Simulating urban flows with geographically explicit synthetic populations

Abstract

Urban human mobility is an active research field that studies movement patterns in urban areas at both the individual and aggregated population levels. Through individual’s movement, higher level phenomena such as traffic congestion and disease outbreaks emerge. Understanding how and why people move around a city plays an important role in urban planning, traffic control, and public health. An abundance of agent-based models have been built by researchers to simulate human movements in cities and are often integrated with a GIS component to realistically represent the study area. In this work we build a geographically explicit agent-based model where agents move between their home and workplaces, to simulate people’s daily commuting patterns within a city. In order to build this model, we develop a geographically explicit synthetic population based on census data. A deep learning spatial-temporal urban flow model is trained to predict the aggregated inflows and outflows within regions of the study area, which are subsequently used to drive individual agents’ movements. To validate results from the agent-based model, agents’ movements are aggregated and evaluated along with the urban flow model. Commuting statistics are also collected and compared to existing travel surveys. As such we aim to demonstrate how urban simulation models can be complemented by recent advancements in GeoAI techniques. Conversely, the aggregated deep learning model predictions can be investigated at a fine-grained individual level. This extends traffic patterns forecasting from just looking at the patterns to the processes that lead to these patterns emerging.