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Journal of Advanced Transportation
Volume 2017 (2017), Article ID 2437539, 14 pages
https://doi.org/10.1155/2017/2437539
Research Article

A New Car-Following Model considering Driving Characteristics and Preceding Vehicle’s Acceleration

1Beijing Advanced Innovation Center for Future Internet Technology, Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Faculty of Information Technology, Beijing University of Technology, 100 Ping Le Yuan, Chaoyang District, Beijing 100124, China
2Beijing Transportation Information Center, Tower B, Shoufa Building, No. A9, Liuliqiao South Lane, Fengtai District, Beijing 100073, China

Correspondence should be addressed to Yong Zhang; nc.ude.tujb@0102gnoygnahz

Received 5 March 2017; Revised 16 May 2017; Accepted 9 July 2017; Published 3 October 2017

Academic Editor: David F. Llorca

Copyright © 2017 Yong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In the past decades, many improved car-following models based on the full velocity difference (FVD) model have been developed. But these models do not consider the acceleration of leading vehicle. Some of them consider individual anticipation behavior of drivers, but they either do not quantitatively determine the types of driving or artificially divide the driving types rather than deriving them from actual traffic data. In this paper, driver’s driving styles are firstly categorized based on actual traffic data via data mining and clustering algorithm. Secondly, a new car-following model based on FVD model is developed, taking into account individual anticipation effects and the acceleration of leading vehicle. The effect of driving characteristics and leading vehicle’s acceleration on car-following behavior is further analyzed via numerical simulation. The results show that considering the acceleration of preceding vehicle in the model improves the stability of traffic flow and different driving characteristics have different influence on the stability of traffic flow.