Effects of population sampling on agent-based transport simulation of on-demand services

Abstract

Large-scale agent-based simulations require higher computing resources than are usually available. Consequently, many applications rely on downscaling, that is, simulating with smaller population samples in which the results are then scaled. Existing studies have shown a need to investigate the impact of downscaling on the output statistics of such simulations. Downscaling is a common practice in transport modeling. In this study, we investigate the impacts of population downscaling on a ride-sharing service with a focus on vehicle occupancy and wait time, travel time and detour time. Our findings reveal that if transport modelers want to model on-demand services with ride sharing, it is strongly recommended to use a 100% population, or when using a smaller population sample, to estimate the relative biases of their desired metrics compared to the results of a 100% population in order for their results to be applicable for real-world situations.