Traffic simulation models can represent real-world conditions such as delays, travel times, queues, and flows. However, accurate evaluation of these traffic conditions depends on the selection of model parameters and the calibration methodology. Most previous calibration studies have focused on minimizing the sum of the differences between the observed data and simulation output during a certain time period on a typical day. However, to capture a realistic distribution of all possible traffic conditions, a more general calibration methodology that can be used with any traffic condition is required. This paper proposes a new calibration methodology-the Bayesian sampling approach in conjunction with the application of the simultaneous perturbation stochastic approximation (SPSA) optimization method [enhanced SPSA (E-SPSA)]-that enables the modeler to enhance calibration by considering statistical data distribution. Instead of calibrating with input data for certain time periods, calibration is performed with data obtained from a complete input distribution. Mean square variation (MSV) was used to evaluate the accuracy of the proposed E-SPSA calibration approach. On the basis of the MSV of flows, the MSV value of the E-SPSA methodology was found to be 0.940, which was greater than the variation of speed obtained from SPSA-only (0.897) or from a variation approach (0.888). Thus, this proposed methodology not only makes it possible to overcome some of the limitations of previous calibration approaches, but also improves the results of simulation model calibration by accurately capturing a wide range of real-world conditions. Future work will focus on testing the proposed calibration methodology using more extensive data sets and models developed using simulation tools other than PARAMICS.
ASJC Scopus subject areas
- Civil and Structural Engineering
- Mechanical Engineering