Mathematical Optimization in Transportation Engineering: 2nd Edition

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

Deadline for manuscript submissions: 20 December 2025 | Viewed by 1045

Special Issue Editors


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Guest Editor
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Interests: transportation system optimization; transport planning; traffic management and transport policy

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Guest Editor
Intelligent Transport Systems, Institute for Transport Studies (ITS), University of Leeds, Leeds, UK
Interests: modelling and assessment of the disruption of emergency evacuation and network resilience; evaluation of transport policy interventions and system dynamics; city mobility and ICT solutions
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E-Mail Website
Guest Editor
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Interests: high-speed railway transportation organization and optimization theory and method; urban rail transit operation management theory and application; public transportation organization and optimization theory; transportation operation management and information system

Special Issue Information

Dear Colleagues,

Transportation engineering typically involves the planning, design, operation, and management of transportation facilities, seeking to maximize efficiency, minimize costs, maximize reliability, or minimize environmental pollution. The most effective way to address these issues is to establish mathematical optimization models and obtain the best solution by solving the model. This involves the analysis of the problem, variables, objectives, and constraints, and requires the development of appropriate solution algorithms to solve the corresponding optimization problems. Therefore, this involves not only transportation engineering problems but also the development of optimization theory.

Mathematical optimization in transportation engineering typically involves the design optimization of various transportation facilities, such as roads, railways, and other networks; bus routes, intersections, and signal control; transportation stations; and parking facilities. It also involves residents’ travel behavior modeling, traffic demand analysis, passenger flow organization, etc., as well as traffic flow patterns under intelligent transportation, autonomous driving, and vehicle networking environments. New modes of transportation, such as shared transportation, mobility as a service, multimodal transport, etc., are also application fields of mathematical optimization in transportation engineering. In addition, it also involves the logistics field, such as the planning and scheduling of vehicle or drone transportation routes, drone monitoring path planning and scheduling, transportation route and facility allocation, operation management optimization in navigation, aviation, and other related fields.

The aim of this SI is to collect some new theoretical contributions and real-world applications with regard to the optimization problems in transportation engineering mentioned above. 

Prof. Dr. Qun Chen
Prof. Dr. Haibo Chen
Prof. Dr. Lianbo Deng
Guest Editors

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Keywords

  • transportation
  • planning
  • design
  • operation
  • transportation facilities
  • optimization
  • roads
  • railway
  • network
  • bus
  • intersection
  • signal control
  • station
  • parking facilities
  • travel behavior modeling
  • traffic demand analysis
  • passenger flow organization
  • traffic flow
  • intelligent transportation
  • autonomous driving
  • vehicle networking
  • shared transportation
  • mobility as a service
  • multimodal transport
  • logistics
  • planning
  • scheduling
  • path planning
  • facility allocation
  • navigation
  • aviation

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Published Papers (2 papers)

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Research

26 pages, 8828 KiB  
Article
Optimizing Scheduled Train Service for Seaport-Hinterland Corridors: A Time-Space-State Network Approach
by Yueyi Li and Xiaodong Zhang
Mathematics 2025, 13(8), 1302; https://doi.org/10.3390/math13081302 - 16 Apr 2025
Viewed by 185
Abstract
Effective cooperation between railways and seaports is crucial for enhancing the efficiency of seaport-hinterland corridors (SHC) . However, existing challenges stem from fragmented decision-making across seaports, rail operators, and inland cities, leading to asynchronous routing and scheduling, suboptimal service coverage, and delays. Addressing [...] Read more.
Effective cooperation between railways and seaports is crucial for enhancing the efficiency of seaport-hinterland corridors (SHC) . However, existing challenges stem from fragmented decision-making across seaports, rail operators, and inland cities, leading to asynchronous routing and scheduling, suboptimal service coverage, and delays. Addressing these issues requires a comprehensive approach to scheduled train service design from a network-based perspective. To tackle the challenges in SHCs, we propose a targeted networked solution that integrates multimodal coordination and resource optimization. The proposed framework is built upon a time-space-state network model, incorporating service selection, timing, and frequency decisions. Furthermore, an improved adaptive large neighborhood search (ALNS) algorithm is developed to enhance computational efficiency and solution quality. The proposed solution is applied to a representative land–sea transport corridor to assess its effectiveness. Compared to traditional operational strategies, our optimized approach yields a 7.6% reduction in transportation costs and a 56.6% decrease in average cargo collection time, highlighting the advantages of networked service coordination. The findings underscore the potential of network-based operational strategies in reducing costs and enhancing efficiency, particularly under unbalanced demand distributions. Additionally, effective demand management policies and targeted infrastructure capacity enhancements at bottleneck points may play a crucial role in practical implementations. Full article
(This article belongs to the Special Issue Mathematical Optimization in Transportation Engineering: 2nd Edition)
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14 pages, 712 KiB  
Article
Analysis of G-Transformation Modes for Building Neuro-like Parallel–Hierarchical Network Identification of Rail Surface Defects
by Vaidas Lukoševičius, Volodymyr Tverdomed, Leonid Tymchenko, Natalia Kokriatska, Yurii Didenko, Mariia Demchenko and Olena Oliynyk
Mathematics 2025, 13(6), 966; https://doi.org/10.3390/math13060966 - 14 Mar 2025
Viewed by 245
Abstract
This work presents the construction of a transformation for the identification of surface defects on rails, starting with the selection of elements from the matrix and the creation of different matrices. It further elaborates on the recursive formulation of the transformation and demonstrates [...] Read more.
This work presents the construction of a transformation for the identification of surface defects on rails, starting with the selection of elements from the matrix and the creation of different matrices. It further elaborates on the recursive formulation of the transformation and demonstrates that, regardless of the elements’ uniqueness, the sum of the transformed matrix remains equal to the sum of the original matrix. This study also addresses the handling of matrices with repeated elements and proves that the G-transformation preserves information, ensuring the integrity of data without any loss or redundancy. Full article
(This article belongs to the Special Issue Mathematical Optimization in Transportation Engineering: 2nd Edition)
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