Large-scale traffic simulations at a microscopic level can mimic the physical reality in great detail so that innovative transport services can be evaluated. However, the simulation times of such scenarios is currently too long to be practical. (1) Background: With the availability of Graphical Processing Units (GPUs), is it possible to exploit parallel computing to reduce the simulation times of large microscopic simulations, such that they can run on normal PCs at reasonable runtimes?; (2) Methods: ParSim, a microsimulator with a monolithic microsimulation kernel, has been developed for CUDA-compatible GPUs, with the aim to efficiently parallelize the simulation processes; particular care has been taken regarding the memory usage and thread synchronization, and visualization software has been optionally added; (3) Results: The parallelized simulations have been performed by a GPU with an average performance, a 24 h microsimulation scenario for Bologna with 1 million trips was completed in 40 s. The average speeds and waiting times are similar to the results from an established microsimulator (SUMO), but the execution time is up to 5000 times faster with respect to SUMO; the 28 million trips of the 24 h San Francisco Bay Area scenario was completed in 26 min. With cutting-edge GPUs, the simulation speed can possibly be further reduced by a factor of seven; (4) Conclusions: The parallelized simulator presented in this paper can perform large-scale microsimulations in a reasonable time on readily available and inexpensive computer hardware. This means microsimulations could now be used in new application fields such as activity-based demand generation, reinforced AI learning, traffic forecasting, or crisis response management.

Large scale traffic simulations at a microscopic level can mimic the physical reality to a great detail and innovative transport services can be evaluated. However, the simulation time of such scenarios is currently too long to be practical. (1) Background: with the availability of Graphical Processing Units (GPUs), is it possible to exploit parallel computing to reduce the simulation time of large microscopic simulations such that they can run on normal PCs at reasonable run-times?; (2) Methods: ParSim, a microsimulator with a monolithic microsimulation kernel has been developed for CUDA compatible GPUs, with the aim to efficiently parallelize the simulation processes, particular care has been taken of the memory usage and thread synchronization, and visualization software has been added optionally; (3) Results: the parallelized simulations have been performed by a GPU with an average performance, the 24h microsimulation scenario of Bologna with 1 million trips has been completed in 40s. Average speeds and waiting times are similar to the results from an established microsimulator (SUMO), but up to 5,000 times faster; the 28 million trips of the 24h San Francisco Bay Area scenario has been completed in 26min. With cutting edge GPUs the simulation speed can possibly be reduced by a factor of 7. (4) Conclusions: the parallelized simulator presented in this paper can perform large scale microsimulation in a reasonable time on available and inexpensive computer hardware. This means microsimulations could now be used in new application fields such as activity-based demand generation, reinforced AI learning, traffic forecasting or crises response management.