Adaptive-AR Model with Drivers’ Prediction for Traffic Simulation
We present a novel model called A2R—“Adaptive-AR”—based on a well-known continuum-based model called AR Aw and Rascle (2000) for the simulation of vehicle traffic flows. However, in the standard continuum-based model, vehicles usually follow the flows passively, without taking into account drivers&#...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | International Journal of Computer Games Technology |
| Online Access: | http://dx.doi.org/10.1155/2013/904154 |
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| _version_ | 1832559421165666304 |
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| author | Xuequan Lu Mingliang Xu Wenzhi Chen Zonghui Wang Abdennour El Rhalibi |
| author_facet | Xuequan Lu Mingliang Xu Wenzhi Chen Zonghui Wang Abdennour El Rhalibi |
| author_sort | Xuequan Lu |
| collection | DOAJ |
| description | We present a novel model called A2R—“Adaptive-AR”—based on a well-known continuum-based model called AR Aw and Rascle (2000) for the simulation of vehicle traffic flows. However, in the standard continuum-based model, vehicles usually follow the flows passively, without taking into account drivers' behavior and effectiveness. In order to simulate real-life traffic flows, we extend the model with a few factors, which include the effectiveness of drivers' prediction, drivers' reaction time, and drivers' types. We demonstrate that our A2R model is effective and the results of the experiments agree well with experience in real world. It has been shown that such a model makes vehicle flows perform more realistically and is closer to the real-life traffic than
AR (short for Aw and Rascle and introduced in Aw and Rascle (2000)) model while having a similar performance. |
| format | Article |
| id | doaj-art-6f64542d285c4bf7bd50140bd7498fc2 |
| institution | Kabale University |
| issn | 1687-7047 1687-7055 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Computer Games Technology |
| spelling | doaj-art-6f64542d285c4bf7bd50140bd7498fc22025-02-03T01:30:08ZengWileyInternational Journal of Computer Games Technology1687-70471687-70552013-01-01201310.1155/2013/904154904154Adaptive-AR Model with Drivers’ Prediction for Traffic SimulationXuequan Lu0Mingliang Xu1Wenzhi Chen2Zonghui Wang3Abdennour El Rhalibi4Zhejiang University, Hangzhou 310027, Zhejiang, ChinaZhengzhou University, Hangzhou 310027, Zhejiang, ChinaZhejiang University, Hangzhou 310027, Zhejiang, ChinaZhejiang University, Hangzhou 310027, Zhejiang, ChinaLiverpool John Moores University, UKWe present a novel model called A2R—“Adaptive-AR”—based on a well-known continuum-based model called AR Aw and Rascle (2000) for the simulation of vehicle traffic flows. However, in the standard continuum-based model, vehicles usually follow the flows passively, without taking into account drivers' behavior and effectiveness. In order to simulate real-life traffic flows, we extend the model with a few factors, which include the effectiveness of drivers' prediction, drivers' reaction time, and drivers' types. We demonstrate that our A2R model is effective and the results of the experiments agree well with experience in real world. It has been shown that such a model makes vehicle flows perform more realistically and is closer to the real-life traffic than AR (short for Aw and Rascle and introduced in Aw and Rascle (2000)) model while having a similar performance.http://dx.doi.org/10.1155/2013/904154 |
| spellingShingle | Xuequan Lu Mingliang Xu Wenzhi Chen Zonghui Wang Abdennour El Rhalibi Adaptive-AR Model with Drivers’ Prediction for Traffic Simulation International Journal of Computer Games Technology |
| title | Adaptive-AR Model with Drivers’ Prediction for Traffic Simulation |
| title_full | Adaptive-AR Model with Drivers’ Prediction for Traffic Simulation |
| title_fullStr | Adaptive-AR Model with Drivers’ Prediction for Traffic Simulation |
| title_full_unstemmed | Adaptive-AR Model with Drivers’ Prediction for Traffic Simulation |
| title_short | Adaptive-AR Model with Drivers’ Prediction for Traffic Simulation |
| title_sort | adaptive ar model with drivers prediction for traffic simulation |
| url | http://dx.doi.org/10.1155/2013/904154 |
| work_keys_str_mv | AT xuequanlu adaptivearmodelwithdriverspredictionfortrafficsimulation AT mingliangxu adaptivearmodelwithdriverspredictionfortrafficsimulation AT wenzhichen adaptivearmodelwithdriverspredictionfortrafficsimulation AT zonghuiwang adaptivearmodelwithdriverspredictionfortrafficsimulation AT abdennourelrhalibi adaptivearmodelwithdriverspredictionfortrafficsimulation |