Measuring Urban Travel Inequality Based on Google API Traffic Data

Authors: Len Albright*, Northeastern University, Ryan Wang, Northeastern University
Topics: Transportation Geography, Urban Geography, United States
Keywords: Traffic, Segregation, Big Data, Transportation
Session Type: Paper
Day: 4/11/2018
Start / End Time: 8:00 AM / 9:40 AM
Room: Grand Ballroom A, Astor, 2nd Floor
Presentation File: No File Uploaded


Although the relation between infrastructure development and urban isolation has been noted, the complexity has prohibited a quantitative analysis. In this exploratory project, we aim to examine urban mobility among different types of urban neighborhoods in the top U.S. cities based on the “big data” of urban informatics. The study is an attempt to shed light on the research question: How is social inequality reflected in and exacerbated by traffic reliability. We present initial findings from an analysis of the dynamic connectivity among urban neighborhoods by using real-time estimations of travel time collected from Google Maps API. We analyze traffic data from the 50 biggest cities in the United States, measuring differences in point to point auto travel times. We analyze variations in connectivity and traffic between similar and different types of neighborhoods, based on race/ethnicity and socio-economics. The analysis will help us answer the following questions: (1) Do different types (race and class) of neighborhoods share similar connectivity compared with the same types of neighborhoods? (2) How do rush hours influence the connectivity? Is the connectivity of different type of neighborhoods more influenced by rush hours or not? (3) Are there complex patterns among different cities? The research aims to explore the complex relation between social inequality and urban infrastructure by developing a quantitative understanding and theoretical analysis of the dynamic connectivity among different urban communities. Future research will test the travel cost burden associated with accessing institutions such as grocery store, banks, and education.

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