Examining the effects of proximity to rail transit on travel to non-work destinations: Evidence from Yelp data for cities in North America and Europe

Zhiqiu Jiang

University of Virginia

Andrew Mondschein

University of Virginia

DOI: https://doi.org/10.5198/jtlu.2019.1409

Keywords: Rail Transit, Social Media, Accessibility, Travel Experience, Transit-Oriented Development


Abstract

Urban planners often seek to establish land use patterns around transit stations that encourage non-auto travel. However, the willingness of travelers to use different modes in the vicinity of transit remains understudied, in part because of the lack of spatially-precise data on destination and mode choices. Using transportation content extracted from Yelp, a location-based social network (LBSN), we investigate how travel mode to non-work destinations is influenced by proximity to transit. We use textual analysis to analyze travel for non-work activities in seven cities across North America and Europe. Mixed-effect and binomial logistic models show how reported travel by mode varies by distance to rail transit stations. We find that for most non-work activity purposes, reported rail use is highly sensitive to proximity to stations, but some purposes are more amenable to rail use overall. Additionally, compared to non-US cities, US cities are far more parking-dependent near rail stations. The results suggest that not all activities elicit the same levels of non-auto travel, and transit-oriented planning should account for specific activities and regional factors that may modify willingness to travel by different modes. While subject to limitations, LBSNs can illuminate local travel with greater spatial specificity than traditional surveys.

Author Biographies

Zhiqiu Jiang, University of Virginia

Department of Urban and Environmental Planning

Andrew Mondschein, University of Virginia

Department of Urban and Environmental Planning

References

Andrienko, G., Andrienko, N., Bosch, H., Ertl, T., Fuchs, G., Jankowski, P., & Thom, D. (2013). Thematic patterns in georeferenced tweets through space-time visual analytics. Computing in Science Engineering, 15(3), 72–82. doi: 10.1109/MCSE.2013.70

Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R. H. B., Singmann, H., … Green, P. (2017). lme4: Linear mixed-effects models using “Eigen” and S4 (Version 1.1-13). Retrieved from https://cran.r-project.org/web/packages/lme4/index.html

Boarnet, M. G., & Compin, N. S. (1999). Transit-oriented development in San Diego County: The incremental implementation of a planning idea. Journal of the American Planning Association, 65(1), 80–95. doi: 10.1080/01944369908976035

Boyd, D., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679. doi: 10.1080/1369118X.2012.678878

Chaniotakis, E., Antoniou, C., Aifadopoulou, G., & Dimitriou, L. (2017). Inferring activities from social media data. Transportation Research Record: Journal of the Transportation Research Board, 2666(1), 29–37. doi: 10.3141/2666-04

Chatman, D. G. (2008). Deconstructing development density: Quality, quantity and price effects on household non-work travel. Transportation Research Part A: Policy and Practice, 42(7), 1008–1030. doi: 10.1016/j.tra.2008.02.003

Chatman, D. G. (2013). Does TOD need the T? Journal of the American Planning Association, 79(1), 17–31. doi: 10.1080/01944363.2013.791008

Chen, C., Gong, H., Lawson, C., & Bialostozky, E. (2010). Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study. Transportation Research Part A: Policy and Practice, 44(10), 830–840. doi: 10.1016/j.tra.2010.08.004

Choe, Y., Kim, J., & Fesenmaier, D. R. (2017). Use of social media across the trip experience: An application of latent transition analysis. Journal of Travel & Tourism Marketing, 34(4), 431–443. doi: 10.1080/10548408.2016.1182459

Chung, N., & Koo, C. (2015). The use of social media in travel information search. Telematics and Informatics, 32(2), 215–229. doi: 10.1016/j.tele.2014.08.005

Clifton, K. J., Currans, K. M., Cutter, A. C., & Schneider, R. (2012). Household travel surveys in context-based approach for adjusting ITE trip generation rates in urban contexts. Transportation Research Record: Journal of the Transportation Research Board, 2307(1), 108–119. doi: 10.3141/2307-12

Cole, T. J. (1991). Applied logistic regression. In D. W. Hosmer & S. Lemeshow (Eds.), New York: John Wiley & Sons. https://doi.org/10.1002/sim.4780100718

Collins, C., Hasan, S., & Ukkusuri, S. (2013). A novel transit rider satisfaction metric: Rider sentiments measured from online social media data — National Center for Transit Research. Journal of Public Transportation, 16(2), 21–45.

Crampton, J. W., Graham, M., Poorthuis, A., Shelton, T., Stephens, M., Wilson, M. W., & Zook, M. (2013). Beyond the geotag: Situating ‘big data’ and leveraging the potential of the geoweb. Cartography and Geographic Information Science, 40(2), 130–139. doi: 10.1080/15230406.2013.777137

Curtin, K. M. (2007). Network analysis in geographic information science: Review, assessment, and projections. Cartography and Geographic Information Science, 34(2), 103–111. doi: 10.1559/152304007781002163

Czepiel, S. A. (2002). Maximum Likelihood Estimation of Logistic Regression Models: Theory and Implementation. Retrieved from czep. net/stat/mlelr.pdf

Dittmar, H., & Ohland, G. (2012). The new transit town: Best practices in transit-oriented development. Washington, DC: Island Press.

Dziak, J. J., Coffman, D. L., Lanza, S. T., & Li, R. (2017). Sensitivity and specificity of information criteria. Manuscript submitted for publication. doi.org/10.7287/peerj.preprints.1103v3

ESRI. (2018). ArcGIS network analyst. Retrieved from https://desktop.arcgis.com/en/arcmap/latest/extensions/network-analyst/what-is-network-analyst-.htm

Evans, L., & Saker, M. (2017). Location-based social media: Space, time and identity. New York: Springer.

Ewing, R., Tian, G., Lyons, T., & Terzano, K. (2017). Trip and parking generation at transit-oriented developments: Five US case studies. Landscape and Urban Planning, 160, 69–78. doi: 10.1016/j.landurbplan.2016.12.002

Faraway, J. J. (2016). Extending the linear model with R: Generalized linear, mixed effects and nonparametric regression models, second edition. Boca Raton, FL: CRC Press.

Forrest, T., & Pearson, D. (2005). Comparison of trip determination methods in household travel surveys enhanced by a global positioning system. Transportation Research Record: Journal of the Transportation Research Board, 1917, 63–71. doi: 10.3141/1917-08

Fox, J. (2003). Effect displays in R for generalised linear models. Journal of Statistical Software, 8(15), 1–27.

Gilbert, E., & Karahalios, K. (2009). Predicting tie strength with social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 211–220). New York: ACM. doi: 10.1145/1518701.1518736

Greenwald, M. J., & Boarnet, M. G. (2001). The built environment as a determinant of walking behavior: Analyzing non-work pedestrian travel in Portland, Oregon. Retrieved from https://escholarship.org/uc/item/9gn7265f#metrics

Gruen, V. (1964). The heart of our cities: The urban crisis, diagnosis and cure. New York: Simon and Schuster.

Guerra, E., Cervero, R., & Tischler, D. (2012). Half-mile circle. Transportation Research Record: Journal of the Transportation Research Board, 2276, 101–109. doi: 10.3141/2276-12

Hargittai, E. (2015). Is bigger always better? Potential biases of big data derived from social network sites. The ANNALS of the American Academy of Political and Social Science, 659(1), 63–76. doi: 10.1177/0002716215570866

Hargittai, E. (2018). Potential biases in big data: Omitted voices on social media. Social Science Computer Review, 089443931878832. doi: 10.1177/0894439318788322

Higuchi, K. (2012). Quantitative content analysis or text mining by KH Coder. Retrieved from https://sourceforge.net/p/khc/wiki/KWIC%20Concordance/

Higuchi, K. (2014). KH Coder (Version 2.00 beta. 32). Retrieved from http://khcoder.net/en/

Hoback, A., Anderson, S., & Dutta, U. (2008). True walking distance to transit. Transportation Planning and Technology, 31(6), 681–692. doi: 10.1080/03081060802492785

Hong, A., Boarnet, M. G., & Houston, D. (2016). New light rail transit and active travel: A longitudinal study. Transportation Research Part A: Policy and Practice, 92, 131–144. doi: 10.1016/j.tra.2016.07.005

Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. Hoboken, NJ: Wiley.

Hu, X., & Liu, H. (2012). Text analytics in social media. In Mining text data (pp. 385–414). New York: Springer. doi: 10.1007/978-1-4614-3223-4_12

Ignatow, G., & Mihalcea, R. (2016). Text mining: A guidebook for the social sciences. Thousand Oaks, CA: SAGE Publications.

Jockers, M. (2014). Text analysis with R for students of literature. New York: Springer.

Kelley, M. J. (2013). The emergent urban imaginaries of geosocial media. GeoJournal, 78(1), 181–203. doi: 10.1007/s10708-011-9439-1

King, G., & Zeng, L. (2001). Explaining rare events in international relations. International Organization, 55(3), 693–715. doi: 10.1162/00208180152507597

Kovacs-Györi, A., Ristea, A., Kolcsar, R., Resch, B., Crivellari, A., & Blaschke, T. (2018). Beyond spatial proximity—classifying parks and their visitors in London based on spatiotemporal and sentiment analysis of Twitter data. ISPRS International Journal of Geo-Information, 7(9), 378. doi: 10.3390/ijgi7090378

Krippendorff, K. (2012). Content analysis: An introduction to its methodology. Thousand Oaks, CA: SAGE.

Kurkcu, A., Ozbay, K., & Morgul, E. F. (2016). Evaluating the usability of geo-located Twitter as a tool for human activity and mobility patterns: A case study for New York City. In TRB 95th Annual Meeting Compendium of Papers. Retrieved from https://trid.trb.org/view/1393445

Lierop, D., Maat, K., & El-Geneidy, A. (2017). Talking TOD: Learning about transit-oriented development in the United States, Canada, and the Netherlands. Journal of Urbanism: International Research on Placemaking and Urban Sustainability, 10(1), 49–62. doi: 10.1080/17549175.2016.1192558

Litman, T. (2011). Evaluating accessibility for transportation planning: Measuring people’s ability to reach desired goods and activities. Victoria, BC: Victoria Transport Policy Institute.

Lüdecke, D. (2018). sjPlot-package: Data visualization for statistics in social science in sjPlot. Retrieved from https://CRAN.R-project.org/package=sjPlot

Manca, M., Boratto, L., Morell Roman, V., Martori i Gallissà, O., & Kaltenbrunner, A. (2017). Using social media to characterize urban mobility patterns: State-of-the-art survey and case-study. Online Social Networks and Media, 1, 56–69. doi: 10.1016/j.osnem.2017.04.002

Manovich, L. (2012). Trending: The promises and the challenges of big social data. In M. K. Gold (Ed.), Debates in the Digital Humanities (pp. 460–475). Minneapolis: University of Minnesota Press. doi: 10.5749/minnesota/9780816677948.003.0047

Markov, Z., & Larose, D. T. (2007). Data mining the web: Uncovering patterns in Web content, structure, and usage. Hoboken, NJ: John Wiley & Sons.

Mcculloch, C., & Neuhaus, J. (2001). Generalized linear mixed models. Hoboken, NJ: John Wiley & Sons.

Mjahed, L. B., Mittal, A., Elfar, A., Mahmassani, H. S., & Chen, Y. (2017). Exploring the role of social media platforms in informing trip planning. Transportation Research Record: Journal of the Transportation Research Board, 2666, 1–9. doi: 10.3141/2666-01

Mondschein, A. (2015). Five-star transportation: Using online activity reviews to examine mode choice to non-work destinations. Transportation, 42(4), 707–722. doi: 10.1007/s11116-015-9600-7

Munar, A. M., & Jacobsen, J. K. S. (2013). Trust and involvement in tourism social media and web-based travel information sources. Scandinavian Journal of Hospitality and Tourism, 13(1), 1–19. doi: 10.1080/15022250.2013.764511

Murray, A. T., Davis, R., Stimson, R. J., & Ferreira, L. (1998). Public transportation access. Transportation Research Part D: Transport and Environment, 3(5), 319–328. doi: 10.1016/S1361-9209(98)00010-8

Nelson, D., & Niles, J. (1999). Essentials for transit-oriented development planning: Analysis of non-work activity patterns and a method for predicting success. Proceedings of the 7th TRB Conference on the Application of Transportation Planning Methods, Boston, Massachusetts. Retrieved from http://docs.trb.org/00939750.pdf

Nikšič, M., Campagna, M., Massa, P., Caglioni, M., & Nielsen, T. (2017). Opportunities for volunteered geographic information use in spatial planning. In Mapping and the citizen sensor (pp. 327–349). London: Ubiquity Press. Retrieved from https://www.ubiquitypress.com/site/chapters/10.5334/bbf.n/

Noland, R. B., Weiner, M. D., DiPetrillo, S., & Kay, A. I. (2017). Attitudes towards transit-oriented development: Resident experiences and professional perspectives. Journal of Transport Geography, 60, 130–140. doi: 10.1016/j.jtrangeo.2017.02.015

Olszewski, P., & Wibowo, S. (2005). Using equivalent walking distance to assess pedestrian accessibility to transit stations in Singapore. Transportation Research Record: Journal of the Transportation Research Board, 1927, 38–45. doi: 10.3141/1927-05

O’Sullivan, S., & Morrall, J. (1996). Walking distances to and from light-rail transit stations. Transportation Research Record: Journal of the Transportation Research Board, 1538, 19–26. doi: 10.3141/1538-03

Quantcast. (2017). Yelp audience insights and demographic analytics. Retrieved from https://www.quantcast.com/yelp.com/demographics/WEB?country=US

R Core Team. (2017). R: A language and environment for statistical computing. Retrieved from https://www.r-project.org/

Rashidi, T. H., Abbasi, A., Maghrebi, M., Hasan, S., & Waller, T. S. (2017). Exploring the capacity of social media data for modelling travel behavior: Opportunities and challenges. Transportation Research Part C: Emerging Technologies, 75, 197–211. doi: 10.1016/j.trc.2016.12.008

Refaeilzadeh, P., Tang, L., & Liu, H. (2009). Cross-validation. In L. Liu & M. T. Özsu (Eds.), Encyclopedia of database systems (pp. 532–538). New York: Springer. doi: 10.1007/978-0-387-39940-9_565

Reilly, M. K., O’Mara, M. P., & Seto, K. C. (2009). From Bangalore to the Bay Area: Comparing transportation and activity accessibility as drivers of urban growth. Landscape and Urban Planning, 92(1), 24–33. doi: 10.1016/j.landurbplan.2009.02.001

Rissel, C., Curac, N., Greenaway, M., & Bauman, A. (2012). Physical activity associated with public transport use—a review and modelling of potential benefits. International Journal of Environmental Research and Public Health, 9(7), 2454–2478. doi: 10.3390/ijerph9072454

Rybarczyk, G., Banerjee, S., Starking-Szymanski, M. D., & Shaker, R. R. (2018). Travel and us: The impact of mode share on sentiment using geo-social media and GIS. Journal of Location Based Services, 12(1), 40–62. doi: 10.1080/17489725.2018.1468039

Sedera, D., Lokuge, S., Atapattu, M., & Gretzel, U. (2017). Likes—the key to my happiness: The moderating effect of social influence on travel experience. Information & Management, 54(6), 825–836. doi: 10.1016/j.im.2017.04.003

Senaratne, H., Mobasheri, A., Ali, A. L., Capineri, C., & Haklay, M. (2017). A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science, 31(1), 139–167. doi: 10.1080/13658816.2016.1189556

Statistics Canada. (2016). Data products, 2016 Census. Retrieved from https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/index-eng.cfm

Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics — challenges in topic discovery, data collection, and data preparation. International Journal of Information Management, 39, 156–168. doi: 10.1016/j.ijinfomgt.2017.12.002

Stopher, P., Jiang, Q., & FitzGerald, C. (2005). Processing GPS data from travel surveys. Australasian Transport Research Forum (ATRF), 28th, 2005, Sydney, New South Wales, Australia.

Stopher, P. R., & Greaves, S. P. (2007). Household travel surveys: Where are we going? Transportation Research Part A: Policy and Practice, 41(5), 367–381. doi: 10.1016/j.tra.2006.09.005

Trajdos, P., & Kurzynski, M. (2018). Weighting scheme for a pairwise multi-label classifier based on the fuzzy confusion matrix. Pattern Recognition Letters, 103, 60–67. doi:org/10.1016/j.patrec.2018.01.012

Tung, E. (2015, September 2). Automatically categorizing Yelp businesses. Retrieved from https://engineeringblog.yelp.com/2015/09/automatically-categorizing-yelp-businesses.html

U.S. Census Bureau. (2016). US Census Bureau: American community survey, 2016 5-year estimates. Suitland, MD: US Census Bureau.

Vinithra, S. N., Selvan, S. J. A., Kumar, M. A., & Soman, K. P. (2015). Simulated and self-sustained classification of Twitter data based on its sentiment. Indian Journal of Science and Technology, 8(24), 1–7. doi: 10.17485/ijst/2015/v8i24/80205

Vuchic, V. R. (2017). Urban transit: Operations, planning and economics. Hoboken, NJ: J. Wiley & Sons.

Walle, S., & Steenberghen, T. (2006). Space and time related determinants of public transport use in trip chains. Transportation Research Part A: Policy and Practice, 40(2), 151–162. doi: 10.1016/j.tra.2005.05.001

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. doi: 10.1109/TKDE.2013.109

Xiang, Z., & Gretzel, U. (2010). Role of social media in online travel information search. Tourism Management, 31(2), 179–188. doi: 10.1016/j.tourman.2009.02.016

Yelp. (2017a). API 2.0: All category list. Yelp for developers. Retrieved from https://www.yelp.com/developers/documentation/v3/all_category_list

Yelp. (2017b, January). Yelp dataset challenge. Retrieved from https://www.yelp.com/dataset_challenge

Yelp. (2017c, March). Yelp factsheet. Retrieved from https://www.yelp.com/factsheet