  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.
