Synthetic population and urban mobility modeling using open and publicly available data - a Washington, DC COVID case study

Abstract

Human behavior changes especially when facilitated and amplified by policy changes during situations such as the COVID pandemic can have far reaching consequences for urban mobility. We have seen orphaned roads and empty metro cars during times when “rush hour” should have been the norm. To better comprehend these phenomena, this work uses Agent-based modeling (ABM), specifically the MATSim framework, in combination with a wealth of publicly available data (CENSUS and Openstreetmap - OSM) to model pre- and during COVID urban mobility for the Washington, DC metro area. The available CENSUS data combined with population generation algorithms and MATSim allows us to model a population of four million people and their daily mobility patterns on a multimodal transportation network that includes a road network, the metro system, and a number of bus services in the Washington, DC metro area. In comparing the simulation output with ground-truth flows, we show that indeed our approach is capable to accurately capturing traffic flows in this multimodal network as observed before and during the COVID pandemic. Overall, this work demonstrates that publicly available population data (CENSUS) and transportation infrastructure and POI data (OSM) can be leveraged in a powerful simulation framework to accurately model urban mobility. Example scenarios could be the evaluation of future policy proposals as well as infrastructure projects that leverage mobility patterns.

Keywords

Digital twin / Urban mobility / Microsimulation / Agent-based modeling

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