Transportation Research Part C: Emerging Technologies
Call for papers for the special issue: Modeling and managing mixed traffic with human-driven and automated vehicles
摘要截稿:
全文截稿: 2019-05-31
影响因子: 6.077
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 工程技术 - 1区
• 小类 : 运输科技 - 1区
Overview
Introduction
Traffic models and theories, as well as traffic control, management and evaluation approaches for conventional traffic have been intensively investigated for several decades. Both, their advantages and limitations are well recognized in the traffic field. However, with the increasing development of connected and automated vehicle (CAV) technologies, new traffic models considering the CAV environment must be developed. In particular, in the coming 20-30 years the vehicle fleet will most likely be made up of a mixture of human-driven vehicles and CAVs. This complex traffic environment presents challenges to traffic modelling, control and management, especially when considering stochastic driving characteristics of humans and the uncertainty associated with the interaction between human driven vehicles and CAVs.
Although the emergence of connected and automated vehicles provides abundant data and opportunities in the new era of traffic modelling, control, management and evaluation, some issues still to be solved. Below are a few sample questions that still remain largely unexplored.
(1) Are existing traffic models able to capture the traffic flow characteristics of a mixed traffic environment where human-driven and CAV vehicles interact? If not, how can we model such environment?
(2) How can we leverage CAV data provided at low penetration rates for improving the understanding of prevailing/emerging traffic phenomena?
(3) How can we efficiently utilize CAV data for traffic control and management?
(4) How can traffic evaluation approaches be improved, and policy making be supported by incorporating CAV data?
Scope of the special issue
The focus of this special issue is on innovative approaches, models and algorithms for traffic control, management and evaluation of mixed traffic flow conditions (i.e., with human-driven vehicles and CAVs). Potential topics of interest include but are not limited to:
Innovative traffic models/theories for mixed traffic conditions
Connected and automated vehicle data-based traffic estimation and prediction approaches (e.g., based on trajectory data)
Traffic control approaches and algorithms for mixed traffic conditions
Performance evaluation of mixed traffic systems via simulation approaches or field experiments
Traffic monitoring and evaluation based on the CAV data in mixed traffic conditions