Analysis on congestion mechanism of CAVs around traffic accident zones.

Autor: Ma Q; Department of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China. Electronic address: qlm@cqjtu.edu.cn., Wang X; Department of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China., Niu S; Department of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China., Zeng H; Sichuan Chongqing Transportation Co., LTD, CNPC Chuanqing Drilling Engineering Company Limited., Chongqing 401147, China., Ullah S; Department of Engineering & Information Technology, Khwaja Fareed University, Punjab 64200, Pakistan.
Jazyk: angličtina
Zdroj: Accident; analysis and prevention [Accid Anal Prev] 2024 Sep; Vol. 205, pp. 107663. Date of Electronic Publication: 2024 Jun 19.
DOI: 10.1016/j.aap.2024.107663
Abstrakt: Unexpected traffic accidents cause traffic congestion and aggravate the unsafe situation on the roadways. Reducing the impact of such congestion by introducing Connected and Autonomous Vehicles (CAVs) into the traditional traffic flow is possible. It requires estimating the incident's duration and analyzing the incident's impact area to determine the appropriate strategy. To guide the driver in making efficient and accurate judgments and avoiding secondary traffic congestion, the Cooperative Adaptive Cruise Control (CACC) model with dynamic safety distance and the Intelligent Driver Model (IDM) based on the safety potential field theory are introduced to build the evolution model of accidental traffic congestion under diversion interference and non-interference. The Huatao Interchange section of the Inner Ring Highway in the Banan District of Chongqing, China, was selected as the test section for simulating mixed traffic flow under different CAVs permeability (P c ). The relationship between the evacuation time, evacuation traffic volume, and the accident impact degree index (including the farthest queue length and accident duration) under the diversion intervention scenario was analyzed, respectively. The results of the study indicate that the higher the penetration of CAVs, the more significant the improvement in traffic flow occupancy, flow, and speed. Diversion interventions reduce congestion, about 50 % of the duration without interventions, when P c  ≤ 80 %. The traffic volume of diversion interference is non-linearly positively correlated with the maximum queue length, and the earlier the interference time, the stronger the positive correlation. The negative correlation between the interference time and queue length is weak at low evacuation traffic volume. With the increase in evacuation traffic volume, the influence of evacuation time on queue length becomes stronger. The maximum queue length value interval under different conditions is [348 m, 3140 m], and the shortest evacuation time is [1649 s, 2834 s]. The traffic flow data obtained from the simulation are imported into the episodic traffic congestion evolution model. The congestion evaluation indexes are calculated under non-interference and interference measures and compared with the simulation results. The maximum relative error is within 5.38 %. The results can be of great significance in relieving congestion caused by traffic accidents and promptly restoring road capacity.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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