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An exploratory analysis of alternative travel behaviors of ride-hailing users

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Abstract

The emergence of ride-hailing, technology-enabled on-demand services such as Uber and Lyft, has arguably impacted the daily travel behavior of users. This study analyzes the travel behavior of ride-hailing users first from conventional person- and trip-based perspectives and then from an activity-based approach that uses tours and activity patterns as basic units of analysis. While tours by definition are more easily identified and classified, daily patterns theoretically better represent overall travel behavior but are simultaneously more difficult to explain. We thus consider basic descriptive analyses for tours and a more elaborate approach, Latent Class Analysis, to describe pattern behavior. The empirical results for tours using data from the 2017 National Household Travel Survey show that 76% of ride-hailing tours can be represented by five dominant tour types with non-work tours being the most frequent. The Latent Class model suggests that the ride-hailing users can be divided into four distinct classes, each with a representative activity-travel pattern defining ride-hailing usage. Class 1 was composed of younger, employed people who used ride-hailing to commute to work. Single, older individuals comprised Class 2 and used ride-hailing for midday maintenance activities. Class 3 represented younger, employed individuals who used ride-hailing for discretionary purposes in the evening. Last, Class 4 members used ride-hailing for mode change purposes. Since each identified class has different activity-travel patterns, they will show different responses to policy directives. The results can assist ride-hailing operators in addressing evolving travel needs as users respond to various policy constraints.

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References

  • Akaike, H.: information theory and an extension of the maximum likelihood principle. In: Petrov, B., Csake,f. (eds.), Second International Symposium on Information Theory, Akademiai Kiado, Budapest, Hungary. 267–281 (1973)

  • Alemi, F., Circella, G., Handy, S., Mokhtarian, P.: What influences travelers to use Uber? Exploring the factors affecting the adoption of on-demand ride services in California. Travel Behav. Soc. 13, 88–104 (2018a)

    Article  Google Scholar 

  • Alemi, F., Circella, G., Mokhtarian, P., Handy, S.: Exploring the latent constructs behind the use of ridehailing in California. J. Choice Model. 29, 47–62 (2018b)

    Article  Google Scholar 

  • Alonso-González, M.J., Hoogendoorn-Lanser, S., van Oort, N., Cats, O., Hoogendoorn, S.: Drivers and barriers in adopting mobility as a service (MaaS)—a latent class cluster analysis of attitudes. Transp. Res. A 132, 378–401 (2020)

    Google Scholar 

  • Beckman, J.D., Goulias, K.G.: Immigration, residential location, car ownership, and commuting behavior: a multivariate latent class analysis from California. Transportation 35(5), 655–671 (2008)

    Article  Google Scholar 

  • Beer, R., Brakewood, C., Rahman, S., Viscardi, J.: Qualitative analysis of ride-hailing regulations in major American cities. Transp. Res. Rec. 2650(1), 84–91 (2017)

    Article  Google Scholar 

  • Brown, A.: Redefining car access: Ride-hail travel and use in Los Angeles. J. Am. Plan. Assoc. 85(2), 83–95 (2019)

    Article  Google Scholar 

  • Clewlow, R.R., Mishra, G.S.: Disruptive transportation: the adoption, utilization, and impacts of ride-hailing in the United States. Institute of Transportation Studies, University of California, Davis. Report UCD-ITS-RR-17–07, 1–38 (2017)

  • Contreras, S.D., Paz, A.: The effects of ride-hailing companies on the taxicab industry in Las Vegas. Nevada. Transp. Res. A 115, 63–70 (2018)

    Google Scholar 

  • Conway, M.W., Salon, D., King, D.A.: Trends in taxi use and the advent of ridehailing, 1995–2017: Evidence from the US National Household Travel Survey. Urban Sci. 2(3), 79 (2018)

    Article  Google Scholar 

  • Cramer, J., Krueger, A.B.: Disruptive change in the taxi business: the case of uber. Am. Econ. Rev. 106(5), 177–182 (2016)

    Article  Google Scholar 

  • de Souza Silva, L.A., de Andrade, M.O., Maia, M.L.A.: How does the ride-hailing systems demand affect individual transport regulation? Res. Transp. Econ. 69, 600–606 (2018)

    Article  Google Scholar 

  • Dias, F.F., Lavieri, P.S., Garikapati, V.M., Astroza, S., Pendyala, R.M., Bhat, C.R.: A behavioral choice model of the use of car-sharing and ride-sourcing services. Transportation 44(6), 1307–1323 (2017)

    Article  Google Scholar 

  • Dias, F.F., Lavieri, P.S., Kim, T., Bhat, C.R., Pendyala, R.M.: Fusing multiple sources of data to understand ride-hailing use. Transp. Res. Rec. 2673(6), 214–224 (2019)

    Article  Google Scholar 

  • Dills, A.K., Mulholland, S.E.: Ridesharing, fatal crashes, and crime. South. Econ. J. 84(4), 965–991 (2018)

    Article  Google Scholar 

  • Flores, O., Rayle, L.: How cities use regulation for innovation: the case of Uber, Lyft and Sidecar in San Francisco. Transp. Res. Procedia 25, 3756–3768 (2017)

    Article  Google Scholar 

  • Gehrke, S.R., Felix, A., Reardon, T.G.: Substitution of ride-hailing services for more sustainable travel options in the greater Boston region. Transp. Res. Rec. 2673(1), 438–446 (2019)

    Article  Google Scholar 

  • GoGoGrandparent.: Your agent for affordable rides. https://gogograndparent.com/ (2020). Accessed 20 April 2020

  • Goodman, L.A.: The multivariate analysis of qualitative data: Interactions among multiple classifications. J. Am. Statist. Assoc. 65(329), 226–256 (1970)

    Article  Google Scholar 

  • Graehler, M., Mucci, A., Erhardt, G.D.: Understanding the recent transit ridership decline in major US cities: service cuts or emerging modes?. Presented at the Transportation Research Board 98th Annual Meeting, Washington, DC (2019)

  • Grahn, R., Harper, C.D., Hendrickson, C., Qian, Z., Matthews, H.S.: Socioeconomic and usage characteristics of transportation network company (TNC) riders. Transportation 1–21 (2019)

  • Hall, J.D., Palsson, C., Price, J.: Is Uber a substitute or complement for public transit? J. Urb. Econ. 108, 36–50 (2018)

    Article  Google Scholar 

  • Henao, A., Marshall, W.E.: The impact of ride-hailing on vehicle miles traveled. Transportation 46(6), 2173–2194 (2019a)

    Article  Google Scholar 

  • Henao, A., Marshall, W.E.: The impact of ride hailing on parking (and vice versa). J. Transp. Land Use 12(1), 127–147 (2019b)

    Article  Google Scholar 

  • Henao, A.: Impacts of ridesourcing—LYFT and UBER—on transportation including VMT, mode replacement, parking and travel behavior. Ph.D. Thesis, University of Colorado (2017)

  • Ho, C.Q., Mulley, C.: Multiple purposes at single destination: a key to a better understanding of the relationship between tour complexity and mode choice. Transp. Res. A 49, 206–219 (2013)

    Google Scholar 

  • Kolanko, D., Gallinger, Z.: How much do you save by using uber? http://www.the10and3.com/how-much-do-you-save-by-using-uber/ (2015). Accessed 20 April 2020

  • Komanduri, A., Wafa, Z., Proussaloglou, K., Jacobs, S.: Assessing the impact of app-based ride share systems in an urban context: findings from Austin. Transp. Res. Rec. 2672(7), 34–46 (2018)

    Article  Google Scholar 

  • Kooti, F., Grbovic, M., Aiello, L.M., Djuric, N., Radosavljevic, V., Lerman, K.: Analyzing Uber's ride-sharing economy. In: Proceedings of the 26th International Conference on World Wide Web Companion 574–582 (2017)

  • Lanza, S.T., Rhoades, B.L.: Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prevent. Sci. 14(2), 157–168 (2013)

    Article  Google Scholar 

  • Lavieri, P.S., Bhat, C.R.: Investigating objective and subjective factors influencing the adoption, frequency, and characteristics of ride-hailing trips. Transp. Res. C 105, 100–125 (2019)

    Article  Google Scholar 

  • Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? A latent class cluster analysis with attitudes and preferences among millennial and generation X commuters in California. Transportation 1–24 (2019)

  • Leistner, D.L., Steiner, R.L.: Uber for seniors?: exploring transportation options for the future. Transp. Res. Rec. 2660(1), 22–29 (2017)

    Article  Google Scholar 

  • Li, W., Pu, Z., Li, Y., Ban, X.J.: Characterization of ridesplitting based on observed data: a case study of Chengdu. China. Transp. Res. C 100, 330–353 (2019)

    Article  Google Scholar 

  • Liao, F.H., Farber, S., Ewing, R.: Compact development and preference heterogeneity in residential location choice behaviour: a latent class analysis. Urban Stud. 52(2), 314–337 (2015)

    Article  Google Scholar 

  • Linzer, D.A., Lewis, J.B.: poLCA: an R package for polytomous variable latent class analysis. J. Statist. Softw. 42(10), 1–29 (2011)

    Article  Google Scholar 

  • Lynott, J., Figueiredo, C.: How the travel patterns of older adults are changing: highlights from the 2009 national household travel survey (No. Fact Sheet 218). AARP Public Policy Institute (2011)

  • Magidson, J., Vermunt, J.: Latent class models for clustering: a comparison with K-means. Canadian J. Mark. Res. 20(1), 36–43 (2002)

    Google Scholar 

  • Molin, E., Mokhtarian, P., Kroesen, M.: Multimodal travel groups and attitudes: a latent class cluster analysis of Dutch travelers. Transp. Res. A 83, 14–29 (2016)

    Google Scholar 

  • Nair, G.S., Bhat, C.R., Batur, I., Pendyala, R.M., Lam, W.H.: A model of deadheading trips and pick-up locations for ride-hailing service vehicles. Transp. Res. A 135, 289–308 (2020)

    Google Scholar 

  • Oberski, D.: Mixture models: latent profile and latent class analysis. Modern statistical methods for HCI, pp. 275–287. Springer, Cham (2016)

    Google Scholar 

  • Rafiq, R., McNally, M.G.: A study of tour formation: pre-, during, and post-recession analysis. Transportation 1–47 (2020a). https://doi.org/10.1007/s11116-020-10126-8

  • Rafiq, R., McNally, M.G.: An empirical analysis and policy implications of work tours utilizing public transit. Transp. Res. A 142, 237–259 (2020b)

    Google Scholar 

  • Rafiq, R., McNally, M.G.: Heterogeneity in activity-travel patterns of public transit users. Transp. Res. A 152, 1–18 (2021)

    Google Scholar 

  • Ralph, K.M.: Multimodal millennials? The four traveler types of young people in the United States in 2009. J. Plan. Educ. Res. 37(2), 150–163 (2017)

    Article  Google Scholar 

  • Rayle, L., Dai, D., Chan, N., Cervero, R., Shaheen, S.: Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco. Transp. Policy 45, 168–178 (2016)

    Article  Google Scholar 

  • Schneider, F., Ton, D., Zomer, L.B., Daamen, W., Duives, D., Hoogendoorn-Lanser, S., Hoogendoorn, S.: Trip chain complexity: a comparison among latent classes of daily mobility patterns. Transportation 1–23 (2020)

  • Schneider, T.: Analyzing 1.1 Billion NYC taxi and uber trips, with a vengeance. https://toddwschneider.com/posts/analyzing-1-1-billion-nyc-taxi-and-uber-trips-with-a-vengeance/#update-2017 (2018). Accessed 17 April 2020

  • Schwarz, G.: Estimating the dimension of a model. Ann. Statist. 6(2), 461–464 (1978)

    Article  Google Scholar 

  • SFMTA.: Taxis and Accessible Services Division: Status of the Taxi Industry. San Franciso Municipal Transportation Agency (2014)

  • Shirgaokar, M.: Expanding seniors’ mobility through phone apps: potential responses from the private and public sectors. J. Plan. Educ. Res. 0739456X18769133 (2018)

  • Sikder, S.: Who uses ride-hailing services in the United States? Transp. Res. Rec. 2673(12), 40–54 (2019)

    Article  Google Scholar 

  • Smith, A.S.: Collaborative and on demand: the new digital economy. Pew Research Center, Washington, D.C. https://www.pewresearch.org/internet/2016/05/19/the-new-digital-economy/ (2016). Accessed 20 April 2020

  • Taylor, B.D., Chin, R., Melanie, C., Dill, J., Hoel, L.A., Manville, M., Steve, P., et al.: Between public and private mobility: examining the rise of technology-enabled transportation services. Special Report 319. Transportation Research Board: Committee for Review of Innovative Urban Mobility Services (2015)

  • Tirachini, A., del Río, M.: Ride-hailing in Santiago de Chile: Users’ characterisation and effects on travel behaviour. Transp. Policy 82, 46–57 (2019)

    Article  Google Scholar 

  • Tirachini, A.: Ride-hailing, travel behaviour and sustainable mobility: an international review. Transportation 1–37 (2019)

  • Uber.: Accessibility at Uber. https://www.uber.com/us/en/about/accessibility/ (2020). Accessed 20 April 2020

  • Vivoda, J.M., Harmon, A.C., Babulal, G.M., Zikmund-Fisher, B.J.: E-hail (rideshare) knowledge, use, reliance, and future expectations among older adults. Transp. Res. F 55, 426–434 (2018)

    Article  Google Scholar 

  • Wadud, Z.: An examination of the effects of ride-hailing services on airport parking demand. J. Air Transp. Manag. 84, 101783 (2020)

    Article  Google Scholar 

  • Wang, F., Ross, C.L.: New potential for multimodal connection: exploring the relationship between taxi and transit in New York City (NYC). Transportation 46(3), 1051–1072 (2019)

    Article  Google Scholar 

  • Young, M., Farber, S.: The who, why, and when of Uber and other ride-hailing trips: an examination of a large sample household travel survey. Transp. Res. A 119, 383–392 (2019)

    Google Scholar 

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Acknowledgements

A preliminary version of portions of this paper was presented at the 2020 TRB Annual Meeting (paper number 20-02752).

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Authors and Affiliations

  1. Institute of Transportation Studies (ITS), University of California, Irvine, CA, 92697-3600, USA

    Rezwana Rafiq

  2. Department of Civil and Environmental Engineering, Institute of Transportation Studies (ITS), University of California, Irvine, CA, 92697-3600, USA

    Michael G. McNally

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The authors confirm contribution to the paper as follows: study conception and design: RR, MGM; data processing: RR; analysis and interpretation of results: RR, MGM; draft manuscript preparation: RR, MGM. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Rezwana Rafiq.

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Rafiq, R., McNally, M.G. An exploratory analysis of alternative travel behaviors of ride-hailing users. Transportation 50, 571–605 (2023). https://doi.org/10.1007/s11116-021-10254-9

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