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Mathematics
Special Issues
Modeling and Optimization in Urban Transport and Ecology
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Modeling and Optimization in Urban Transport and Ecology

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 3796

Special Issue Editor

Dr. Vladimir Shepelev

E-Mail Website
Guest Editor
Department of Automobile Transport, South Ural State University, 454080 Chelyabinsk, Russia
Interests: machine learning; transport planning; data mining; artificial intelligence in transportation and logistics; Internet of Things (IoT) in transportation and logistics; blockchain technology in transportation and logistics

Special Issue Information

Dear Colleagues,

This Special Issue publishes articles on the results of mathematical modeling to solve transport and environmental urban problems. The modeling and optimization of transport system parameters is a tool for efficient transport planning, dynamic management, and finding optimal solutions to overcome the challenges dictated by the dynamic development of transport systems. The development of innovative technologies in informatics and telematics involves the in-depth use of mathematical applications and software solutions in a non-mathematical context based on both traditional methods and new knowledge in artificial intelligence. The development of technological innovations in sustainable transport systems promotes the relevance of the following areas of work: improvement of technical, technological, and information methods to integrate the transport infrastructure and vehicles; improvement of theoretical and methodological approaches for the description of transport macro models of cities, taking into account transport demand and supply; development of decision-making systems based on IoT‒artificial intelligence technologies and algorithms; and mathematical description of adaptive technical systems for traffic management, modeling, and advancement of technical systems and processes. Special attention is paid to research in mathematical modeling and software implementation to assess and forecast the distribution of vehicle-related pollutant concentrations, taking into account urban development.

We need papers covering new ideas, results, and innovative and state-of-the-art methodologies and algorithms that could be applied to real transport and environmental problems. We also encourage the submission of articles that include modules and computational packages to reproduce and implement the results. Research papers, review articles, and brief communications are invited.

Potential topics include, but are not limited to:

  • Mathematical modeling for transport and environmental applications using neural network solutions;
  • Development of new theoretical models using the methods to integrate Big Data and Data Science technologies for the transition to sustainable high-tech development of transport systems;
  • Modeling and optimization of models for assessing and forecasting the distribution of vehicle-related emissions;
  • Modeling and improvement of the mathematical description of traffic flow parameters and population mobility indicators;
  • IoT-based AI architecture design for collecting and interpreting Big Data;
  • Analytical and numerical methods for solving transport and environmental problems;
  • Modeling and optimization of vehicles‒infrastructure (V2I) and environment (V2E) interaction algorithms.

Dr. Vladimir Shepelev
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • transportation
  • supply chain management
  • logistics
  • transportation network design
  • transportation planning
  • decision making
  • transportation optimization
  • mathematical modeling
  • routing and scheduling
  • transportation and logistics
  • artificial intelligence in transportation and logistics
  • machine learning in transportation and logistics
  • freight management
  • inventory management
  • supply chain optimization
  • predictive analytics in transportation and logistics
  • blockchain technology in transportation and logistics
  • big data analytics in transportation and logistics
  • Internet of Things (IoT) in transportation and logistics
  • sustainable transportation and logistics

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

30 pages, 11545 KiB  
Article
Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions
by Vladimir Shepelev, Aleksandr Glushkov, Ivan Slobodin and Mohammed Balfaqih
Mathematics 2023, 11(16), 3591; https://doi.org/10.3390/math11163591 - 19 Aug 2023
Cited by 7 | Viewed by 1767
Abstract
This paper proposes a new approach to assessing the impact of changes in the traffic flow on pollutant emissions and the traffic capacity of signal-controlled intersections. We present an intelligent vision system tailored to monitor the traffic behavior at signal-controlled intersections in urban [...] Read more.
This paper proposes a new approach to assessing the impact of changes in the traffic flow on pollutant emissions and the traffic capacity of signal-controlled intersections. We present an intelligent vision system tailored to monitor the traffic behavior at signal-controlled intersections in urban areas. Traffic cameras are used to collect real-time vehicle traffic data. Our system provides valuable insight into the relationship between traffic flows, emissions, and intersection capacity. This study shows how changes in the traffic composition reduce the traffic capacity of intersections and increase emissions, especially those involving fine dust particles. Using the combination of fuzzy logic methods and Gaussian spline distribution functions, we demonstrate the variability of these relationships and highlight the need to further study compromises between mobility and air quality. Ultimately, our results offer promising opportunities for the development of intelligent traffic management systems aimed at balancing the demands of urban mobility while minimizing environmental impact. This study demonstrates the importance of taking into account the correlation between the change in the composition of traffic queues due to a random change in the traffic flow and its impact on emissions and the traffic capacity of intersections. This study found that the presence of various groups of vehicles and their position in the queue can reduce the traffic capacity by up to 70% and increase the growth of harmful emissions by 14 fold. Full article
(This article belongs to the Special Issue Modeling and Optimization in Urban Transport and Ecology)
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16 pages, 1245 KiB  
Article
Analytical Model for Information Flow Management in Intelligent Transport Systems
by Alexey Terentyev, Alexey Marusin, Sergey Evtyukov, Aleksandr Marusin, Anastasia Shevtsova and Vladimir Zelenov
Mathematics 2023, 11(15), 3371; https://doi.org/10.3390/math11153371 - 1 Aug 2023
Cited by 1 | Viewed by 1234
Abstract
The performance of this study involves the use of the zoning method based on the principle of the hierarchical relationship between probabilities. This paper proposes an analytical model allowing for the design of information and analysis platforms in intelligent transport systems. The proposed [...] Read more.
The performance of this study involves the use of the zoning method based on the principle of the hierarchical relationship between probabilities. This paper proposes an analytical model allowing for the design of information and analysis platforms in intelligent transport systems. The proposed model uses a synthesis of methods for managing complex systems’ structural dynamics and solves the problem of achieving the optimal balance between the information situations existing for the object and the subject under analysis. A series of principles are formulated that govern the mathematical modeling of information and analysis platforms. Specifically, these include the use of an object-oriented approach to forming the information space of possible decisions and the division into levels and subsystems based on the principles of technology homogeneity and information state heterogeneity. Using the proposed approach, an information and analysis platform is developed for sustainable transportation system management, that allows for the objective, multivariate forecasting-based record of changes in the system’s variables over time for a particular process, and where decision-making simulation models can be adjusted in relation to a particular process based on an information situation existing for a particular process within a complex transport system. The study demonstrates a mathematical model that solves the optimal balance problem in organizationally and technically complex management systems and is based on vector optimization techniques for the most optimal decision-making management. The analysis involves classical mathematical functions with an unlimited number of variables including traffic volume, cargo turnover, safety status, environmental performance, and related variables associated with the movement of objects within a transport network. The study has produced a routing protocol prescribing the optimal vehicle trajectories within an organizationally and technically complex system exposed to a substantial number of external factors of uncertain nature. Full article
(This article belongs to the Special Issue Modeling and Optimization in Urban Transport and Ecology)
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