@article{ref1,
title="Investigating the long- and short-term driving characteristics and incorporating them into car-following models",
journal="Transportation research part C: emerging technologies",
year="2020",
author="Chen, Xiaoyun and Sun, Jian and Ma, Zian and Sun, Jie and Zheng, Zuduo",
volume="117",
number="",
pages="e102698-e102698",
abstract="This study provides a new method for better incorporating human factors in modeling car-following behavior. As the primary decision maker and vehicle operator, human driver is the vital component of the driving process. During the driving process, an external stimulus may trigger short-term psychological changes, and these changes are considered as the endogenous cause of many abnormal driving behaviors, which often lead to unsafe traffic disturbances and even crashes. In this paper, we investigate the intrinsic long-term driving characteristics and its short-term changes after driver experiences an external stimulus. A long- and short-term driving (LSTD) model is proposed to incorporate such changes into car-following driving behavior modelling. The long-term driving characteristics are extracted through a cluster analysis, and the changes after an external stimulus are identified and measured as the indicator of the short-term driving characteristics. NGSIM data are used to demonstrate the existence of LSTD characteristics, and the soundness of the LSTD model. Two classical car-following models (i.e. the intelligent driver model, Gipps' model) are integrated with the LSTD model, and the integrated models show a promising performance as the errors decrease by 36.7% and 35.7%, respectively.<p /> <p>Language: en</p>",
language="en",
issn="0968-090X",
doi="10.1016/j.trc.2020.102698",
url="http://dx.doi.org/10.1016/j.trc.2020.102698"
}