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

Authors: Taylor Oshan*, University of Maryland, College Park
Topics: Spatial Analysis & Modeling, Transportation Geography, Geographic Information Science and Systems
Keywords: spatial interaction, movement, statistics, spatial analysis, big data, transportation
Session Type: Paper
Day: 4/4/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: 8228, Park Tower Suites, Marriott, Lobby Level
Presentation File: No File Uploaded

As urban populations swell, massive amounts of data that characterize how people meet their economic needs, interact within social communities, and utilize shared resources such as transportation infrastructure are being made available. Harnessing the ever-increasing streams of data being produced by cities is crucial for obtaining an understanding of the drivers of dynamic urban activities. However, big data do not necessarily imply an increase in knowledge or understanding and in 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 NYC 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. As a result, it is demonstrated here 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 while a pitfall of big transport data is that it is less useful for modeling distinct phenomena such as commuting, there is a strong potential for modeling the high frequency temporal dynamics of diverse urban activities

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