Adaptive-AR Model with Drivers' Prediction for Traffic Simulation. (26th September 2013)

• Record Type: Journal Article
• Authors: Lu, Xuequan; Xu, Mingliang; Chen, Wenzhi; Wang, Zonghui; El Rhalibi, Abdennour
• Other Names: Arya Ali Academic Editor.
• Is Part Of: International journal of computer games technology. Volume 2013(2013)
• Publication Date: 2013-09-26
• Subjects: Computer games -- Design -- Periodicals; Computer games -- Programming -- Periodicals; Jeux d'ordinateur; Jeux d'ordinateur -- Programmation; Computer games -- Design; Computer games -- Programming; Electronic journals; Periodicals; 794.81
• DOI: 10.1155/2013/904154
• Languages: English
• ISSNs: 1687-7047

Abstract

Abstract : We present a novel model called A 2 R—"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 A 2 R 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.