Microscopic traffic simulation models have been playing an important role in the evaluation of transportation engineering and planning practices for the past few decades, particularly in cases in which field implementation is difficult or expensive to conduct. To achieve high fidelity and credibility for a traffic simulation model, model calibration and validation are of utmost importance. Most calibration efforts reported in the literature have focused on the informal practice, and they have seldom proposed a systematic procedure or guideline for the calibration and validation of simulation models. This paper proposes a procedure for microscopic simulation model calibration. The validity of the proposed procedure was demonstrated by use of a case study of an actuated signalized intersection by using a widely used microscopic traffic simulation model, Verkehr in Staedten Simulation (VISSIM). The simulation results were compared with multiple days of field data to determine the performance of the calibrated model. It was found that the calibrated parameters obtained by the proposed procedure generated performance measures that were representative of the field conditions, while the simulation results obtained with the default and best-guess parameters were significantly different from the field data.

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A mobile sensor and sample-based algorithm (MOSES) to detect incidents on freeways is described. The proposed algorithm is based on statistical difference in the mean section travel time from two sets of probe vehicle samples before and during an incident. Unlike other incident detection algorithms, which operate at fixed time intervals, this sample-based algorithm is applied to detect an incident whenever a fixed sample of new probe vehicles has traversed a freeway section. The incident detection performance of MOSES at various sampling rates and probe vehicle percentages in the traffic stream has been tested on a set of data generated by a calibrated microscopic traffic simulation model. The results are compared with those of two of the most promising neural network incident detection models, which use input from stationary sensors and operate on a fixed time interval. When more than 50% of the vehicles are sampled as probes, MOSES can achieve a detection rate and false alarm rate comparable to that of the two neural network models but with faster mean time to detection and lower misclassification.