Agent-Based Model (ABM) for City-Scale Traffic Simulation: A Case Study on San Francisco

Abstract

Agent-Based Model (ABM) is a promising tool for city-scale traffic simulation to understand the complex behaviour of the entire urban transportation system under different scenarios. In the ABM, traffic is intuitively simulated as movements and interactions between large numbers of agents, each capable of finding the route for an individual traveller or vehicle. This paper presents such an ABM development to reproduce the traffic patterns of the city of San Francisco. The model features a detailed road network and hour-long simulation time step to capture realistic variations in traffic conditions. Agent speed is determined according to a simplified volume-delay macroscopic relationship, which is more efficient than applying microscopic rules (e.g., car following) for evaluating city-scale traffic conditions. Two particular challenges of building such an ABM are addressed in this paper: data availability and computational cost. The key inputs to the ABM are sourced from standard and publicly available datasets, including the travel demand surveys published by local transport authorities and the road network data from the OpenStreetMap. In addition, an efficient priority-queue based Dijkstra algorithm is implemented to overcome the computational bottleneck of agent routing. The ABM is designed to run on High Performance Computing (HPC) clusters, thereby improving the computational speed significantly. Preliminary validation of the ABM is conducted by comparing its results with a published model. Overall, the ABM has been demonstrated to run efficiently and produce reliable results.