QarSUMO: A Parallel, Congestion-optimized Traffic Simulator

TL;DR

QarSUMO is a parallel, congestion-optimized traffic simulator built on SUMO, designed to reduce simulation time in congested scenarios while maintaining compatibility and accuracy, aiding urban planning and reinforcement learning tasks.

Contribution

It introduces a parallelization framework for SUMO that improves efficiency in congested traffic scenarios and modifies the engine for better performance without sacrificing accuracy.

Findings

QarSUMO significantly reduces simulation time in congested scenarios.

It maintains high accuracy compared to original SUMO.

The framework is compatible with future SUMO updates.

Abstract

Traffic simulators are important tools for tasks such as urban planning and transportation management. Microscopic simulators allow per-vehicle movement simulation, but require longer simulation time. The simulation overhead is exacerbated when there is traffic congestion and most vehicles move slowly. This in particular hurts the productivity of emerging urban computing studies based on reinforcement learning, where traffic simulations are heavily and repeatedly used for designing policies to optimize traffic related tasks. In this paper, we develop QarSUMO, a parallel, congestion-optimized version of the popular SUMO open-source traffic simulator. QarSUMO performs high-level parallelization on top of SUMO, to utilize powerful multi-core servers and enables future extension to multi-node parallel simulation if necessary. The proposed design, while partly sacrificing speedup, makes QarSUMO compatible with future SUMO improvements. We further contribute such an improvement by modifying the SUMO simulation engine for congestion scenarios where the update computation of consecutive and slow-moving vehicles can be simplified. We evaluate QarSUMO with both real-world and synthetic road network and traffic data, and examine its execution time as well as simulation accuracy relative to the original, sequential SUMO.