Vehicular License Plate Detection Data Fusion and Algorithms for Dynamic Traffic Signal Control
The license plate recognition (LPR) data have become widely available at urban intersections in China. The LPR data is featured with measured headways with time stamps at all approaches of the intersections such that traffic flow rate and operational conditions can be further estimated in replacement of traditional loop data for actuated traffic signal control. This paper presents a study of developing innovative algorithms to dynamically determine the signal control parameters against varying traffic conditions measured by the LPR data sets. The performance of the proposed control algorithm was evaluated via simulation by comparing the LPR-based actuated signal control scheme and the existing pre-timed control, with the use of the data obtained at intersections in City of Jinan, Shandong Province, China. The results indicate an obvious improvement of the traffic control performance and initially prove the developed algorithms adaptive to the LPR-based actuated traffic signal control schemes.
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Availability:
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Supplemental Notes:
- © 2021 American Society of Civil Engineers.
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Corporate Authors:
American Society of Civil Engineers
1801 Alexander Bell Drive
Reston, VA United States 20191-4400 -
Authors:
- Wei, Heng
- Nie, Chunting
- Shi, Jianjun
- Zhang, Mengmeng
- Publication Date: 2021
Language
- English
Media Info
- Pagination: pp 414 - 425
- Monograph Title: International Conference on Transportation and Development 2021: Transportation Operations, Technologies, and Safety
Subject/Index Terms
- TRT Terms: Advanced traffic management systems; Algorithms; Character recognition; Data files; Data fusion; Innovation; Intersections; License plates; Loop detectors; Simulation; Traffic control; Traffic flow rate; Vehicle detectors
- Geographic Terms: Jinan (China)
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors;
Filing Info
- Accession Number: 01777563
- Record Type: Publication
- ISBN: 9780784483534
- Files: TRIS, ASCE
- Created Date: Jul 23 2021 3:26PM