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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
