Adaptive-AR model with drivers' prediction for traffic simulation

Lu, Xuequan, Xu, Mingliang, Chen, Wenzhi, Wang, Zonghui and El Rhalibi, Abdennour 2013, Adaptive-AR model with drivers' prediction for traffic simulation, International journal of computer games technology, doi: 10.1155/2013/904154.

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Title Adaptive-AR model with drivers' prediction for traffic simulation
Author(s) Lu, XuequanORCID iD for Lu, Xuequan
Xu, Mingliang
Chen, Wenzhi
Wang, Zonghui
El Rhalibi, Abdennour
Journal name International journal of computer games technology
Article ID 904154
Total pages 8
Publisher Hindawi Publishing Corporation
Place of publication Cairo, Egypt
Publication date 2013
ISSN 1687-7047
Summary 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. © 2013 Xuequan Lu et al.
Language eng
DOI 10.1155/2013/904154
Indigenous content off
Field of Research 08 Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2013, Xuequan Lu et al.
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