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.
A linear acceleration car-following model has been developed for realistic simulation of traffic flow in intelligent transportation systems (ITS) applications. The new model provides continuous acceleration profiles instead of the stepwise profiles that are currently used. The brake reaction times of the drivers are simulated effectively and are independent of the simulation time steps. Chain-reaction times of the drivers are also simulated and perception thresholds are incorporated in the model. The preferred time headways are utilized to determine the simulated drivers’ separation during car-following. The features of the model and the realistic vehicle simulation in car-following and in stop-and-go conditions make this model suitable to ITS, especially to autonomous intelligent cruise-control systems. The car-following algorithm is validated at microscopic and macroscopic levels by using field data. Simulated versus field trajectories and statistical tests show very strong agreement between simulation results and field data.
Truck weight enforcement helps protect infrastructure and improve traffic safety. However, requiring all trucks to stop at all weigh stations reduces productivity and increases enforcement costs. This pilot study was conducted at the Williamsville weigh station in Springfield, Ill., to quantify the delay and traffic conflicts experienced by trucks around weigh stations. The quantification is needed to evaluate the effectiveness of automatic vehicle identification/weigh in motion (AVI/WIM) systems for electronic screening of trucks at weigh stations. The main emphasis of this project is to examine potential benefits of intelligent transportation system (ITS) technologies for intrastate commercial vehicle operations (CVO) applications. Reducing the number of traffic conflict incidents due to the merging and diverging movements of trucks would increase traffic safety. In the case study, without the AVI/WIM system, on the average, 30 percent of all trucks could not be weighed because the weigh station was temporarily closed to prevent a queue backup. The average delay was 4.95 min/truck and varied from 3.56 to 6.59 min/truck. The maximum delay varied from 8.69 to 137.62 min/truck. There were significant numbers of conflicts even under light-volume conditions. Models to predict the number of conflicts were developed. The number of diverge conflicts depends on car and truck volumes, and the number of merge conflicts depends on the truck volume on the ramp and the car volume in the right and middle lanes of a six-lane interstate.
Currently, the Highway Capacity Manual does not provide any tools for the analysis of toll plazas, specifically for the calculation of queue delay. Calculating delay experienced by vehicles in the plaza is a difficult task because of large variations in plaza design and operations; therefore, various simulation models such as TPSIM, WATSim, AIMSUN, and VISSIM are used extensively for toll plaza modeling. However, the performance of these models depends on their calibration and assumptions. As a result, the model results could differ greatly from reality, and the user would not have a means to evaluate their accuracy in the absence of field data. To solve this problem, a methodology has been developed to determine capacity, queuing patterns, and delays of toll plazas by considering the approach roadway conditions and traffic demand characteristics. This methodology, which is suitable for manual calculation, is suggested to improve users’ understanding of toll plaza operations and to provide a means of evaluating simulation results. The application of this methodology to the Throgs Neck Bridge toll plaza in New York City shows that the queuing pattern and delays can be estimated accurately for the a.m. peak period.
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