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Innovative and Sustainable Planning, Control and Optimization Methods for Urban Transportation System

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: 2 January 2025 | Viewed by 1838

Special Issue Editors

Prof. Dr. Yiming Bie

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Guest Editor
School of Transportation, Jilin University, Changchun 130022, China
Interests: advanced transit operations; traffic design and control
Special Issues, Collections and Topics in MDPI journals
Dr. Hu Zhang

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Guest Editor
School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Interests: traffic operation optimization; traffic engineering
Dr. Shidong Liang

E-Mail Website
Guest Editor
Business School, University of Shanghai for Science & Technology, Shanghai 200093, China
Interests: urban transit planning and operation; traffic control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The question of how to improve the sustainability of transportation has always been a hot topic. On the one hand, urban residents hope to improve the efficiency of the transportation system in order to reduce travel time. Conversely, transportation systems inevitably produce carbon emissions and affect the social environment. As such, scholars must propose more innovative methods for urban transportation systems in order to improve the efficiency and sustainability simultaneously. In recent years, the ownership of electric vehicles and connected autonomous vehicles has continued to increase, which can not only significantly reduce the carbon emissions of vehicles, but also make urban transportation smarter. With the application of big data and artificial intelligence technology in the transportation field, more innovative planning, control and operation optimization methods have emerged.

To make the urban transportation system safer, more efficient and low carbon, we must develop innovative methods on transportation planning, control and optimization. Efforts should also be geared toward understanding the potential, limits and mechanisms of the new vehicles and smart methods that will assist in mitigating traffic congestion and decreasing carbon emissions.  

Further research is required to determine the optimal planning, control and operation plans for the urban transportation system. In this Special Issue, we invite researchers to submit original research and review articles addressing all aspects related to urban transportation systems. Potential topics include, but are not limited to, the following:  

  • Electrified transportation system
  • Sustainable transit system
  • Multi-mode transportation system
  • Automated and connected transportation system
  • Adaptive traffic signal control
  • Data mining and big data in transportation system
  • Urban transportation infrastructure planning
  • Innovative methods for traffic safety and operations
  • Environmental impacts of transportation system
  • Eco-friendly mobility for urban vehicles

We look forward to receiving your contributions.

Prof. Dr. Yiming Bie
Dr. Hu Zhang
Dr. Shidong Liang
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. Sustainability 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 2400 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

  • intelligent transportation
  • transportation planning
  • traffic signal control
  • operation optimization
  • sustainable transportation
  • urban transit
  • artificial intelligence
  • big data
  • traffic safety
  • connected and automated vehicle

Published Papers (3 papers)

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Research

24 pages, 4747 KiB  
Article
Health Monitoring Analysis of an Urban Rail Transit Switch Machine
by Zishuo Wang, Di Sun, Jin Zhou, Kaige Guo, Jiaxin Zhang and Xiangyu Kou
Sustainability 2024, 16(9), 3527; https://doi.org/10.3390/su16093527 - 23 Apr 2024
Viewed by 293
Abstract
This paper discusses the health evaluation of an urban rail transit switch machine. In this paper, the working current data of the S700K switch machine are processed, and four common abnormal operating current curves are obtained through the existing data. Then, the MLP [...] Read more.
This paper discusses the health evaluation of an urban rail transit switch machine. In this paper, the working current data of the S700K switch machine are processed, and four common abnormal operating current curves are obtained through the existing data. Then, the MLP is used as the feature extractor of the action current curve to analyze the input action current data, learn and capture deep features from raw current data as Q-networks, and build MLP-DQN models. The monitoring of the abnormal state operation current of the switch machine is optimized by learning and optimizing the model weight through repeated experience. The experimental results show that the training accuracy of this model is stable at about 96.67%. Finally, the Fréchet distance was used to analyze the abnormal motion current curve, combined with the occurrence frequency and repair complexity of the abnormal type curve, the calculated results were analyzed, and the health of the switch machine was evaluated, which proved the high efficiency and superiority of the MLP-DQN method in the fault diagnosis of the switch machine equipment. The good health evaluation function of the switch machine can effectively support the maintenance of the equipment, and it has an important reference value for the intelligent operation and maintenance of subway signal equipment. The research results mark the maintenance of key equipment of urban rail transit systems, represent a solid step towards intelligent and automated transformation, and provide strong technical support for the safe operation and intelligent management of future rail transit systems. Full article
(This article belongs to the Special Issue Innovative and Sustainable Planning, Control and Optimization Methods for Urban Transportation System)
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19 pages, 1494 KiB  
Article
Enhancing Electric Bus Charging Scheduling: An Energy-Integrated Dynamic Bus Replacement Strategy with Time-of-Use Pricing
by Yang Liu, Bing Zeng, Kejun Long and Wei Wu
Sustainability 2024, 16(8), 3334; https://doi.org/10.3390/su16083334 - 16 Apr 2024
Viewed by 415
Abstract
Existing studies on electric bus (EB) scheduling mainly focus on the arrangement of bus charging at the bus terminals, which may lead to inflexible charging plans, high scheduling costs, and low utilization of electricity energy. To address these challenges, this paper proposes a [...] Read more.
Existing studies on electric bus (EB) scheduling mainly focus on the arrangement of bus charging at the bus terminals, which may lead to inflexible charging plans, high scheduling costs, and low utilization of electricity energy. To address these challenges, this paper proposes a dynamic bus replacement strategy. When the power of an in-service EB is insufficient, a standby EB stationed at nearby charging stations is dispatched in advance to replace this in-service EB at a designated bus stop. Passengers then transfer to the standby bus to complete their journey. The replaced bus proceeds to the charging station and transitions into a “standby bus” status after recharging. A mixed-integer nonlinear programming (MINLP) model is established to determine the dispatching plan for both standby and in-service EBs while also designing optimal charging schemes (i.e., the charging time, location, and the amount of charged power) for electric bus systems. Additionally, this study also incorporates the strategy of time-of-use electricity prices to mitigate the adverse impact on the power grid. The proposed model is linearized to the mixed-integer linear programming (MILP) model and efficiently solved by commercial solvers (e.g., GUROBI). The case study demonstrates that EBs with different energy levels can be dynamically assigned to different bus lines using bus replacement strategies, resulting in reduced electricity costs for EB systems without compromising on scheduling efficiency. Full article
(This article belongs to the Special Issue Innovative and Sustainable Planning, Control and Optimization Methods for Urban Transportation System)
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17 pages, 2408 KiB  
Article
CVDMARL: A Communication-Enhanced Value Decomposition Multi-Agent Reinforcement Learning Traffic Signal Control Method
by Ande Chang, Yuting Ji, Chunguang Wang and Yiming Bie
Sustainability 2024, 16(5), 2160; https://doi.org/10.3390/su16052160 - 05 Mar 2024
Viewed by 637
Abstract
Effective traffic signal control (TSC) plays an important role in reducing vehicle emissions and improving the sustainability of the transportation system. Recently, the feasibility of using multi-agent reinforcement learning technology for TSC has been widely verified. However, the process of mapping road network [...] Read more.
Effective traffic signal control (TSC) plays an important role in reducing vehicle emissions and improving the sustainability of the transportation system. Recently, the feasibility of using multi-agent reinforcement learning technology for TSC has been widely verified. However, the process of mapping road network states onto actions has encountered many challenges, due to the limited communication between agents and the partial observability of the traffic environment. To address this problem, this paper proposes a communication-enhancement value decomposition, multi-agent reinforcement learning TSC method (CVDMARL). The model combines two communication methods: implicit and explicit communication, decouples the complex relationships among the multi-signal agents through the centralized-training and decentralized-execution paradigm, and uses a modified deep network to realize the mining and selective transmission of traffic flow features. We compare and analyze CVDMARL with six different baseline methods based on real datasets. The results show that compared to the optimal method MN_Light, among the baseline methods, CVDMARL’s queue length during peak hours was reduced by 9.12%, the waiting time was reduced by 7.67%, and the convergence algebra was reduced by 7.97%. While enriching the information content, it also reduces communication overhead and has better control effects, providing a new idea for solving the collaborative control problem of multi-signalized intersections. Full article
(This article belongs to the Special Issue Innovative and Sustainable Planning, Control and Optimization Methods for Urban Transportation System)
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