Special Issue "Intelligent Traffic Control and Optimization"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 31 May 2023 | Viewed by 1041

Special Issue Editors

College of Transportation, Jilin University, Changchun 130022, China
Interests: traffic simulation; transportation planning; travel behavior analysis
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518133, China
Interests: traffic signal control; individual-based traffic data analysis; transportation cyber-physical systems
School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Interests: traffic flow theory; connected and automated vehicles; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals
Dr. Qiaowen Bai
E-Mail Website
Guest Editor
College of Design and Engineering, National University of Singapore, Singapore 117566, Singapore
Interests: traffic flow theory; transport infrastructures (planning, design, operations and management)

Special Issue Information

Dear Colleagues,

With the recent emerging technologies, including the Internet of things (IOT), vehicle to everything (V2X), artificial intelligence (AI) and distributed computing, the transportation system has been enabled to perceive and manage diversified travel demand in individual-level resolution. Based on the trajectory records of individual trips, various operation and control issues in different domains, such as intersection signal control, transportation scheduling, vehicle movement planning, and bus dispatching, can be dealt with increasing amounts of information and more advanced optimization methods. The emerging technologies have promoted the development of conventional control and optimization theories in the existing literature and the practical applicability of related works in the real world.

Therefore, the aim of this Special Issue is to welcome original research and surveys with a focus on intelligent traffic control and optimization theories and applications, based on advanced technologies. It also aims to promote the exchanges and interactions between investigators across different fields. We invite high quality, original research articles, as well as review articles.

Potential topics include, but are not limited to, the following:

  1. Transport infrastructure layout and design for IOT applications;
  2. Transportation cyber-physical systems modeling;
  3. Traffic information detection and fusion based on vehicle-to-infrastructure sensing;
  4. Intelligent traffic signal control and cooperative intersections;
  5. Advanced control technology for connected and automated vehicles (CV/AVs);
  6. Cooperative control of signal intersections and CV/AVs with V2X communications;
  7. Traffic flow optimization in the mix of human-driven vehicles (HDVs) and CV/Avs;
  8. Integration and cooperative optimization of multimodal transportation systems;
  9. Traffic system simulation under V2X communication environment.

Prof. Dr. Fang Zong
Dr. Yiting Zhu
Dr. Yanyan Qin
Dr. Qiaowen Bai
Guest Editors

Manuscript Submission Information

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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. Electronics 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 2000 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.


  • traffic control
  • traffic flow optimization
  • vehicle operation
  • transport infrastructure layout
  • traffic simulation
  • ITS
  • V2X

Published Papers (1 paper)

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Adaptive Kernel Density Estimation for Traffic Accidents Based on Improved Bandwidth Research on Black Spot Identification Model
Electronics 2022, 11(21), 3604; https://doi.org/10.3390/electronics11213604 - 04 Nov 2022
Viewed by 725
At present, the total length of accident blackspot accounts for 0.25% of the total length of the road network, while the total number of accidents that occurred at accident black spots accounts for 25% of the total number of accidents on the road [...] Read more.
At present, the total length of accident blackspot accounts for 0.25% of the total length of the road network, while the total number of accidents that occurred at accident black spots accounts for 25% of the total number of accidents on the road network. This paper describes a traffic accident black spot recognition model based on the adaptive kernel density estimation method combined with the road risk index. Using the traffic accident data of national and provincial trunk lines in Shanghai and ArcGIS software, the recognition results of black spots were compared with the recognition results of the accident frequency method and the kernel density estimation method, and the clustering degree of recognition results of adaptive kernel density estimation method were analyzed. The results show that: the accident prediction accuracy index values of the accident frequency method, kernel density estimation method, and traffic accident black spot recognition model were 14.39, 16.36, and 18.25, respectively, and the lengths of the traffic accident black spot sections were 184.68, 162.45, and 145.57, respectively, which means that the accident black spot section determined by the accident black spot recognition model was the shortest and the number of traffic accidents identified was the largest. Considering the safety improvement budget of 20% of the road length, the adaptive kernel density estimation method could identify about 69% of the traffic accidents, which was 1.13 times and 1.27 times that of the kernel density estimation method and the accident frequency method, respectively. Full article
(This article belongs to the Special Issue Intelligent Traffic Control and Optimization)
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