Empirical Analysis of a Generalized Linear Multianticipative Car-Following Model in Congested Traffic Conditions
Publication: Journal of Transportation Engineering
Volume 140, Issue 6
Abstract
This paper empirically investigates the multianticipative car-following behavior using a multiple linear regression approach. Two important aspects of the multiple linear regression of a modified generalized linear multianticipative car-following model are addressed, namely, multicollinearity between explanatory variables and the serial correlation of time series data, which have not been taken into account by previous studies. This paper analyzes the multianticipative car-following behavior using the next generation simulation (NGSIM) trajectory data. Specifically, the following issues are discussed: reaction time estimation, multicollinearity diagnosis, serial correlation test, model selection, and parameter estimation. Preliminary results show that a driver in extremely congested traffic conditions is able to react to the stimuli of the first, second, and even third leader with respect to the relative speed and the stimuli of the first or the second leader with respect to the desired space. It is reascertained that a driver is more perceptual to speed difference than to desired space behavior in extremely congested traffic conditions. Moreover, it is observed that a driver in less congested traffic conditions only reacts to the stimuli of the first leader with respect to both the relative speed and desired distance. Therefore, it is empirically believed that the stimuli perceived by a driver may be different in different traffic conditions with respect to multianticipative car-following behavior.
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Acknowledgments
Part of this work was supported by the research grant from the University of Missouri research board. Special thanks are due to NGSIM program for providing the trajectory data sets. The contents of this paper reflect the views of the author who is responsible for the facts and the accuracy of the data presented in this paper.
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Published In
Journal of Transportation Engineering
Volume 140 • Issue 6 • June 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Mar 11, 2013
Accepted: Jan 28, 2014
Published online: Mar 19, 2014
Published in print: Jun 1, 2014
Discussion open until: Aug 19, 2014
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