Modeling the Evolution of Ride-Hailing Adoption and Usage: A Case Study of the Puget Sound Region
01 Mar 2021-Transportation Research Record (SAGE PublicationsSage CA: Los Angeles, CA)-Vol. 2675, Iss: 3, pp 81-97
TL;DR: In this article, the authors provide a basis to understand and quantify changes in ride-hailing services in cities around the world, using publicly available data sources that can be used for understanding and quantifying changes.
Abstract: 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 i...
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11 Feb 2010
TL;DR: The American Community Survey (ACS) as discussed by the authors has been conducted on an ongoing basis for the entire country since 2005 and has been shown to be more accurate than the traditional decennial census.
Abstract: Historically, most demographic data for states and substate areas were collected from the long version of the decennial census questionnaire. A “snapshot” of the characteristics of the population on the April 1 census date was available once every 10 years. The long form of the decennial census has been replaced by the American Community Survey (ACS) that has been conducted on an ongoing basis for the entire country since 2005. Instead of a snapshot in which all of the data are gathered at one time, the ACS aggregates data collected over time, making the results more difficult to interpret. However, the ACS data are updated annually.
691 citations
TL;DR: In this paper , the authors examined the factors influencing the frequency of ride-hailing use and the demand for pooled and autonomous vehicle (AV) rides in Australia and found that there is a divide between factors affecting not only the use of using ridehailing in the status quo but also in the future adoption of new services of pooled and AV-based services.
Abstract: While ride-hailing has become ubiquitous in most metropolitan areas, it is still unclear how it prevails in the regional areas, especially after the emergence of new services of pooling or self-driving rides. This paper contributes to this knowledge gap by examining the regional/metropolitan dimension in the factors influencing the frequency of ride-hailing use and the demand for pooled and autonomous vehicle (AV) rides in Australia. Through individual-level survey data, we found that there is indeed a divide between factors affecting not only the frequency of using ride-hailing in the status quo but also in the future adoption of new services of pooled and AV-based services. Using the hybrid discrete choice model, we identify several latent psychological constructs that influence the use of ride-hailing services, namely being pro-technology, pro-pooling, pro-AV, anti-driving and security-cautious traits. Moreover, the results of multivariate models point to different observed and latent characteristics influencing the adoption of pooled and AV ride-hailing services for various trip purposes. In line with the literature, it was found that the socio-demographic characteristics that affect the frequency of use or adopting pooled and AV services in future include gender, age, income, education attainment, employment status, car ownership, and the current travel behaviour. We also found that ride-hailing in Australia has played more as a substitution role for public transport, private car and taxi than as a complementary role or feeder of the public transport system, both in the metropolitan and regional areas.
3 citations
TL;DR: In this article , the authors examined the trip purposes, travel time, and origins and destinations of TNC trips recorded in the travel survey to explore how and why residents use TNC services, and explored the associations between TNC adoption, usage, and a series of demographic and socioeconomic variables.
Abstract: Due to the lack of data, there have been limited studies that link TNC (e.g., Uber and Lyft) usage and trips with demographic and socio-economic characteristics of individual customers, especially customers who might be financially vulnerable and lack car access. Using Chicago region’s 2018–19 household travel survey, this paper bridges the gap between TNC usage, trip characteristics, and individual's demographic and socio-economic characteristics. The first analysis examines the trip purposes, travel time, and origins and destinations of TNC trips recorded in the travel survey to explore how and why residents use TNC services. The second analysis uses zero-inflated negative binomial regression to explore the associations between TNC adoption, usage, and a series of demographic and socio-economic variables. Both analyses pay particular attention to financially disadvantaged residents who lack access to private cars. The analyses reveal that TNC use decreases with vehicle ownership. Compared to higher-income carless residents, lower-income carless residents use TNC services more for essential trips, especially during hours with infrequent transit services. For essential TNC trips, lower-income residents likely pay a bigger share of their income than higher-income residents do. The findings have implications on transit planning and transportation benefit programs that aim to integrate TNC services to enhance access for lower-income residents.
3 citations
TL;DR: In this article , the authors compared three types of choice-set structures in the context of urban travel mode choice by estimating standard logit and nested logit models to test six hypotheses on the associations of RHS adoption with its determinants.
Abstract: The ride-hailing service (RHS) has emerged as a major form of daily travel in many Southeast Asian cities where motorcycles are extensively used. This study aims to analyze the local context in motorcycle-based societies, which may affect the establishment of travelers’ choice set after the appearance of RHSs. In particular, it empirically compares three types of choice-set structures in the context of urban travel mode choice by estimating standard logit and nested logit models to test six hypotheses on the associations of RHS adoption with its determinants. Revealed preference data of 449 trips from both RHS users and non-RHS users were collected through a face-to-face interview-based questionnaire survey in Hanoi, Vietnam, in December 2020. The results of model estimations revealed: (1) a substitutional effect for two-wheelers but not for four-wheelers, (2) a significant positive influence of car ownership on car RHS adoption but not on motorcycle RHS adoption, (3) significantly high sensitivity to travel time of motorcycle RHS but not of car RHS, (4) a significant negative effect of traffic congestion on car RHS adoption but an insignificant one on motorcycle RHS adoption, and (5) a significant positive association of an individual’s experience in using a smartphone with car RHSs but insignificant association with motorcycle RHSs. Our findings suggest that transportation policies of RHS motorcycles should be different from those of RHS cars because of the heterogeneity in travel behaviors of RHS users between them. They also indicate that the transition from motorcycles to cars as well as the difference in service availability among different types of RHSs should be incorporated into the development of transportation policies in Southeast Asian cities.
2 citations
References
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03 Apr 2017
TL;DR: A study of the Uber market that analyzes large-scale data covering 59 million rides which spans a period of 7 months finds that the surge pricing does not bias Uber use towards higher income riders and shows that more homophilous matches can result in higher driver ratings.
Abstract: Uber is a popular ride-sharing application that matches people who need a ride (or riders) with drivers who are willing to provide it using their personal vehicles. Despite its growing popularity, there exist few studies that examine large-scale Uber data, or in general the factors affecting user participation in the sharing economy. We address this gap through a study of the Uber market that analyzes large-scale data covering 59 million rides which spans a period of 7 months. The data were extracted from email receipts sent by Uber collected on Yahoo servers, allowing us to examine the role of demographics (e.g., age and gender) on participation in the ride-sharing economy. In addition, we evaluate the impact of dynamic pricing (i.e., surge pricing) and income on both rider and driver behavior. We find that the surge pricing does not bias Uber use towards higher income riders. Moreover, we show that more homophilous matches (e.g., riders to drivers of a similar age) can result in higher driver ratings. Finally, we focus on factors that affect retention and use information from earlier rides to accurately predict which riders or drivers will become active Uber users.
81 citations
"Modeling the Evolution of Ride-Hail..." refers background or methods in this paper
...For example, Kooti et al. (2017) partnered with Yahoo to gather the receipts sent by Uber to riders’ email addresses after a trip was completed....
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...16 A number of studies have previously documented that older age groups are associated with a lower level of ride-hailing use (e.g., Rayle et al., 2016; Kooti et al., 2017; and Dias et al., 2017)....
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TL;DR: In this paper, an online survey was conducted with 556 residents of metropolitan Austin, Texas, to determine intent to use and four intent-to-use categories were determined: extremely unlikely, 18%; somewhat unlikely, 32%; somewhat likely, 36%; and extremely likely, 14%.
Abstract: This study gathered empirical evidence on adoption patterns of self-driving vehicles, their likely use, and how that use might influence the amount of travel, mode choice, auto ownership, and other travel behavior decisions. Because self-driving vehicles were not yet on the market, a car technology acceptance model was applied to understand adoption and use. Researchers implemented a two-stage data collection effort. An online survey was conducted with 556 residents of metropolitan Austin, Texas, to determine intent to use. Four intent-to-use categories were determined: extremely unlikely, 18%; somewhat unlikely, 32%; somewhat likely, 36%; and extremely likely, 14%. Of those who indicated intent to use, qualitative interviews were conducted to ascertain the impact on their travel behavior. Most respondents would rather own a self-driving vehicle than use one such as Car2Go or Uber taxi. In addition, respondents reported that using a self-driving vehicle would make no change in where people would choose to...
78 citations
"Modeling the Evolution of Ride-Hail..." refers background in this paper
...technology adoption in a number of applications including, but not limited to, potential adoption of self-driving vehicles (Zmud et al., 2016)....
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TL;DR: A day-to-day dynamic evolution model with the consideration of BR is proposed, which can better captures travelers’ characteristics in the path finding within an urban railway network and can serve as a general framework of modeling passengers’ BR behavior.
Abstract: Existing day-to-day traffic assignment models are all built to capture day-to-day traffic fluctuations, but most of the evolution process itself and the final equilibrium state are based on the assumption of passengers’ rational behavior, that is, to find the path with the minimum travel cost, which ignore the correlation among the days’ evolution and boundedly rational (BR) of travelers in the path choice and thus can give very unreasonable results for ones with this behavior. Such an assumption basically ignores the correlation among day-to-day evolution and bounded rationality (BR) of travelers in the path choice and thus could result in inaccurate results for the travelers with such behavior. This paper proposes a day-to-day dynamic evolution model with the consideration of BR, which can better captures travelers’ characteristics in the path finding within an urban railway network. In order to capture the correlation of path choice over time, we introduce a time series method, detrended fluctuation analysis (DFA), to analyze the complex long-term correlations hiding in the passengers’ evolution over time. The study results clearly show that the proposed model and analytical approach is better for capture the day-to-day dynamics in travel behaviors and can serve as a general framework of modeling passengers’ BR behavior.
66 citations
"Modeling the Evolution of Ride-Hail..." refers background in this paper
...(23), Miller and Shalaby (24), Sharaby and Shiftan (25), Zhao et al....
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TL;DR: Findings from an in-depth analysis of RideAustin’s dataset of over one million trips can be used by planners and modelers as they integrate rideshare systems within their planning and modeling frameworks and may provide insights into a future system of autonomous and shared vehicles.
Abstract: App-driven ridesharing platforms are gaining popularity and are transforming urban movement patterns in cities throughout the world. Because of privacy and business considerations, their owners hav...
55 citations
TL;DR: In this paper, the authors present a descriptive analysis of the historical evolution of personal travel behavior in the Greater Toronto Area (GTA) over the past 35 years and highlight ways in which the GTA, particularly the city of Toronto, deviates from the North American “norm.
Abstract: This paper presents a descriptive analysis of the historical evolution of personal travel behavior in the Greater Toronto Area (GTA) over the past 35 years. The analysis indicates that in many respects the GTA taken as a whole is similar to other cities within North America in terms of increasing auto ownership; increasing individual auto-drive trip rates; increasing suburbanization of population and employment into areas poorly served by transit; increasingly complex travel patterns; and transit, at best, maintaining a constant number of trips per capita but losing modal share. The analysis also highlights ways in which the GTA, particularly the city of Toronto, deviates from the North American “norm.” These include transit per capita ridership, overall mode splits, revenue-cost operating ratios are still extremely high by North American standards; the regional commuter rail system has been very successful in attracting increasing numbers of commuters from outside Toronto into the Toronto central area; t...
43 citations
"Modeling the Evolution of Ride-Hail..." refers background in this paper
...Some other studies examine travel behavior variability without necessarily tying to transferability, including Marchetti (21), Meyer (22), Wu et al. (23), Miller and Shalaby (24), Sharaby and Shiftan (25), Zhao et al. (26), and Abenoza et al. (27)....
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...Miller and Shalaby (24), Sharaby and Shiftan (25), Zhao et al....
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