A City-level High-performance Spatio-temporal Mobility Simulation System.

Urban mobility simulation refers to simulating human fine-grained spatio-temporal mobility and activity behaviors in cities, which facilitates the measurement of traffic operations, assesses the impacts of transportation on other areas of the city like environment, and supports the designation of simulation-driven mobility-related sustainable policies. It is becoming one of the important tools in the development of sustainable cities. However, the main challenge currently restricting the mobility simulation and its applications is poor performance when dealing with city-scale million or even ten million people simulation. In mobility simulation, agents like people and vehicles need to take actions based on the previous step's states of other agents nearby, which reflects the spatio-temporal dependencies among agents. Facing city-scale scenarios, the spatio-temporal dependencies become the main barrier to achieving efficient parallel computation acceleration. To alleviate the impact of spatio-temporal dependencies on parallel acceleration, we propose a city-level high-performance spatio-temporal mobility simulation system designed with a two-stage parallel process based on read/write separation and a parallel-friendly indexing subsystem. The two-stage parallel process optimizes cross-step state read/write processes among agents by reorganizing all states into three categories (public read-only, public write-only, and private) and introducing a two-stage control flow design to divide the entire data flow into two easily parallelized groups. The indexing subsystem optimizes spatial belonging relationship maintenance and relative location queries through parallel-friendly data structure selection with addition, deletion, and query process design. We implement the whole system on both CPU and GPU to adapt to different hardware environments. We also conduct extensive experiments and build use cases to demonstrate that the system achieves the expected results in terms of performance and can support innovative applications about sustainable mobility research. The experiments show that our proposed system achieves a computational speedup of 278.77 times the wall clock time, i.e. 3.59 milliseconds per step, in a city-level simulation with nearly 1 million people simultaneously.