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Five-star transportation: using online activity reviews to examine mode choice to non-work destinations

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  • Andrew Mondschein

Abstract

Information and communication technologies are generating unprecedented quantities of data with potential applications in transportation planning and research. Because of their focus on non-work activities, crowdsourced activity reviews such as Yelp reviews have the potential to inform how individuals make travel choices to a range of activities with significant economic impacts. Substantial numbers of Yelp reviews include transportation content, including mode choices. I use content analysis to extract and understand statements on mode from a dataset of more than 225,000 Yelp reviews in the Phoenix metropolitan area. Spatial analysis of the results shows that access to non-work destinations varies significantly by mode across the region and within neighborhoods. The findings address ongoing questions in accessibility research, including preferences for transit around rail stations and local variability in walking preferences. Yelp data do not replace travel surveys, but they provide significantly more information and spatial detail on mode choice to many non-work destinations. Though this and similar datasets show promise for several applications in transportation planning and research, the issues of potential sampling biases and data ownership and access must also be addressed for these data to become widespread tools for practioners and researchers. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Andrew Mondschein, 2015. "Five-star transportation: using online activity reviews to examine mode choice to non-work destinations," Transportation, Springer, vol. 42(4), pages 707-722, July.
    • Handle: RePEc:kap:transp:v:42:y:2015:i:4:p:707-722
    DOI: 10.1007/s11116-015-9600-7
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    References listed on IDEAS

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    1. Ben-Elia, Eran & Alexander, Bayarma & Hubers, Christa & Ettema, Dick, 2014. "Activity fragmentation, ICT and travel: An exploratory Path Analysis of spatiotemporal interrelationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 68(C), pages 56-74.
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    Cited by:

    1. Griffin, Greg Phillip & Mulhall, Megan & Simek, Chris & Riggs, William W., 2020. "Mitigating Bias in Big Data for Transportation," SocArXiv trbv9, Center for Open Science.
    2. Han Dong & Cinzia Cirillo & Marco Diana, 2018. "Activity involvement and time spent on computers for leisure: an econometric analysis on the American Time Use Survey dataset," Transportation, Springer, vol. 45(2), pages 429-449, March.
    3. Jacqueline Arriagada & Claudio Mena & Marcela Munizaga & Daniel Schwartz, 2023. "The effect of economic incentives and cooperation messages on user participation in crowdsourced public transport technologies," Transportation, Springer, vol. 50(5), pages 1585-1612, October.
    4. Andre Carrel & Raja Sengupta & Joan L. Walker, 2017. "The San Francisco Travel Quality Study: tracking trials and tribulations of a transit taker," Transportation, Springer, vol. 44(4), pages 643-679, July.

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