ABSTRACT: Freeway car-following is of particular necessity because the longitudinal interaction behavior of vehicles on freeways is mainly the car-following. In order to build more accurate and realistic freeway car-following models, driving characteristics specific to freeway car following must be considered. This study therefore analyzed three car-following models calibrated for different driving styles. Almost six thousand freeway car-following events were extracted from 161,055 km of driving data collected in the Shanghai Naturalistic Driving Study (SH-NDS) database. Based on the fuzzy inference system built in this study, these car-following events were categorized as representing one of two styles: normal or aggressive. The two driving styles were visualized by using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. The Gipps, Weidemann, and intelligent driver model (IDM) car-following models were calibrated and validated for each driving style group. Using a genetic algorithm to analyze the calibrated parameters of the investigated car-following models, it was found the parameter values for each of the models were related to driving style. When their performances were evaluated, results showed that the IDM performed best, producing the smallest level of errors for both style groups. Using the IDM calibrated by the normal and aggressive driving style groups to simulate the same car-following scenarios, output results showed that the spacing gap mean values of the two styles were 38 m and 30 m, respectively; the time gap mean values were 1.7 s and 1.4 s, respectively.
Ping Sun, Xuesong Wang, Omar Hassanin, Meixin Zhu. Modeling Car-following Behavior on Freeways Considering Driving Style. Transportation Research Board 100th Annual Meeting, Washington D.C., USA, 2021. 1.25-29.