TY - JOUR
T1 - Modeling the Evolution of Ride-Hailing Adoption and Usage
T2 - A Case Study of the Puget Sound Region
AU - Dias, Felipe F.
AU - Kim, Taehooie
AU - Bhat, Chandra R.
AU - Pendyala, Ram M.
AU - Lam, William H.K.
AU - Pinjari, Abdul R.
AU - Srinivasan, Karthik K.
AU - Ramadurai, Gitakrishnan
N1 - Funding Information:
The authors are grateful to Lisa Macias for her assistance in formatting the manuscript and appreciate the comments of five anonymous reviewers on an earlier version of the paper. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially supported by the Data-Supported Transportation Operations and Planning (D-STOP) Center (Grant No. DTRT13GUTC58) and the Center for Teaching Old Models New Tricks (TOMNET) (Grant No. 69A3551747116), both of which are Tier 1 University Transportation Centers sponsored by the U.S. Department of Transportation. The work described in this paper was also supported by a research grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (No. PolyU 152095/17E), and also funded by the Ministry of Human Resource Development (MHRD) of the Government of India through its Scheme for Promotion of Academic and Research Collaboration (SPARC) program.
Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2020.
PY - 2020/1/11
Y1 - 2020/1/11
N2 - Ride-hailing services have grown in cities around the world. There are, however, few studies and even fewer publicly available data sources that provide a basis to understand and quantify changes in ride-hailing usage over time. Ride-hailing use may change over time because of socio-demographic shifts, economic and technological changes, and service attribute enhancements, as well as changes in unobserved attributes such as attitudes and perceptions, lifestyle preferences, technology savviness, and social influences. It is important to quantify the effects of these different forces on ride-hailing frequency so that robust forecasts of ride-hailing use can be developed. This paper uses repeated cross-sectional data collected in 2015 and 2017 in the Puget Sound region to analyze the differential effects of socio-demographic variables on the evolution of ride-hailing adoption and usage. By doing so, the study is able to isolate and quantify the pure effect of the passage of time on adoption of ride-hailing services. A joint binary probit-ordered probit model is estimated on the pooled dataset to explicitly account for sample-selection differences between the 2015 and 2017 surveys that may affect estimates of ride-hailing adoption in the two years. Model estimation results are used to compute average treatment effects of different variables on ride-hailing usage over time. It is found that the effects of most demographic variables on individuals’ propensity to use ride-hailing are softening over time, leading to reduced differences in ride-hailing use among market segments. This suggests that there is a “democratization” of ride-hailing services over time.
AB - Ride-hailing services have grown in cities around the world. There are, however, few studies and even fewer publicly available data sources that provide a basis to understand and quantify changes in ride-hailing usage over time. Ride-hailing use may change over time because of socio-demographic shifts, economic and technological changes, and service attribute enhancements, as well as changes in unobserved attributes such as attitudes and perceptions, lifestyle preferences, technology savviness, and social influences. It is important to quantify the effects of these different forces on ride-hailing frequency so that robust forecasts of ride-hailing use can be developed. This paper uses repeated cross-sectional data collected in 2015 and 2017 in the Puget Sound region to analyze the differential effects of socio-demographic variables on the evolution of ride-hailing adoption and usage. By doing so, the study is able to isolate and quantify the pure effect of the passage of time on adoption of ride-hailing services. A joint binary probit-ordered probit model is estimated on the pooled dataset to explicitly account for sample-selection differences between the 2015 and 2017 surveys that may affect estimates of ride-hailing adoption in the two years. Model estimation results are used to compute average treatment effects of different variables on ride-hailing usage over time. It is found that the effects of most demographic variables on individuals’ propensity to use ride-hailing are softening over time, leading to reduced differences in ride-hailing use among market segments. This suggests that there is a “democratization” of ride-hailing services over time.
UR - http://www.scopus.com/inward/record.url?scp=85103781464&partnerID=8YFLogxK
U2 - 10.1177/0361198120964788
DO - 10.1177/0361198120964788
M3 - Journal article
AN - SCOPUS:85103781464
SN - 0361-1981
VL - 2675
SP - 81
EP - 97
JO - Transportation Research Record
JF - Transportation Research Record
IS - 3
ER -