Parallel-Computing-Based Calibration for Microscopic Traffic Simulation Model

Abstract

Microscopic traffic simulation is vital to assess the performances of various traffic operation and management schemes. Microscopic traffic simulation is usually not parameter-free, and it relies on independent parameters to predict traffic evolution. Thus, parameter calibration is indispensable to conveying trustworthy simulation results. Heuristic algorithms are widely used for parameter calibration. Its logic is for achieving iterative optimization through continuous trial-and-error simulations. This process is time-consuming and usually takes several hours, making the calibration unable to meet the requirements of speed and efficiency. In recent years, parallel computing technology has been gradually applied in the engineering realm, which makes rapid calibration possible. Following the three steps of parallel framework selection, algorithm bottleneck identification, and subtask load balancing, this paper designs and implements the parallelization of genetic algorithm and particle swarm optimization (PSO) calibration algorithms. Finally, the proposed parallel framework is applied to simulation parameter calibration of a section of a 5 km long highway in Australia, and the effectiveness of parallel computing is evaluated from the two dimensions of reduction in calibration computational time and scalability. The results show that the proposed parallel calibration algorithm can shorten the 5 h calibration process to less than 1 h, reducing the calibration time by 80%. The parallel PSO calibration algorithm has better scalability, and its acceleration effect is better when more processors are used.