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Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand
- Source :
- Journal of Advanced Transportation. Annual, 2018, Vol. 2018
- Publication Year :
- 2018
-
Abstract
- Variable speed limit (VSL) is becoming recognized as an effective way to improve traffic throughput and road safety. In particular, methods based on traffic state prediction exhibit promising potential to prevent future traffic congestion and collisions. However, field observations indicate that the traffic state prediction model results in nonnegligible error that impacts the next step decision making of VSL. Thus, this paper investigates how to eliminate this prediction error within a VSL environment. In this study, the traffic state prediction model is a second-order traffic flow model named METANET, while the VSL control is model predictive control (MPC) based, and the VSL decision is discrete optimized choice. A simplified version of the switching mode stochastic cell transmission model (SCTM) is integrated with the METANET model to eliminate the prediction error. The performance of the proposed method is assessed using field data from a VSL pilot test in Edmonton, Canada, and is compared with the prediction results of the baseline METANET model during the road test. The results show that during the most congested period the proposed SCTM-METANET model significantly improves the prediction accuracy of regular METANET model.<br />1. Introduction The VSL control method was designed to keep the credibility of speed limits even under adverse conditions such as congestion and bottleneck road segments, so that the speed [...]
- Subjects :
- Traffic congestion -- Analysis
Subjects
Details
- Language :
- English
- ISSN :
- 01976729
- Volume :
- 2018
- Database :
- Gale General OneFile
- Journal :
- Journal of Advanced Transportation
- Publication Type :
- Academic Journal
- Accession number :
- edsgcl.591394783
- Full Text :
- https://doi.org/10.1155/2018/7959815