Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving Behavior
Variable speed limit (VSL) control is a flexible restriction on the rate at which motorists can drive on a given stretch of road. Effective VSL control can increase safety and provide clear guidance for motorists. Previous traffic flow models of VSL control were mostly based on the influence of VSL...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8859494 |
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| _version_ | 1832560260420730880 |
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| author | Xu Qu Linheng Li Ziwei Yi Peipei Mao Mofeng Yang |
| author_facet | Xu Qu Linheng Li Ziwei Yi Peipei Mao Mofeng Yang |
| author_sort | Xu Qu |
| collection | DOAJ |
| description | Variable speed limit (VSL) control is a flexible restriction on the rate at which motorists can drive on a given stretch of road. Effective VSL control can increase safety and provide clear guidance for motorists. Previous traffic flow models of VSL control were mostly based on the influence of VSL on average speed (macro) or driver’s expected speed (micro). Few models considered the influence of VSL on driver’s actual driving behavior. In this paper, we first briefly introduce the big traffic data involved in this study and explain the mapping relationship between the data and driving behavior. Then, we analyze the driver’s actual driving behavior under the VSL control. Then, an improved single-lane cellular automaton model is established based on the driving behavior characteristics under VSL control. After that, we calibrate the parameters of the single-lane cellular automaton model with the left lane as the calibration object. Finally, this paper uses the proposed single-lane cellular automaton model to simulate the traffic flow characteristics under VSL control. The numerical simulation results show that the simulation of the variable speed limit in different density intervals presents different results, but these results are consistent with the actual situation of variable speed limit control, which verifies the validity of the proposed model. |
| format | Article |
| id | doaj-art-95103de53d7844278d9de80008468bac |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-95103de53d7844278d9de80008468bac2025-02-03T01:27:59ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88594948859494Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving BehaviorXu Qu0Linheng Li1Ziwei Yi2Peipei Mao3Mofeng Yang4School of Transportation, Southeast University, Nanjing, ChinaSchool of Transportation, Southeast University, Nanjing, ChinaSchool of Transportation, Southeast University, Nanjing, ChinaSchool of Transportation, Southeast University, Nanjing, ChinaDepartment of Civil and Environmental Engineering, University of Maryland, College Park, MD 20742, USAVariable speed limit (VSL) control is a flexible restriction on the rate at which motorists can drive on a given stretch of road. Effective VSL control can increase safety and provide clear guidance for motorists. Previous traffic flow models of VSL control were mostly based on the influence of VSL on average speed (macro) or driver’s expected speed (micro). Few models considered the influence of VSL on driver’s actual driving behavior. In this paper, we first briefly introduce the big traffic data involved in this study and explain the mapping relationship between the data and driving behavior. Then, we analyze the driver’s actual driving behavior under the VSL control. Then, an improved single-lane cellular automaton model is established based on the driving behavior characteristics under VSL control. After that, we calibrate the parameters of the single-lane cellular automaton model with the left lane as the calibration object. Finally, this paper uses the proposed single-lane cellular automaton model to simulate the traffic flow characteristics under VSL control. The numerical simulation results show that the simulation of the variable speed limit in different density intervals presents different results, but these results are consistent with the actual situation of variable speed limit control, which verifies the validity of the proposed model.http://dx.doi.org/10.1155/2020/8859494 |
| spellingShingle | Xu Qu Linheng Li Ziwei Yi Peipei Mao Mofeng Yang Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving Behavior Journal of Advanced Transportation |
| title | Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving Behavior |
| title_full | Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving Behavior |
| title_fullStr | Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving Behavior |
| title_full_unstemmed | Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving Behavior |
| title_short | Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving Behavior |
| title_sort | traffic flow modeling of freeway variable speed limit control based on the big data of driving behavior |
| url | http://dx.doi.org/10.1155/2020/8859494 |
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