Shared Mobility and the Transformation of Public Transit (2016) / Chapter Skim
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Pages 75-78
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E-1 A P P E N D I X E Ridesourcing Demand and Transit Capacity Calculation
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E-2 Shared Mobility and the Transformation of Public Transit Overview of data collecon To collect the data, we built a set of scripts in the R and Python computer languages that did the following: 1. For each metro geography, we built files with tract-level counts of a variety of Census variables, by which we weight the random tract selecon for the next step.
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Ridesourcing Demand and Transit Capacity Calculation E-3 Combined, the two rounds of collecon produced some 1.07 million usable observaons for the study regions. Scheduled transit capacity from GTFS To determine how Uber rides corresponded with transit trips, the researchers compared the Uber data with agencies' General Transit Feed Specificaon (GTFS)
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E-4 Shared Mobility and the Transformation of Public Transit Note: Data not concurrent; TLC data covers January–June 2015, while API data was collected October–December 2015. Figure E-1.
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