia
• Guía para estudiantes extranjeros
• Manuales de prevención de riesgos
• 09. Delegación de alumnos
• Relación de Asociaciones de Estudiantes
• Cooperación para el Desarrollo
• Unidad de Atención a la Discapacidad
• Voluntariado
• 12. Competiciones de Estudiantes
• Normas específicas
• Seguro escolar
• Seguro Mayores de 28 años
• Seguro de Movilidad Nacional y Prácticas
• Seguro de Movilidad Internacional
• Seguro para alumnos de títulos propios
• Seguro de Responsabilidad Civil

También te puede interesar:

• Servicios Tecnológicos
• Biblioteca Universitaria
• Actividades Deportivas
• Políticas de Igualdad
• Defensor Universitario
• Actividades Culturales
• Acción Social
• Unidad de Accesibilidad y Atención a la Discapacidad

Normas de redacción y depósito de la tesis doctoral

Puedes acceder a la herramienta automatizada para la redacción de bibliografías .

PROCEDIMIENTOS DE DEPÓSITO DE LA MEMORIA DE TESIS

La solicitud de depósito de tesis y su documentación asociada deben realizarse desde la plataforma THESIS . Pincha aquí para consultar la normativa de doctorado y sus procedimientos.

PROCEDIMIENTO DE SUBSANACIÓN ERRORES TESIS DESPUÉS DE DEPOSITADA

La memoria adjunta a la solicitud de depósito de la tesis no podrá ser modificada una vez iniciado el trámite de depósito de la misma. Si, tras la solicitud de depósito por parte del doctorando/a se detectan errores en la memoria o hay errores en la portada, se podrán subsanar desde la  plataforma THESIS  antes de que se inicie el periodo de exposición pública. En el caso de la Fe de Erratas, deberá contar con el VºBº del director y habrá de ser aprobada por la CAPD.

PLANTILLAS DE MEMORIA DE TESIS

La memoria de tesis puede redactarse en español o en inglés. La cubierta y portada de la tesis deben utilizar el formato especificado en la plantilla de tesis UPM, que está disponible en:

• Plantilla en español ( Word ,  LATEX )
• Plantilla en inglés ( Word , LATEX )

NORMAS DE OBLIGADO CUMPLIMIENTO:

• Respetar escrupulosamente el modelo de plantilla
• Modificar únicamente los apartados marcados con para introducir los datos oportunos
• No incluir otros escudos
• Nombre del centro siempre en español ( ver Lista de PD )
• Nombre del doctorando completo, tal como aparece en su expediente

Primera página (Portada)

• Respetar escrupulosamente el modelo de plantilla 
• Puede incluir el escudo del centro (Escuela o Facultad), respetando el tamaño marcado en la plantilla
• Incluye el nombre del programa de doctorado (idéntico al indicado en esta Lista de PD )
• Incluye el nombre del director de tesis y del codirector si lo hubiese.

Segunda página

• Incluir datos del director de tesis: nombre, puesto e institución. Lo mismo para el codirector si lo hubiese.
• Los apartados “Revisores Externos”, “Tribunal de tesis” y “Fecha de defensa” se dejan sin completar.
• Si se ha recibido financiación de alguna convocatoria competitiva (FPU, Horizon Europe,...), debe indicarse al final de esta página (ver plantilla).
• Se debe incluir un resumen tanto en español como en inglés, independientemente del idioma de la tesis.
• Formato: máximo de 4000 caracteres, texto plano (sin símbolos)
• Estructura: es una presentación de la tesis y, por tanto, debe tener una estructura clara, incluyendo introducción (o motivación), objetivos, desarrollo y conclusiones.
• Soporte electrónico (PDF)
• Tamaño de página A4
• La cubierta y la primera página no se numeran.
• Para las páginas preliminares (desde la página 3 hasta la Introducción) se utiliza numeración romana centrada en el margen inferior.
• Para el texto (a partir de la Introducción) se utiliza numeración arábiga centrada en el margen inferior.

RECOMENDACIONES:

• Estructura del texto: Introducción – Estado de la cuestión – Material y métodos - Resultados – Discusión –  Conclusiones
• Bibliografía (publicaciones utilizadas en el desarrollo de la tesis)
• Anexos: Apéndice documental, en su caso
• Si se trata de una tesis por compendio de publicaciones, consultar las normas específicas.
• Secciones y subsecciones reseñadas con numeración decimal a continuación de cada capítulo
• Títulos de los capítulos en letras mayúsculas - Capítulos con numeración arábiga
• Bibliografía consignada por orden alfabético del apellido del autor, año de publicación, título completo de la publicación y título completo o abreviado de la revista, en este último caso, según la norma establecida por alguna base de datos de uso común. Se recomienda visitar el siguiente enlace .

• JRC Articles
• Proceedings
• Technical Reports
• Presentations
• Quesada (2023). Multivariate Time-Series Modelling and Forecasting with High-Order Dynamic Bayesian Networks Applied in Industrial Settings . Ph.D. in Computer Science. Technical University of Madrid
• E. Puerto-Santana (2023). Asymmetric Hidden Markov Models and Extensions Applied to Industry . Ph.D. in Computer Science. Technical University of Madrid
• Rodríguez Sánchez, Fernando (2021).  Multidimensional clustering with Bayesian networks. Tesis (Doctoral), E.T.S. de Ingenieros Informáticos (UPM). DOI
• Atienza González, David (2021).  Nonparametric Models and Bayesian Networks. Applications to Anomaly Detection. Tesis (Doctoral), E.T.S. de Ingenieros Informáticos (UPM). DOI
• Córdoba Sánchez, Irene (2020).  Unifying methodologies for graphical models with Gaussian parametrization. Tesis (Doctoral), E.T.S. de Ingenieros Informáticos (UPM). DOI
• Benjumeda, M.,  Learning Tractable Bayesian Networks,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2019
• Diaz-Rozo, J.,  Clustering probabilístico dinámico para la búsqueda de patrones de degradación de elementos de máquina en el ámbito de la Industria 4.0,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2019.
• Fernandez-Gonzalez, P.,  Developments in probabilistic graphical models, circular distributions and theory of random forests with applications in neuroscience , , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2019.
• Luengo-Sanchez, S.,  Clustering based on Bayesian networks with Gaussian and angular predictors : applications in Neuroscience,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2019.
• Leguey, I.,  Directional-linear Bayesian networks and applications in neuroscience,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2018.
• Mihaljevic, B.,  Contributions to Bayesian network classifiers and interneuron classification,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2018.
• Varando, G.,  Theoretical studies on Bayesian network classifiers,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2018.
• Anton-Sanchez, L..,  “Statistical and optimization methods for spatial data analysis applied to neuroscience”,  (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid., 2017.
• Ibáñez, A.,  Machine Learning in Scientometrics,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2015.
• Borchani, H.,  Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data , , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2013.
• Karshenas, H.,  Regularized Model Learning in EDAs for Continuous and Multi-Objective Optimization,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2013.
• Lopez-Cruz, P. L.,  Contributions to Bayesian network learning with applications to neuroscience,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2013.
• Guerra, L.,  Semi-supervised subspace clustering and applications to neuroscience,  , (supervised by C. Bielza and V. Robles), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2012.
• Vidaurre, D.,  Regularization for sparsity in statistical analysis and machine learning , , (supervised by Pedro Larrañaga and Concha Bielza), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2012.
• Correa, M.,  Inteligencia Artificial para Predicción y Control del Acabado Superficial en Procesos,  , (supervised by Concha Bielza), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2010.
• Miquelez, T.,  Avances en Algoritmos de Estimación de Distribuciones. Alternativas en el Aprendizaje y Representación de Problemas,  , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2010.
• Pérez, A.,  Supervised Classification in Continuous Domains with Bayesian Networks,  , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2010.
• Armañanzas, R.,  Consensus Policies to Solve Bioinformatic Problems Through Bayesian Network Classifiers and Estimation of Distribution Algorithms,  , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2009.
• Morales, D.,  Clasificadores Bayesianos en la Selección Embrionaria en Tratamientos de Reproducción Asistida , , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2009.
• Calvo, B.,  Positive Unlabelled Learning with Applications in Computational Biology , , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2008.
• Santafé, G.,  Advances on Supervised and Unsupervised Learning of Bayesian Networks Models. Applications to Population Genetics,  , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2008.
• Romero, T., Algoritmos de Estimación de Distribuciones Aplicados a Problemas Combinatorios en Modelos Gráficos Probabilísticos, , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2007.
• Fernandez del Pozo, J. A.,  Listas KBM2L para la Síntesis de Conocimiento en Sistemas de Ayuda a la Decisión,  , (supervised by Concha Bielza), Facultad de Informática, Politécnica de Madrid, 2006.
• González, C.,  Contributions on Theoretical Aspects of Estimation of Distribution Algorithms,  , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2006.
• Santana, R.,  Advances in Probabilistic Graphical Models for Optimization and Learning. Applications in Protein Modelling , , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasc, 2006.
• Blanco, R., Learning Bayesian Networks from Data with Factorization and Classification Purposes. Applications in Biomedicine, , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2005.
• Merino, M., Predicción de Mortalidad Precoz tras Implantación Percutánea Intrahepática en Pacientes Cirróticos. Aplicación de Métodos de Clasificación Supervisada, , (supervised by Pedro Larrañaga), Departamento de Medicina Interna, Universidad de Navarra, 2004.
• Robles, V.,  Clasificación Supervisada basada en Redes Bayesianas. Aplicación en Biología Computacional,  , (supervised by Pedro Larrañaga), Departamento de Arquitectura y Tecnología de Sistemas Informáticos, Universidad Politécnica de Madrid, 2003.
• Bengoetxea, E.,  Inexact Graph Matching Using Estimation of Distribution Algorithms,  , (supervised by Pedro Larrañaga), Département Traitement du Signal et des Images, Ecole Nationale Supérieure de Télécomunications, 2002.
• Gómez, M., IctNeo: Un Sistema de Ayuda a la Decisión para el Tratamiento de la Ictericia, , (supervised by Concha Bielza), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2002.
• Inza, I., Advances in Supervised Classification Based on Probabilistic Graphical Models, , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2002.
• Peña, J. M., On Unsupervised Learning of Bayesian Networks and Conditional Gaussian Networks, , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2001.
• Sierra, B., Aportaciones Metodológicas a la Clasificación Supervisada, , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2000.
• Lozano, J. A., Algoritmos Genéticos Aplicados a la Clasificación no Supervisada, , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 1998.

• PROSPECTIVE STUDENTS
• NEW STUDENTS
• CURRENT STUDENTS
• PROGRAMME OF STUDY
• FINANCIAL ASSISTANCE & SCHOLARSHIPS
• PUTRA SARJANA
• OTHER INFORMATION

https://ttsreader.com/ https://www.naturalreaders.com/online/

A.  THESIS SUBMISSION REQUIREMENTS FOR EXAMINATION (GS-15a/GS-15b)

Abstract – • between 300 – 500 words. • written in both English and Bahasa Melayu. • include Keywords (not more than 5 keywords) and SGDs (not more than 3 SDG).

The margins of pages are as follows: top : 25 mm right : 25 mm bottom : 25 mm left : 40 mm, spacing – • double spacing for text. • single spacing for footnotes, long quotations, references or bibliography, multi-line captions, appendices (such as questionnaires, letter) and headings or subheadings., white simile a4 size (210mm x 297mm) paper (80g) or paper of equivalent quality should be used. students must include an extra blank sheet for the front and back of the thesis. photocopies of the thesis must be on similar quality paper (if a hardcopy of thesis required by the examiner)., the thesis, along with the required documents, should be submitted via email to [email protected] ..

• Thesis A4 Format (Template)
• Please refer to the Guide to Thesis Preparation for other details format of the thesis. 
• You are advised not to use software for graphics and diagrams which may complicate conversion and documents transmission.

Updated:: 03/04/2024 [aslamiah]

Universiti Putra Malaysia, 43400 UPM Serdang ,Selangor

Contact List by Unit Staff and Services

Digital Technologies of the Project “Moscow ‘Smart City—2030’”: The Transport Sector

• First Online: 17 May 2023

Cite this chapter

• Aleksandr A. Matenkov   ORCID: orcid.org/0000-0003-3831-1245 3 ,
• Ruslan I. Grin   ORCID: orcid.org/0000-0003-4343-9219 3 ,
• Markha K. Muzaeva   ORCID: orcid.org/0000-0003-0843-5685 3 &
• Dali A. Tsuraeva   ORCID: orcid.org/0000-0002-2445-6729 3  

Part of the book series: Environmental Footprints and Eco-design of Products and Processes ((EFEPP))

440 Accesses

The research deals with the priority areas of digitalization in the transport sector in interpreting the strategy “Moscow ‘Smart City—2030’.” The research aims to study the priority areas of digitalization of transport flows of the metropolis and the potential impact of digitalization on the functioning of the territory. By applying the methods of content analysis and the regulatory-legal method in the research, the authors assessed the position of the city authorities on the most sought-after areas of innovation in the transport sector and determined the composition of socio-economic benefits of digitalization of the transport sector. The analysis of statistical indicators of the development of the transport sector of the Moscow urban agglomeration has confirmed the growing need to improve the efficiency of transport infrastructure in the broad sense, including an increase in the level of connectivity of the city districts and the level of sustainability of the transport system. The results show certain disproportions between the priority areas of transport development and the actual needs of the urban infrastructure, as well as the presence of significant legal constraints in implementing uncrewed transport concepts. It is demonstrated that there is a certain consensus between the municipal authorities and the population on the issue of assigning the transport sector among the priorities for implementing digital technology. The specifics of the metropolitan area (high concentration of capital and innovation activity) allow for considering Moscow as a model example of the introduction of innovative technologies. In this regard, it is necessary to optimize the legal restrictions on the introduction of innovations in the field of transport (on the model of a legal sandbox, Regulatory Sandbox).

Download chapter PDF

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

• Digitalization
• Digital technologies
• Innovations

JEL Classification

1 introduction.

The impact of the development of Russian megacities on the state of the transport sector is manifested in the overall growth of the load on the infrastructure, along with the increasing need to accelerate the movement of people (including—as active participants in the labor market and consumers) and goods. For the Russian Federation, the problems of improving the efficiency of transport infrastructure management in large metropolitan areas play a particularly important role in the context of imbalances in territorial development and related consequences for the national economy. At the turn of 2020, Russia has 20 large urban agglomerations (according to the Government of the Russian Federation, about 40 urban agglomerations [ 1 ]), which account for over 30% of the population and about 40% of GDP [ 2 ]. In general, the trend of localization of the economically active population and capital in large megacities is a worldwide trend. PricewaterhouseCoopers estimates that by 2030, 24% of the world’s population will live in agglomerations with a population of more than 1.5 million people. Additionally, PricewaterhouseCoopers states that the contribution of agglomerations to global GDP will increase from 38% in the late 2010s to 43% in 2030 (a quarter of the world’s population is predicted to live in cities by 2030) [ 3 ].

The Moscow agglomeration is the largest Russian agglomeration. It unites the city of Moscow and the surrounding territories. It is highly probable that in the foreseeable future, Moscow and the Moscow agglomeration will continue playing the role of a center of attraction of resources of all forms with the corresponding consequences in the form of increased load on all elements of infrastructure, logistical, and operational risks. This argument logically leads to the recognition of the importance of theoretical and methodological issues of infrastructure development of agglomerations with a focus on improving their efficiency and carrying capacity. The introduction of digital technologies in the context of the project “Moscow ‘Smart City—2030’” implemented by the Moscow authorities is considered a tool to solve this problem.

2 Methodology

To achieve the research goal, the work with sources and literature relied on general scientific methods of analysis and synthesis. Given that the practice of using information technology in the field of transport is studied based on program and strategic documents of the federal level and the level of the subject of the federation, the methods of legal analysis were in demand. The study of the practice of using information technologies in the sphere of transport in Moscow is based on the application of methods of content analysis and historical analysis to analytical materials and statistical collections.

The Moscow agglomeration is among the largest in the world. Using the normative-legal approach to considering the essence of agglomerations, we will consider them as “a set of compactly located settlements and territories between them, connected with the joint use of infrastructure facilities and united by intensive economic, including labor, and social ties” [ 1 ]. Attempts are made (e.g., within the framework of the general plans of development of Moscow and the Moscow Region) to indirectly influence the dynamics of migration inflow to Moscow agglomeration (“pull away” part of the population). Nevertheless, as the statistics show, these attempts are not successful. The logical consequence of placing Moscow among the largest on a national and global scale is the high social, administrative, and financial potential, adjacent to the excessive load on the transport system. In a broad sense, infrastructure is understood as a set of certain institutions (organizations), norms and rules by which they function, and technical systems that support their activities. The purpose of infrastructure is to ensure the functioning of the agglomeration in a variety of areas of this functioning.

A characteristic feature of agglomerations as areas of population concentration is a progressive increase in the load on the elements of infrastructure: social, transport, household, and housing. This increases logistics costs and operational risks and requires improving the quality of management and design of transport and urban infrastructure using digital technology [ 4 ].

According to the estimates of international organizations, relatively recently, Moscow was among the world’s top five megacities. Moscow was one of the world’s five largest megacities with a high load on the transport system [ 5 ]. At the turn of the 2020s, the city managed to improve the efficiency of the transport infrastructure significantly. A significant role in this improvement was played by the consistent actions of the city authorities to integrate information technology into the transport infrastructure. The current stage of development of these efforts is included in the priorities of the project “Moscow ‘Smart City—2030’”.

First, it is necessary to illustrate the context of the practice of implementing information technology in the transport sector in Moscow. As the political, administrative, and economic center of the Russian Federation, Moscow is one of the relatively few constituent entities of the federation capable of covering its expenditures with budget revenues (with revenues exceeding 3 trillion rubles in 10 months of 2022). Moscow is the location of the head offices of major companies (including innovative and communications industry companies) and the attraction region for significant flows of incoming migration. Moscow is also characterized by a relatively high level of development of information and communications infrastructure. A 2021 survey conducted among 13.5 thousand respondents in a number of European countries, including the Russian Federation, showed a high level of digital technology penetration in everyday life (Cisco Broadband Index Survey) [ 6 ].

Until 2010, the transport situation in Moscow was close to critical: the road network had reached its maximum capacity, and Moscow had one of the worst traffic situations in the world. That is why, in 2011, the Government of Moscow City and leading Russian and international experts worked out the State Program for Moscow Transport Development until 2020. The plan focuses on analyzing a large amount of commuter data to reduce the road load through a strategic approach to modernization and new construction, as well as the launch of the Intelligent Transport System (ITS). Subsequently, program activities in the field of transport development were integrated into the project “Moscow ‘Smart City—2030” [ 5 ].

High budget possibilities allow the city authorities to develop the transport network intensively, but the number of cars registered in the Moscow transport hub is outstripping even the record pace of road construction [ 5 ]. There remains an imbalance between the residential and working areas in terms of load and requirements to transport infrastructure: 59% of jobs are located within the Third Ring Road, but only 9% of the population lives there. There are about 3.7 million cars daily on Moscow roads; in conjunction with the Moscow region, this figure reaches 8.4 million cars, creating a huge load on the street and road network [ 7 ]. That is why finding and implementing innovative solutions for traffic management that meet the growing mobility needs of Muscovites and visitors to the capital remains one of the key objectives of the development of urban transport infrastructure.

Already in the 2010s, Moscow made significant progress in introducing digital technologies in the field of transport. The Intelligent Transport System (ITS) has been in operation in Moscow since 2011. Initially, it covered 30% of the city’s territory, reaching 100% by 2017 [ 8 ]. ITS is a comprehensive monitoring system for managing traffic and public transport. In 2013, the Moscow Traffic Management Center launched the Control Center, which analyzes data from the equipment installed throughout the city—speed sensors, adaptive traffic lights and traffic safety cameras, monitored surveillance cameras, and GPS/GLONASS sensors on public transport [ 9 ]. Thus, we can judge about the formed complex of hardware and software integrated into the transport system of the city. The intelligent transport system of Moscow tracks 10,000 ground vehicles, more than 72,000 cabs, and 11,000 cars in the car-sharing network [ 8 ]. The Traffic Control Center is the largest in Europe. Every day, the Traffic Management Center receives more than 350 million packets of data from various locations, including the following:

80 million trips;

45 million speed measurements from sensors;

More than 60 million records of vehicle telematics data in the Regional Navigation Information System (RNIS).

The results of the introduction of information technology in the sphere of transport in Moscow indicate objectively high social and economic usefulness, including the following:

The average speed of private transport within the transport infrastructure of Moscow will increase in 2019–2020 to the level of 2010 by 20%;

Punctuality and reliability of ground transportation services using dedicated infrastructure reached 97% in 2019–2020 (compared to 76% in 2010);

42% reduction in traffic fatalities (down to 2.9 deaths per 100,000 residents) compared to 2010;

20% increase in average speed from 2010 to 2019 [ 5 ].

Reducing the number of traffic accidents by more than four times in 2019 compared to 2010;

Moscow is at the forefront of change, introducing the most advanced technology and the best national and international innovations. The introduction of the intelligent transport system has led to a seismic change in the traffic situation thanks to smart traffic lights, a network of cameras and sensors that analyze and regulate traffic flows, and other IT solutions that often remain hidden from the view of Moscow residents. With the development of the Internet of Things and computer modeling, it became possible to create a digital model of any physical object. A digital twin of Moscow is now being developed. A digital twin is a prototype of a real city, by which one can analyze the real situation on the roads, providing a reaction to possible changes and external influences [ 10 ]. This is an accurate reflection of the city in the digital realm, with information from various sensors, monitoring systems, and resource meters. A dynamic traffic model is already working in Moscow, enabling real-time assessment of the traffic situation, making short-term forecasts, and informing the residents 24/7. Muscovites receive targeted SMS and push notifications to their smartphones about changes in the operation of public transport and traffic situation. The information is based on a specific person’s transportation behavior, profile, and situational triggers. Currently, more than 4.5 million Moscow residents have received the information.

One of the distinctive features of Moscow as a territory of the implementation of information technologies in the sphere of transport is also the achievement of consensus between the city authorities and the population on the issue of digitalization. As follows from the results of the survey of Moscow respondents on the priority areas of digitalization within the framework of the smart city system, transport is among the five areas in which the population is already actively using the achievements of digitalization (Fig. 1 ).

Source Calculated and built by the authors

The results of the survey of Moscow respondents on the priority areas of digitalization within the smart city system.

In the second half of the 2010s, the infrastructure for implementing information technology in the urban transportation system expanded. In 2017, the Transportation Security Management Center was opened. It receives data from all CCTV cameras in the metro and has access to cameras in the Moscow Metro. Currently, more than 7700 security officers are on duty at stations and metro entrances. There are emergency call points at all stations; security posts with specialized equipment to detect prohibited items and substances are installed at subway entrances. The comprehensive approach applied in 2017 made it possible to reduce the number of crimes committed in the subway by 35% compared to the previous year and the number of administrative offenses by 21% [ 8 ].

One of the integrated technological solutions in the transport sector in Moscow is the concept of Mobility as a Service or Vehicles as a Service (MaaS) [ 11 ]. In its essence, this concept means the abandonment of personal transport in favor of public or rental vehicles. Cabs and car-sharing platforms like Yandex.Drive, Citymobil, Delimobil, BelkaCar, and others, as well as electric scooter rental companies Urent, Whoosh, and Samokat Sharing, form the infrastructure support for this concept based on their own technological solutions. A further logical step for the city authorities was to create a unified mobile application based on the MaaS concept, uniting all types of public transport and providing the opportunity to create a multimodal route. All operators of cabs, car-sharing services, bicycle rentals, and scooters were invited to join the platform. In 2021, the platform “Moscow Transport” was launched, which partially provides access to the infrastructure based on MaaS. This application allows users to lay out a convenient route, pay once, choose a suitable mode of transport, and immediately get all accompanying information about the trip, restrictions, closures, and tariffs of different operators [ 1 ].

The development of uncrewed transport is among the ambitious tasks of developing Moscow’s transport infrastructure. As part of the calculation of the Autonomous Vehicles Readiness Index (AVRI), KPMG surveys of experts in the Russian Federation on the perception of uncrewed technologies put the country in fourth place after India, Mexico, and the United Arab Emirates [ 12 ]. The Department of Information Technology of Moscow assessed public opinion, including the issues of uncrewed mobility, as part of the evaluation of the prospects of the strategic plan for the megalopolis “Concept of Moscow 2030.” The results of the survey of more than five thousand Moscow respondents aged 18–65 years showed that the city population, in general, is actively using innovative technologies in their daily lives. Simultaneously, 37% of respondents indicated that they would like to see uncrewed transport in the digital city of the future [ 11 ].

One of the few studies on the problems of smart cities and the development of uncrewed mobility in the Russian Federation is the work of E. N. Yadova and P. A. Levich, which reflects the results of a questionnaire survey of 2314 respondents by spontaneous sampling [ 9 ]. The study showed results that are natural for the sample: 89.4% of respondents were positive or rather positive about the prospects of introducing uncrewed cars into everyday life, and 78.9% showed a willingness to purchase or use such technology.

The results obtained by the researchers allow for illustrating one of the critical dependencies for promoting the smart city concept. It consists of the fact that respondents tend to give a more positive assessment of the implemented technologies in the presence of evidence-based benefits from the introduction of innovations. A controversial characteristic from the point of view of sample representativeness is the research of E. N. Yadova and P. A. Levich, which used the method of questioning by placing questionnaires on the Internet. It is apparent that respondents with access to network technologies (due to geographical, social, or economic factors) initially demonstrate a higher predisposition to assess the smart city concept positively. This indicates a higher quality of human capital, skills, and willingness to use digital capital in the population of the most urbanized regions of the country [ 13 ].

One of the significant limitations in the further development of information technology in the smart city project of Moscow is the ability of the municipal authorities to ensure the maintenance and further development of the infrastructure in the context of high dependence on imports of high-tech equipment and the imposed sanctions restrictions. As follows from the reporting materials of the Department of Transport of Moscow, as of 2020–2021, the functioning of the infrastructure relied on the following elements:

180,000 units of surveillance cameras;

2960 units of recording equipment;

More than 40,000 units of traffic control devices;

3900 units of road transport scanners [ 5 ].

On the scale of the software and hardware complex (considering the need to maintain it in working order, modernization, and renewal), we can imagine the challenges facing the city authorities in terms of ensuring the infrastructure’s functioning [ 14 ]. The level of personnel training with regard to the requirements of the digital economy and the lack of investment in the knowledge economy are considerable technological challenges, the overcoming of which will help remove the constraints on the implementation of these projects [ 15 ].

4 Conclusion

In the context of the arguments of the growing load on the transport infrastructure of Moscow, the priorities selected by the city authorities for the introduction of information technology in the transport sector seem relevant and justified. The promotion of uncrewed transport, intelligent transport system, and transport sharing are positioned by the municipal authorities as the key priorities for developing information technology in the transport sector. In a broader format, the goals of introducing digital technologies in the transport sector of Moscow are also aimed at reducing the need for physical transport channels (digital technologies as a tool for equating virtual presence with real presence, promoting logistics services and services, and abandoning the use of personal transport) and achieving the goals of improving the quality of life and protecting the environment. Moscow has a relatively favorable legal and institutional environment for implementing information technologies in the transport sector: a relatively high standard of living for the population, access to capital, and the ability of the city authorities to promptly make changes to the legal environment and finance capital-intensive projects.

5 Recommendations

The current development of the transport sector in Moscow is characterized by a high level of penetration of information technology, with the exception of uncrewed transport. Their further penetration and the ability to achieve the goals set out in Strategy “Moscow—2030” largely depend on the state of the regulatory environment (restrictions on uncrewed transport), the dynamics of investment activity, the speed of social change, and the availability of technology (digitalization of the transport sphere requires appropriate infrastructure development in conditions of high dependence on imports of knowledge-intensive products). As tools to remove these constraints, it is advisable to increase the mobility of city authorities in the field of legal regulation of innovations in the transport sector (on the model of the legal sandbox, Regulatory Sandbox), strengthen links of production and innovation centers (based on networking principles, active use of technology parks and business incubators), and create conditions for the development of public–private partnerships, including in the field of digital education.

Government of the Russian Federation (2022) Decree “On approval of the strategy of spatial development of the Russian Federation until 2025” (13 Feb 2019 No. 207-r, as amended on 30 Sep 2022). Moscow, Russia. Retrieved from https://docs.cntd.ru/document/552378463 . Accessed 10 Dec 2022

Polidi T (2017) Liberation of cities: How agglomerations will help Russian economy. RBK Daily. Retrieved from https://www.rbc.ru/opinions/economics/11/10/2017/59dde2ce9a79475a5f5e5df5 . Accessed 10 Dec 2022

Demidova A, Gubernatorov E (2017) A quarter of the world’s population got predicted life in giant cities by 2030. RBK Daily. Retrieved from https://www.rbc.ru/business/06/07/2017/595df2c19a794776e863d1b3 . Accessed 10 Dec 2022

Ivanitskaya NV, Baybulov AK, Safronchuk MV (2020) Modeling of the stress-strain state of a transport tunnel under load as a measure to reduce operational risks to transportation facilities. J Phys: Conf Ser 1703(1):012024. https://doi.org/10.1088/1742-6596/1703/1/012024

Article   Google Scholar  

Ministry of Transport of the Russian Federation (2020) Building a transport system of the future: the traffic control center’s performance report 2020. Retrieved from https://www.polisnetwork.eu/wp-content/uploads/2021/02/MTCC_EN.pdf . Accessed 10 Dec 2022

Bakhur V (2021) Cisco broadband index survey: Russians consider Internet access no less important than utilities. CNews. Retrieved from https://www.cnews.ru/news/line/2021-07-19_issledovanie_cisco_broadband_index . Accessed 12 Dec 2022

Autonews (2021) Authorities named the total number of cars in Moscow. Retrieved from https://www.autonews.ru/news/61c853cb9a794703b66ac3d4 . Accessed 10 Dec 2022

Department of Transport of Moscow (2017) Digitalization of Moscow transport: department of transport of Moscow. Retrieved from https://report2010-2017.transport.mos.ru/pdf/ar/en/mega-projects_digitalization.pdf . Accessed 10 Dec 2022

Yadova EN, Levich PA (2020) Analysis of preparedness to the modern (or up to date) technologies in conceptual frame of STS and RRI. Technologos 2:25–41. https://doi.org/10.15593/perm.kipf/2020.2.03

RAI Amsterdam (2021) Three smart cities in traffic management: Perth, Moscow, Mexico City. Retrieved from https://www.intertraffic.com/news/traffic-management/three-smart-cities-in-traffic-management-perth-mexico-city-moscow/ . Accessed 10 Dec 2022

Department of Information Technology of Moscow (2018) Concept of Moscow 2030. Retrieved from https://2030.mos.ru/netcat_files/userfiles/documents_2030/opros.pdf . Accessed 10 Dec 2022

KPMG (2020) Autonomous vehicles readiness index. Retrieved from https://home.kpmg/xx/en/home/insights/2020/06/autonomous-vehicles-readiness-index.html . Accessed 12 Dec 2022

Safronchuk MV, Sergeeva MV (2021) The concept of economic growth through digital economy perspective. In: Popkova EG, Sergi BS (eds) Modern global economic system: evolutionary development vs. revolutionary leap. Springer, Cham, Switzerland, pp 1264–1271. https://doi.org/10.1007/978-3-030-69415-9_138

Ivanov OV, Shamanina EA (2021) PPP as a tool to achieve sustainable development goals and implement the concept of “Quality infrastructure investments”. In: Zavyalova EB, Popkova EG (eds) Industry 4.0: exploring the consequences of climate change. Palgrave Macmillan, Cham, Switzerland, pp 309–322. https://doi.org/10.1007/978-3-030-75405-1_28

Safronchuk MV, Ivanitskaya NV, Baibulov AK (2022) Global labor market and challenges of digitalization. In: Popkova EG (eds) Imitation market modeling in digital economy: game theoretic approaches. Springer, Cham, Switzerland, pp 142–150. https://doi.org/10.1007/978-3-030-93244-2_17

Download references

Author information

Authors and affiliations.

MGIMO University, Moscow, Russia

Aleksandr A. Matenkov, Ruslan I. Grin, Markha K. Muzaeva & Dali A. Tsuraeva

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Aleksandr A. Matenkov .

Editor information

Editors and affiliations.

RUDN University, Moscow, Russia

Elena G. Popkova

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Matenkov, A.A., Grin, R.I., Muzaeva, M.K., Tsuraeva, D.A. (2023). Digital Technologies of the Project “Moscow ‘Smart City—2030’”: The Transport Sector. In: Popkova, E.G. (eds) Smart Green Innovations in Industry 4.0 for Climate Change Risk Management. Environmental Footprints and Eco-design of Products and Processes. Springer, Cham. https://doi.org/10.1007/978-3-031-28457-1_45

Download citation

DOI : https://doi.org/10.1007/978-3-031-28457-1_45

Published : 17 May 2023

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-28456-4

Online ISBN : 978-3-031-28457-1

eBook Packages : Engineering Engineering (R0)

Share this chapter

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

• Publish with us

Policies and ethics

• Find a journal
• Track your research