Forecasting Travel Supply and Demand by Modeling Multiscale Urban Environments

Abstract

Traditional microscopic transport simulation approaches usually separate the supply from the demand forecasting by modeling travel demand and the transportation network separately. This approach generally leads to a sizeable calibration task, especially when nested multiscale simulations must be conducted. A generic approach is proposed to model a virtual urban environment in a way that combines data about the population, transportation network, and locations. A scale-independent approach for modeling such an urban environment is presented, along with its application to the Quebec City, Canada, metropolitan region. Some initial results from the TransNetSIM mesoscopic simulator and some computational challenges that were addressed are presented and discussed.