A high-performance traffic flow microsimulation for large problems

Abstract

Traffic flow microsimulations are interesting for transport planning problems due to their high temporal and spatial resolution. Unfortunately, most of them involve high computational costs making them impractical for running large scale scenarios. In this paper, we present how we extend our previous event-driven queue-based mircosimulation to run efficiently on parallel computers. Using appropriate load balancing and minimizing communication interfaces, we are able to simulate a test scenario involving 7 million simulated person days on a road network with 28k links in 87 seconds on 64 CPUs. Furthermore, we add support for signaled intersections that makes the model well suited for application to urban street networks. Finally, we show that our resulting model reproduces a reasonable relation between traffic flow and density similar to fundamental diagrams extracted from real world counts data.