Empirical Analysis of a Generalized Linear Multianticipative Car-Following Model in Congested Traffic Conditions

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.