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Mathematics
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Applied Mathematics and Optimization Methods in Transport Planning and Management
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Applied Mathematics and Optimization Methods in Transport Planning and Management

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 853

Special Issue Editors

Dr. Jie Liu

E-Mail Website
Guest Editor
1. Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
2. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Interests: transportation resilience; transportation reliability; transportation robustness; transportation management and optimization
Dr. Yue Xie

E-Mail Website
Guest Editor
Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
Interests: evolutionary computation; mathematical modeling; combinatorial optimization; intelligence traffic system
Dr. Xin Wu

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Villanova University, Villanova, PA, USA
Interests: transportation management and optimization; transportation network modeling; logistics system design; railway system management

Special Issue Information

Dear Colleagues,

With the integration of multi-modal transportation and the expansion of transport networks, modern transport systems are becoming increasingly complex, facing growing challenges in managing rising travel demand, alleviating congestion, optimizing operations, and responding to various disruptions. The complexity of modern transport systems necessitates the use of mathematical theories and optimization methods to support strategic planning, enhance operational efficiency, and improve decision-making processes. Mathematical methods provide powerful tools for modeling traffic dynamics, optimizing network performance, predicting mobility patterns, and assessing risks, enabling transport systems to function more effectively under dynamic and uncertain conditions.

This Special Issue aims to showcase cutting-edge research that applies mathematical theories and optimization techniques to transport planning and management. We invite original research articles, systematic reviews, and case studies covering different modes of transportation and various research scales. Topics of interest include, but are not limited to, the following: network design optimization, schedule optimization, ride-sharing and carpooling optimization, delivery optimization, and disaster response optimization.  

Dr. Jie Liu
Dr. Yue Xie
Dr. Xin Wu
Guest Editors

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 planning
  • transportation management
  • transportation optimization
  • algorithm design

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Published Papers (2 papers)

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Research

23 pages, 718 KiB  
Article
State-Aware Graph Dynamics for Urban Transport Systems with Topology-Based Rate Modulation
by Yiwei Shi, Chunyu Li, Wei Wang and Yaowen Hu
Mathematics 2025, 13(16), 2574; https://doi.org/10.3390/math13162574 - 12 Aug 2025
Viewed by 193
Abstract
We introduce a novel optimization method, the Bud Lifecycle Algorithm (BLA), and present a mathematical model for optimizing urban transportation systems, demonstrated through a Baltimore case study. Our approach centers on the Proximity Topology Attribute Model, which integrates topological graph properties with K-means [...] Read more.
We introduce a novel optimization method, the Bud Lifecycle Algorithm (BLA), and present a mathematical model for optimizing urban transportation systems, demonstrated through a Baltimore case study. Our approach centers on the Proximity Topology Attribute Model, which integrates topological graph properties with K-means clustering to partition city nodes and identify key activity areas via betweenness centrality. A simulated bridge collapse reveals significant impacts on insurance companies and transport users. To balance traffic efficiency with construction costs in public transport projects, we propose a multi-objective optimization model prioritizing transit hubs while minimizing expenses in congested zones. We introduce the Bud Lifecycle Algorithm (BLA) to enhance traditional Genetic Algorithm performance, achieving improvements in system coverage, cost-efficiency, and user satisfaction. Our findings suggest that expanding public transport networks and optimizing rail projects could substantially boost employment and tourism in West Baltimore. We propose the Smart Traffic Management System (STMS) and Community Traffic Safety Program (CTSP) to enhance traffic safety, reduce congestion, and improve residents’ quality of life. Full article
(This article belongs to the Special Issue Applied Mathematics and Optimization Methods in Transport Planning and Management)
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29 pages, 1289 KiB  
Article
An Analysis of Hybrid Management Strategies for Addressing Passenger Injuries and Equipment Failures in the Taipei Metro System: Enhancing Operational Quality and Resilience
by Sung-Neng Peng, Chien-Yi Huang, Hwa-Dong Liu and Ping-Jui Lin
Mathematics 2025, 13(15), 2470; https://doi.org/10.3390/math13152470 - 31 Jul 2025
Viewed by 413
Abstract
This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates [...] Read more.
This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article
(This article belongs to the Special Issue Applied Mathematics and Optimization Methods in Transport Planning and Management)
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