Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/7959815 |
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| _version_ | 1832559054535262208 |
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| author | Yuwei Bie Mudasser Seraj Can Zhang Tony Z. Qiu |
| author_facet | Yuwei Bie Mudasser Seraj Can Zhang Tony Z. Qiu |
| author_sort | Yuwei Bie |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1a0f69db3ae04efdabbd96425fe4c360 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-1a0f69db3ae04efdabbd96425fe4c3602025-02-03T01:30:54ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/79598157959815Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and DemandYuwei Bie0Mudasser Seraj1Can Zhang2Tony Z. Qiu3Department of Civil and Environmental Engineering, University of Alberta, Edmonton, CanadaDepartment of Civil and Environmental Engineering, University of Alberta, Edmonton, CanadaDepartment of Civil and Environmental Engineering, University of Alberta, Edmonton, CanadaDepartment of Civil and Environmental Engineering, University of Alberta, Edmonton, CanadaVariable 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.http://dx.doi.org/10.1155/2018/7959815 |
| spellingShingle | Yuwei Bie Mudasser Seraj Can Zhang Tony Z. Qiu Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand Journal of Advanced Transportation |
| title | Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand |
| title_full | Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand |
| title_fullStr | Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand |
| title_full_unstemmed | Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand |
| title_short | Improving Traffic State Prediction Model for Variable Speed Limit Control by Introducing Stochastic Supply and Demand |
| title_sort | improving traffic state prediction model for variable speed limit control by introducing stochastic supply and demand |
| url | http://dx.doi.org/10.1155/2018/7959815 |
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