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Sustainability
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Urban Intelligent Traffic System Control and Optimization
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Urban Intelligent Traffic System Control and Optimization

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

Deadline for manuscript submissions: closed (17 June 2023) | Viewed by 3820

Special Issue Editors

Prof. Dr. Shuai Su

E-Mail Website
Guest Editor
1. State Key Laboratory of Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
2. Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University, Beijing, China
Interests: intelligent traffic control and optimization; unmanned systems and intelligent sensing; control engineering; artificial intelligence (AI)
Dr. Jidong Lv

E-Mail Website
Guest Editor
The National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing, China
Interests: transport engineering; transport modeling; traffic safety; ITS

Special Issue Information

Dear Colleagues,

How to improve the transportation capacity is currently one of the most important directions for research on the development of urban traffic systems. It is envisioned that intelligent driving will provide enabling key technologies to enhance transportation efficiency, reduce traffic incidents, improve safety, and mitigate the impacts of traffic congestion. Intelligent driving, realized through the seamless integration of advanced artificial intelligence, control, and communication technologies, will face a series of technological, economic, regulatory, and other challenges.

To allow the movement of the vehicle to be planned, it is very important that the trajectory of the surrounding moving body is accurately predicted, which can serve as a basis for the subsequent implementation of vehicle safety control. Additionally, for complex operation scenarios, further discussions are needed to realize stability in the control of vehicles and platoons with nonlinear characteristics under the premise of satisfying multiple objectives and constraints.

The aim of this Special Issue is to present a collection of high-quality research papers on recent developments, current research challenges, and future directions in the use of intelligent driving to realize an urban intelligent traffic system that is safer and more efficient.

Potential topics include but are not limited to the following:

1) Energy efficiency and sustainability of urban public transportation

2) Networked information processing, decision making, and intelligent control

3) Rail system modeling and optimization

4) Trajectory prediction and motion planning in intelligent driving

5) Intelligent transportation, rail traffic modeling, and decentralized congestion control

6) Role of artificial intelligence and operations research in urban traffic system control

We look forward to receiving your contributions.

Prof. Dr. Shuai Su
Dr. Jidong Lv
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 driving
  • traffic operation safety
  • platoon control theory
  • trajectory optimization

Published Papers (2 papers)

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Research

18 pages, 3705 KiB  
Article
Deep Reinforcement Learning-Based Holding Control for Bus Bunching under Stochastic Travel Time and Demand
by Dong Liu, Feng Xiao, Jian Luo and Fan Yang
Sustainability 2023, 15(14), 10947; https://doi.org/10.3390/su151410947 - 12 Jul 2023
Cited by 1 | Viewed by 1269
Abstract
Due to the inherent uncertainties of the bus system, bus bunching remains a challenging problem that degrades bus service reliability and causes passenger dissatisfaction. This paper introduces a novel deep reinforcement learning framework specifically designed to address the bus bunching problem by implementing [...] Read more.
Due to the inherent uncertainties of the bus system, bus bunching remains a challenging problem that degrades bus service reliability and causes passenger dissatisfaction. This paper introduces a novel deep reinforcement learning framework specifically designed to address the bus bunching problem by implementing dynamic holding control in a multi-agent system. We formulate the bus holding problem as a decentralized, partially observable Markov decision process and develop an event-driven simulator to emulate real-world bus operations. An approach based on deep Q-learning with parameter sharing is proposed to train the agents. We conducted extensive experiments to evaluate the proposed framework against multiple baseline strategies. The proposed approach has proven to be adaptable to the uncertainties in bus operations. The results highlight the significant advantages of the deep reinforcement learning framework across various performance metrics, including reduced passenger waiting time, more balanced bus load distribution, decreased occupancy variability, and shorter travel time. The findings demonstrate the potential of the proposed method for practical application in real-world bus systems, offering promising solutions to mitigate bus bunching and enhance overall service quality. Full article
(This article belongs to the Special Issue Urban Intelligent Traffic System Control and Optimization)
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21 pages, 7122 KiB  
Article
Brake Instability Dynamic Model and Active Control Strategy for a Multiunit Articulated Rubber-Wheel Autonomous Rail Rapid Transit System
by Tao Li, Shuo Zhang, Gang Xiao, Minqi Wang, Hanwen Zhong and Jianghua Feng
Sustainability 2022, 14(21), 14531; https://doi.org/10.3390/su142114531 - 4 Nov 2022
Cited by 2 | Viewed by 1895
Abstract
Due to the particularity of the structure, the dynamic properties of multiunit articulated rubber-wheel autonomous rail rapid transit system are very complex, which increases the difficulty of studying its braking stability. In this paper, a dynamic analysis model for the emergency braking of [...] Read more.
Due to the particularity of the structure, the dynamic properties of multiunit articulated rubber-wheel autonomous rail rapid transit system are very complex, which increases the difficulty of studying its braking stability. In this paper, a dynamic analysis model for the emergency braking of a multiunit articulated rubber-wheel autonomous rail rapid transit system is established by introducing the axle load transfer, suspension deformation compatibility equation, articulation force relationship equations, etc. Based on an in-depth analysis of the risks of the lateral swing instability and their formation mechanisms, an active control strategy for the multiunit articulated rubber-wheel autonomous rail rapid transit system under emergency braking conditions is innovatively proposed to ensure the stability of the vehicle, with the shortest braking distance as the optimization goal. Through simulation and experimentation, the established dynamic model is confirmed to approach the real vehicle well, and the feasibility of the active control strategy is proved. Full article
(This article belongs to the Special Issue Urban Intelligent Traffic System Control and Optimization)
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