Adaptive-AR Model with Drivers’ Prediction for Traffic Simulation | Zendy

Xuequan Lu | Zendy; Mingliang Xu | Zendy; Wenzhi Chen | Zendy; Zonghui Wang | Zendy; Abdennour El Rhalibi | Zendy

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Adaptive-AR Model with Drivers’ Prediction for Traffic Simulation

Author(s) -

Xuequan Lu,

Mingliang Xu,

Wenzhi Chen,

Zonghui Wang,

Abdennour El Rhalibi

Publication year - 2013

Publication title -

international journal of computer games technology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.248

H-Index - 19

eISSN - 1687-7055

pISSN - 1687-7047

DOI - 10.1155/2013/904154

Subject(s) - traffic model , computer science , microscopic traffic flow model , traffic flow (computer networking) , traffic simulation , simulation , econometrics , traffic generation model , mathematics , real time computing , transport engineering , engineering , microsimulation , computer security , computer network

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 thanAR (short for Aw and Rascle and introduced in Aw and Rascle (2000)) model while having a similar performance

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