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Presented in this paper are the car-following methods and algorithms of the NETSIM, INTRAS, FRESIM, CARSIM, and INTELSIM models. Moreover, the car-following performance of these models is compared with the field data. NETSIM, INTRAS, FRESIM, and CARSIM car-following models first move the leader and then update the follower in one simulation time step. Because of this approach, these car-following models cannot be used to command vehicles in real-time intelligent transportation systems applications. Moreover, brake reaction times are limited by the simulation time step because of this method of updating the vehicles. INTELSIM was developed to overcome these deficiencies. INTELSIM moves vehicles simultaneously and produces solutions for a continuous time frame. INTELSIM produced the best agreement with the field data and required the least amount of calibration effort.
Aycin et al. (Fri,) studied this question.
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