Abstract

In recent years, due to the advances in computation technology, microscopic vehicular traffic simulation has become one of the main tools used by transportation professionals to solve various design and analysis problems (e.g. safety performance evaluation of highways, impact of different design scenarios in units of safety and efficiency, etc.). The effective use of any of the existing simulation models is limited by the calibration of specific parameters that are based on observed real-life conditions. However, because the calibration of the simulation models is a time consuming and resource intensive process, one might resort to using the default parameter values. In this study, a soft computing-based methodology which synergistically combines Artificial Neural Networks and Genetic Algorithm (GA) applications, is proposed as an alternative for calibration methodology that considerably reduces the computation time in comparison to other commonly used methods. First, a Latin Hypercube Sampling method is used to select representative sets of values for VISSIM’s main calibration parameters. Second, the effect of each set of parameter values on the simulated traffic stream speed is recorded. Third, a neural-network is trained to determine the relationship between the input parameter values and the output vehicular speed. Finally, a genetic-algorithm uses the trained neural-network in its fitness function to determine the appropriate set of values for the calibration parameters. The proposed methodology allows for the calibration of microscopic traffic models with fewer computational resources than is commonly used. The feasibility of the method and its applicability to real-world traffic conditions is proved by employing the model using a real-world High Occupancy Vehicle (HOV) lane along a freeway segment. The results of proposed calibration method are compared with those from GA-only based calibration method.. It is concluded that the proposed method performs faster than the GA based calibration method while maintinaing a certain level of accuracy. To highlight the potential benefits of the proposed calibration method, a before-and-after calibration conflict analysis is presented. It is recommended to apply the proposed method to urban environments and to consider other performance measures (travel time, queue length, etc.) to investigate proposed method’s generality.