Population downscaling in multi-agent transportation simulations: A review and case study

Abstract

Simulating the dynamics and evolution of metropolitan transportation systems serving millions of travelers remains a difficult task, beyond existing standard software's abilities. MATSim (Multi-Agent Transportation Simulation) is the only high-resolution spatially-explicit framework that allows intrinsic population downscaling - simulating the entire system's dynamics based only on a fraction k of the traveling population. Till now, the choice of k was dictated by hardware performance, and a common rule was not to downscale below 10%. We investigate downscaling in MATSim by comparing the aggregate and disaggregate statistics that describe the dynamics of car traffic in full-scaled and downscaled simulations of the Sioux Falls test case road network. Simulations with 25% or higher shares of the traveler population preserve all major urban traffic statistics. Within the 10–25% interval, downscaling becomes unstable for some of the statistics. For scenarios that are downscaled 10% and below, statistics can substantially deviate from the full-scale model. We further discuss the problems related to a multimodal transportation simulation's downscaling that also includes public transportation.