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Open Access

Dangerous Driving Behavior Recognition and Prevention Using an Autoregressive Time-Series Model

Department of Automation, Tsinghua University, Beijing 100084, China.
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Abstract

Time headway is an important index used in characterizing dangerous driving behaviors. This research focuses on the decreasing tendency of time headway and investigates its association with crash occurrence. An autoregressive (AR) time-series model is improved and adopted to describe the dynamic variations of average daily time headway. Based on the model, a simple approach for dangerous driving behavior recognition is proposed with the aim of significantly decreasing headway. The effectivity of the proposed approach is validated by means of empirical data collected from a medium-sized city in northern China. Finally, a practical early-warning strategy focused on both the remaining life and low headway is proposed to remind drivers to pay attention to their driving behaviors and the possible occurrence of crash-related risks.

Keywords

time headwaydriving behaviortraffic safetyautoregressive time-series modelremaining lifedriving warning strategy

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Tsinghua Science and Technology
Volume 22 Issue 6,
December 2017
Pages 682-690
DOI: 10.23919/TST.2017.8195350
Cite this article:
Chen H, Feng S, Pei X, et al. Dangerous Driving Behavior Recognition and Prevention Using an Autoregressive Time-Series Model. Tsinghua Science and Technology, 2017, 22(6): 682-690. https://doi.org/10.23919/TST.2017.8195350

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Received: 21 July 2016
Revised: 28 September 2016
Accepted: 03 October 2016
Published: 14 December 2017
© The author(s) 2017
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