Assessment of Car-Following Models Using Field Data

Abstract

Car-following models are important components of simulation tools, since they describe the behavior of the following vehicle as a function of the lead vehicle trajectory. Several models have been developed and evaluated using field data. However, the literature has not reported the applicability of various car-following models under different operational conditions such as congested vs. non-congested, and for different driver types. The objective of this study was to assess car-following models using field data under different conditions and driver types. After review, Gipps (component of AIMSUN software), Pitt (use in CORSIM software), MITSIM (utilize in MIT simulation program), and Modified Pitt models were selected. The database used in the analysis was collected by cameras installed in a vehicle and consists of video recordings of the speed, time and distances between the subject vehicle and its surrounding vehicles. Congested, uncongested, and rain with/without congestion were evaluated. Trajectories from participants with different driving behavior (aggressive, average, and conservative) were obtained. Their field trajectories were compared to the trajectories obtained by each of the models evaluated. Results showed that the variable best replicated by the models was speed however it is recommended to perform calibrations based on spacing. Three calibration analyses were completed: first using all the data, second by traffic condition, and third by driver type. Results showed that the best results were obtained when the parameters were calibrated by driver type using MITSIM model. The study concluded with recommended calibration parameters, and application guidelines related to the car-following models examined.