Potential and pitfalls of big transport data for spatial interaction models of urban mobility

Abstract

Massive amounts of data that characterize how people meet their economic needs, interact within social communities, and utilize shared resources are being produced by cities. Harnessing these ever-increasing data streams is crucial for understanding urban dynamics. Within the context of transportation modeling it still remains largely unknown whether or not these new data sources provide the opportunity to better understand spatial processes. Therefore, in this paper, the usefulness of a recently available big transport dataset -the New York City (NYC) taxi trip data -is evaluated within a spatial interaction modeling framework. This is done by first comparing parameter estimates from a model using the taxi data to parameter estimates from a model using a traditional commuting dataset. In addition, the high temporal resolution of the taxi data provide an exciting means to explore potential dynamics in movement behavior. It is demonstrated how parameter estimates can be obtained for temporal subsets of data and compared over time to investigate mobility dynamics. The results of this work indicate that a pitfall of big transport data is that it is less useful for modeling distinct phenomena; however, there is a strong potential for modeling high frequency temporal dynamics of diverse urban activities.