Deployment Optimization of Connected and Automated Vehicle Lanes with the Safety Benefits on Roadway Networks

Reasonable deployment of connected and automated vehicle (CAV) lanes which separating the heterogeneous traffic flow consisting of both CAVs and human-driven vehicles (HVs) can not only improve traffic safety but also greatly improve the overall roadway efficiency. This paper simplified CAV lane dep...

Full description

Saved in:
Bibliographic Details
Published in:Journal of advanced transportation 2020, Vol.2020 (2020), p.1-9
Main Authors: Long, Kejun, Hao, Wei, Wu, Zhizhou, Gao, Zhibo
Format: Article
Language:English
Subjects:
Algorithms
Automation
Construction costs
Cost analysis
Decision making
Design
Energy consumption
Genetic algorithms
Market penetration
Optimization
Roads
Roads & highways
Sensitivity analysis
Traffic accidents & safety
Traffic assignment
Traffic capacity
Traffic congestion
Traffic control
Traffic flow
Traffic management
Traffic models
Traffic planning
Traffic safety
Transportation
Transportation models
Travel
Vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Reasonable deployment of connected and automated vehicle (CAV) lanes which separating the heterogeneous traffic flow consisting of both CAVs and human-driven vehicles (HVs) can not only improve traffic safety but also greatly improve the overall roadway efficiency. This paper simplified CAV lane deployment plan into the problem of traffic network design and proposed a comprehensive decision-making method for CAV lane deployment plan. Based on the traffic equilibrium theory, this method aims to reduce the travel cost of the traffic network and the management cost of CAV lanes using a bilevel primary-secondary programming model. In addition, the upper level is the decision-making scheme of the lane deployment, while the lower level is the traffic assignment model including CAV and HV modes based on the decision-making scheme of the upper level. After that, a genetic algorithm was designed to solve the model. Finally, a medium-scaled traffic network was selected to verify the effectiveness of the proposed model and algorithm. The case study shows that the proposed method obtained a feasible scheme for lane deployment considering from both the system travel cost and management cost of CAV lanes. In addition, a sensitivity analysis of the market penetration rate of CAVs, traffic demand, and the capacity of CAVLs further proves the applicability of this model, which can achieve better allocation of system resources and also improve the traffic efficiency.
ISSN:0197-6729
2042-3195
DOI:10.1155/2020/9401062