A GPU based trafficparallel simulation module of artificial transportation systems

Abstract

Traffic micro-simulation is an important tool in the Intelligent Transportation Systems (ITS) research. In the micro-simulation, a bottom up system can be built up by the interactions of vehicle agents, road agents, traffic lights agents, etc. The Artificial societies, Computational experiments, and Parallel execution (ACP) approach suggests integrating other metropolitan systems such as logistic, infrastructure, legal and regulatory, weather and environmental systems to build an Artificial Transportation System (ATS) to help solve ITS problems. This is reasonable as the transportation system is complex that is affected by many systems interacting with each other. However, there is a challenge that the computing burden can be very heavy as there can be many agents of different kinds interacting in parallel in ATS. In recent years, the Graphics Processing Units (GPUs) have been applied successfully in many areas for parallel computing. Compared with the traditional CPU cluster, GPU has an obvious advantage of low cost of hardware and electricity consumption. In this paper, we build a parallel traffic simulation module of ATS with GPU. The simulation results are reasonable and a maximum speedup factor of 105 is obtained compared with the CPU implementations.