Journal of Advanced Transportation

Traffic Safety in Intelligent and Connected Environment


Publishing date
01 Mar 2021
Status
Closed
Submission deadline
16 Oct 2020

Lead Editor

1Hefei University of Technology, Hefei, China

2The Hong Kong Polytechnic University, Kowloon, Hong Kong

3Beijing University of Technology, Beijing, China

4Beihang University, Beijing, China

This issue is now closed for submissions.

Traffic Safety in Intelligent and Connected Environment

This issue is now closed for submissions.

Description

Connected and Automated Vehicle (“CAV”) is well recognized as a crucial component of the modern intelligent transportation system. The application of CAV can relieve traffic congestion, reduce traffic emission and more importantly, enhance the overall operation efficiency of the transportation system, based on the real-time data exchange of vehicle trajectory using vehicle-to-vehicle (“V2V”) and vehicle-to-infrastructure (“V2I”) information and communication technology. Although promising great enhancements to traffic operation, CAV can induce great challenges to traffic safety. In particular, driver distraction is one of the leading contributing factors in collisions involving CAV. It is necessary to examine the role of the driver and their contribution to safety hazards. Furthermore, it is necessary to consider the effects of road environment and traffic conditions (mixed traffic composition of traditional vehicles and CAVs) on driver behavior and driving safety.

Additionally, automated driving is an emerging transport technology, which would become the ultimate form of CAV. However, achieving the ultimate goal of fully automated driving is still a long way off. “Human-Machine Cooperation Stage” refers to the transition stage between fully automated and conventional manual driving. Currently, human drivers still play a major role in traffic operation and safety. It is necessary to explore the fundamental issues of driving performance in the "Human-Machine Cooperation Stage" from the perspectives of driver’s behavior, physiological attributes, mental workload, and safety perception. Therefore, there is still a requirement for the implementation of cost-effective measures through public policy, vehicle technology, infrastructure development, legislation, and education.

This Special Issue aims to provide a platform for researchers and practitioners to exchange the idea of traffic safety in an intelligent and connected environment. Original research and review articles will be considered. All aspects of statistical analysis, machine learning, driving simulator experiments, and naturalistic driving experiments are of interest.

Potential topics include but are not limited to the following:

  • Traffic Safety Analysis of Connected and Automated Vehicles and Traditional Vehicles in Mixed State
  • Driving Behavior and Physiological Characteristics in an Intelligent and Connected Environment
  • Research on the Impact of Drivers' Risk Perception in Intelligent and Connected Environment
  • Research on Road Traffic Control in Intelligent and Connected Environment
  • Travel Behavior of Other Traffic Participants (such as pedestrians, cyclists, passengers, etc.) In an Intelligent and Connected Environment
  • Analysis of Factors Influencing Acceptance and Willingness toward Automated Driving
  • Interaction between Autonomous Vehicles and Other Traffic Participants
  • Optimization Method of Road Traffic Safety Facilities under Automated Driving Environment
  • Analysis of Collision Accidents Involving Autonomous Vehicles
  • Research on the Willingness and Manner of Taking Over During Human-Machine Cooperation for Automated Driving
  • Take Over Training Method during Human-Machine Cooperation for Automated Driving
  • Driving Simulation and Natural Driving Experiment under Human-Machine Cooperation Environment

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 6624130
  • - Research Article

An Overlapping Phase Approach to Optimize Bus Signal Priority Control under Two-Way Signal Coordination on Urban Arterials

Zijun Liang | Yun Xiao | Yun-Pang Flötteröd
  • Special Issue
  • - Volume 2021
  • - Article ID 6668091
  • - Research Article

Experimental Study of Electric Vehicle Yaw Rate Tracking Control Based on Differential Steering

Cong Li | Yun-Feng Xie | ... | Hui Jing
  • Special Issue
  • - Volume 2021
  • - Article ID 6617205
  • - Research Article

Implications of a Narrow Automated Vehicle-Exclusive Lane on Interstate 15 Express Lanes

Sahar Ghanipoor Machiani | Alidad Ahmadi | ... | Arash Jahangiri
  • Special Issue
  • - Volume 2020
  • - Article ID 8830752
  • - Research Article

Multiclock Constraint System Modelling and Verification for Ensuring Cooperative Autonomous Driving Safety

Jinyong Wang | Zhiqiu Huang | ... | Fei Wang
  • Special Issue
  • - Volume 2020
  • - Article ID 6675042
  • - Research Article

Passenger Volume Prediction by a Combined Input-Output and Distributed Lag Model and Data Analytics of Industrial Investment

Fan Yang | Zhao-guo Huang | ... | Sai Wang
  • Special Issue
  • - Volume 2020
  • - Article ID 6649867
  • - Research Article

Path Planning for Autonomous Vehicle Based on a Two-Layered Planning Model in Complex Environment

Jiajia Chen | Rui Zhang | ... | Pan Zhao
  • Special Issue
  • - Volume 2020
  • - Article ID 6678158
  • - Research Article

Multimode Traffic Travel Behavior Characteristics Analysis and Congestion Governance Research

Wen Li | Wei Feng | Hua-zhi Yuan
  • Special Issue
  • - Volume 2020
  • - Article ID 8881545
  • - Research Article

Analysis of Factors Affecting the Severity of Automated Vehicle Crashes Using XGBoost Model Combining POI Data

Hengrui Chen | Hong Chen | ... | Ruiyu Zhou
  • Special Issue
  • - Volume 2020
  • - Article ID 8878265
  • - Research Article

Critical Factors Analysis of Severe Traffic Accidents Based on Bayesian Network in China

Hong Chen | Yang Zhao | Xiaotong Ma
  • Special Issue
  • - Volume 2020
  • - Article ID 8848874
  • - Research Article

A New Video-Based Crash Detection Method: Balancing Speed and Accuracy Using a Feature Fusion Deep Learning Framework

Zhenbo Lu | Wei Zhou | ... | Chen Wang
Journal of Advanced Transportation
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Acceptance rate22%
Submission to final decision126 days
Acceptance to publication18 days
CiteScore3.900
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Impact Factor2.3
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