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Real-Time Incident-Responsive Signal Control Strategy under Partially Connected Vehicle Environment
- Source :
- Journal of Advanced Transportation. July 22, 2022, Vol. 2022
- Publication Year :
- 2022
-
Abstract
- The performance of the traffic system can drastically drop when nonrecurrent congestion caused by incidents occurs. Early detection and clearing of traffic incidents will enable the mitigation of the congestion and early restoration of normal traffic conditions. The research in this paper utilized the vehicle information from the recent technological advancement in transportation systems, connected vehicles (CV), and loop-detector information for nonconnected vehicles (NCVs) and developed a novel algorithm to (1) control traffic signals for normal traffic conditions in the absence of incidents, (2) detect traffic incidents using CV/NCV information, and (3) control traffic signals during the occurrence and dissipation of incidents. All the 3 strategies were integrated into one algorithm, which runs as per the real-time traffic conditions, in the presence or absence of incidents. Space-mean speeds of the vehicles on nonincident lanes and throughput maximization criteria were taken as the indicators for the activation of specific signal timings directed at the incident-affected approach. Diverse incident scenarios were tested on a four-legged isolated intersection using the VISSIM simulation tool. Incident detection results showed a higher detection rate and lower mean detection time at higher CV penetration and higher traffic volumes, and at the incident locations nearer to the stop-line. The proposed incident-responsive signal control strategy at 40% and higher CV penetration showed better performance over EPICS adaptive signal control solution, in reducing average travel time delay and the average number of stops per vehicle.<br />Author(s): Kancharla K. K. Chandan (corresponding author) [1]; Álvaro J. M. Seco [2]; Ana M. C. Bastos Silva [2] 1. Introduction and Background Nonrecurrent congestion caused by traffic incidents remains [...]
- Subjects :
- Algorithm
Detectors
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 01976729
- Volume :
- 2022
- Database :
- Gale General OneFile
- Journal :
- Journal of Advanced Transportation
- Publication Type :
- Academic Journal
- Accession number :
- edsgcl.712138402
- Full Text :
- https://doi.org/10.1155/2022/8970695