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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
These figures including the subsequent ones for states and cities were all obtained by generating population estimates from the microdata sample set from the US Census using (2015) data.
- 2.
A PUM area is a geographically designated enumeration unit by the US Census with a population in excess of 100,000 but below 200,000 residents.
- 3.
The North Hills refer collectively to Pittsburgh’s northern suburbs and is made up of approximately 40 townships and boroughs.
References
Bhat, C., & Sen, S. (2006). Household vehicle type holdings and usage: An application of the multiple discrete-continuous extreme value (MDCEV) model. Transportation Research Part B, 40(1), 35–53.
Blumenberg, E., & Shiki, K. (2007). Transportation assimilation: Immigrants, race and ethnicity, and mode choice. In86th Annual Meeting of the Transportation Research Board.
Fabusuyi, T., & Hampshire, R. (2013). Needs assessment for the integrated parking application project. Pittsburgh: Carnegie Mellon University/University of Pennsylvania University Transportation Center.
Fabusuyi, T., Hampshire, R. C., & Hill, V. (2013). Evaluation of a smart parking system. Transportation Research Record: Journal of the Transportation Research Board, 2359(1), 10–16.
Fabusuyi, T., Hampshire, R., Hill, V., & Sasanuma, K. (2014). Decision analytics for parking availability in downtown Pittsburgh. Interfaces, 44(3), 286–299.
Fay, R. (1995). VPLX: Variance estimation for complex surveys. Bureau of US Census: Program Documentation.
Ferguson, E. (1997). The rise and fall of the American carpool: 1970–1990. Transportation, 24, 349–376.
Haas, P., Makarewicz, C., Benedict, A., Sanchez, T., & Dawkins, C. (2006). Housing and transportation cost trade-offs and burdens of working households in 28 Metros. Chicago, IL: Center for Neighborhood Technology.
Hu, L., & Giuliano, G. (2011, January 23–27). Beyond the inner city: A new form of spatial mismatch. In Proceedings of the 90th Annual Meeting of the Transportation Research Board.
Krizek, K., Johnson, P., & Tilahun, N. (2004, November 18–20). Gender differences in bicycling behavior and facility preferences. In Conference Proceedings 35 Research on Women’s Issues in Transportation.
Long, S., & Freese, J. (2014). Regression models for categorical dependent variables using stata (Third ed.). College Station, TX: Stata Press.
Manski, C. F., & Lerman, S. R. (1977). The estimation of choice probabilities from choice based samples. Econometrica: Journal of the Econometric Society, 45(8), 1977–1988.
McFadden, D. (1974). The measurement of urban travel demand. Journal of Public Economics, 3(4), 303–328.
Thakuriah, V., Sriraj, P., Soot, S., Liao, Y., & Berman, G. (2005). Activity and travel changes of job access transportation service users: Analysis of a user survey. Transportation Resarch Record: Journal of the Transportation Research Board, 1927, 55–62.
U.S. Census Bureau. (2014). Quarterly Workforce Indicators Data. Longitudinal-Employer Household Dynamics Program. Retrieved from http://lehd.ces.census.gov/data/#qwi
United States Census Bureau. (2015). Public use microdata sample (PUMS) dataset. Retrieved from http://www.census.gov/acs/www/data_documentation/pums_data/
United States Census Bureau Center for Economic Studies. (2015). Longitudinal Employer-Household Dynamics. Retrieved May 13, 2014 from http://lehd.ces.census.gov/
Vrieze, S. I. (2012). Model selection and psychological theory: A discussion of the differences between the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Psychological Methods, 17, 228–243.
Waddell, P., Outwater, M., Bhat, C., & Blain, L. (2002). Design of an integrated land use and activity-based travel model system for the puget sound region. Transportation Research Record: Journal of the Transportation Research Board, 1805, 108–118.
Weinberger, R. (2007). Men, women, job sprawl and journey to work in the Philadelphia Region. Public Works Management and Policy, 11(3), 177–193.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Fabusuyi, T., Hampshire, R.C. (2017). The Mode Most Traveled: Transportation Infrastructure Implications and Policy Responses. In: Geertman, S., Allan, A., Pettit, C., Stillwell, J. (eds) Planning Support Science for Smarter Urban Futures. CUPUM 2017. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-57819-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-57819-4_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57818-7
Online ISBN: 978-3-319-57819-4
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)