The mode most traveled: Transportation infrastructure implications and policy responses

Abstract

Using the United States Census Public Use Microdata Sample (PUMS) dataset, we documented the severity of the disparity in commuting pattern across the contiguous US. The analysis was complemented by a more granular analysis with the Greater Pittsburgh area as the geographic area of focus. In addition to the locational variation in travel mode obtained using population estimates derived from the PUMS dataset, the dataset was utilized for a discrete choice model that generated detailed commuting profiles for the region’s workforce, showing statistically significant differences not only by socio-economic attributes but more importantly, by commuters’ place of abode. Policy levers that could address travel mode shift are discussed primarily with regards to changing population and its impact on transportation resources and the onset of fully autonomous vehicle in transportation networking companies’ space—a subject of key topical interest given the choice of the city as the test bed for Uber’s driverless ride sourcing services.