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Journal ArticleDOI

Modeling the Evolution of Ride-Hailing Adoption and Usage: A Case Study of the Puget Sound Region

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...
Citations
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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 The long form of the decennial census has been replaced by the American Community Survey (ACS) that has been conducted on an ongoing basis for the entire country since 2005.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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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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Journal ArticleDOI
TL;DR: In this paper, a discussion of the sustainability and travel behavior impacts of ride-hailing is provided, based on an extensive literature review of studies from both developed and developing countries.
Abstract: A discussion of the sustainability and travel behaviour impacts of ride-hailing is provided, based on an extensive literature review of studies from both developed and developing countries. The effects of ride-hailing on vehicle-kilometres travelled (VKT) and traffic externalities such as congestion, pollution and crashes are analysed. Modal substitution, user characterisation and induced travel outputs are also examined. A summary of findings follows. On the one hand, ride-hailing improves the comfort and security of riders for several types of trips and increases mobility for car-free households and for people with physical and cognitive limitations. Ride-hailing has the potential to be more efficient for rider-driver matching than street-hailing. Ride-hailing is expected to reduce parking requirements, shifting attention towards curb management. On the other hand, results on the degree of complementarity and substitution between ride-hailing and public transport and on the impact of ride-hailing on VKT are mixed; however, there is a tendency from studies with updated data to show that the ride-hailing substitution effect of public transport is stronger than the complementarity effect in several cities and that ride-hailing has incremented motorised traffic and congestion. Early evidence on the impact of ride-hailing on the environment and energy consumption is also concerning. A longer-term assessment must estimate the ride-hailing effect on car ownership. A social welfare analysis that accounts for both the benefits and costs of ride-hailing remains unexplored. The relevance of shared rides in a scenario with mobility-as-a-service subscription packages and automated vehicles is also highlighted.

181 citations


"Modeling the Evolution of Ride-Hail..." refers background in this paper

  • ...The reader is also referred to Tirachini (19) for a more exhaustive review of ride-hailing studies, both in the U.S. and internationally....

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  • ...The reader is also referred to Tirachini (19) for a more exhaustive review of ride-hailing studies, both in the U....

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Journal ArticleDOI
TL;DR: In this article, the authors identify and characterize current and potential users of public transport in Sweden and identify the most important determinants of travel satisfaction with public transport services for each segment of travelers.
Abstract: Increasing public transport ridership while providing a service that better caters to individual travelers poses an important goal and challenge for society, particularly public transport authorities and operators. This study identifies and characterizes current and potential users of public transport in Sweden and identifies the most important determinants of travel satisfaction with Public Transport services for each segment of travelers. In addition, it investigates the changes over time of attribute importance among the different segments and the inter-segment geographical variation of overall satisfaction. The analysis is based on a dataset of almost half a million records. Travelers were clustered based on their socio-demographics, travel patterns and accessibility measures to enable the analysis of determinants of satisfaction for different market segments. The cluster analysis results with five segments of Swedish travelers include: (i) inactive travelers; (ii) long distance commuters; (iii) urban motorist commuters; (iv) rural motorist commuters and; (v) students. By contrasting satisfaction with the importance of each quality of service attribute, three key attributes that should be prioritized by stakeholders are identified: customer interface, operation, network and length of trip time. Interestingly, the results suggest an overall similarity in the importance of service attributes among traveler segments. Nevertheless, some noticeable differences could be observed. The quality of service attributes’ importance levels reveal overall changes in appreciations and consumption goals over time. The more frequent public transport user segments are more satisfied across the board and are characterized by a more balanced distribution of attribute importance while rural motorist commuters are markedly dissatisfied with service operation attributes. This work can help authorities to tailor their policies to specific traveler groups.

176 citations


"Modeling the Evolution of Ride-Hail..." refers background in this paper

  • ...(2012), and Abenoza et al. (2017). But none of these studies examines the relatively new phenomenon of travel behavior changes over time regarding ride-hailing use. In an attempt to address the issue of how ride-hailing impacts user behavior, Young et al. (2018) focused on the twenty business trips made to Columbus, Ohio by one single individual, splitting the trips into two groups according to what the individual used as the primary mode of transportation: rental cars or ride-hailing services....

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  • ...(2012), and Abenoza et al. (2017). But none of these studies examines the relatively new phenomenon of travel behavior changes over time regarding ride-hailing use....

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  • ...(2012), and Abenoza et al. (2017). But none of these studies examines the relatively new phenomenon of travel behavior changes over time regarding ride-hailing use. In an attempt to address the issue of how ride-hailing impacts user behavior, Young et al. (2018) focused on the twenty business trips made to Columbus, Ohio by one single individual, splitting the trips into two groups according to what the individual used as the primary mode of transportation: rental cars or ride-hailing services. In their study, the authors analyze the individual’s business expense reports, which contain data on the individual’s vehicle miles traveled, their cost of transportation, and their daily work routines. While the analysis presented by Young et al. (2018) focuses on how this one single individual changes travel behaviors when using ride-hailing services (compared to when the individual uses car rental services), it does not provide any information regarding the temporal change in ride-hailing use....

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Journal ArticleDOI
TL;DR: In this paper, the authors investigated ride-hailing experience, frequency, and trip characteristics through two multi-dimensional models estimated using data from the Dallas-Fort Worth Metropolitan Area, and found that low residential location density and people's privacy concerns were the main deterrents to pooled ride-sharing adoption, with non-Hispanic Whites being more privacy sensitive than individuals of other ethnicities.
Abstract: Even as ride-hailing has become ubiquitous in most urban areas, its impacts on individual travel are still unclear. This includes limited knowledge of demand characteristics (especially for pooled rides), travel modes being substituted, types of activities being accessed, as well as possible trip induction effects. The current study contributes to this knowledge gap by investigating ride-hailing experience, frequency, and trip characteristics through two multi-dimensional models estimated using data from the Dallas-Fort Worth Metropolitan Area. Ride-hailing adoption and usage are modeled as functions of unobserved lifestyle stochastic latent constructs, observed transportation-related choices, and sociodemographic variables. The results point to low residential location density and people’s privacy concerns as the main deterrents to pooled ride-hailing adoption, with non-Hispanic Whites being more privacy sensitive than individuals of other ethnicities. Further, our results suggest a need for policies that discourage the substitution of short-distance “walkable” trips by ride-hailing, and a need for low cost and well-integrated multi-modal systems to avoid substitution of transit trips by this mode.

172 citations


"Modeling the Evolution of Ride-Hail..." refers background in this paper

  • ...However, it is conceivable that there are several other such factors or combinations of factors that are unobserved and affect ride-hailing usage (13)....

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  • ...The authors confirm contribution to the paper as follows: study conception and design: F.F. Dias, T. Kim, C.R. Bhat, R.M. Pendyala, W.H.K. Lam, A.R. Pinjari, K.K. Srinivasan, G. Ramadurai; data collection: PSRC; analysis and interpretation of results: F.F. Dias, T. Kim, C.R. Bhat, R.M. Pendyala, W.H.K. Lam, A.R. Pinjari, K.K. Srinivasan, G. Ramadurai; draft manuscript preparation: F.F. Dias, T. Kim, C.R. Bhat, R.M. Pendyala, W.H.K. Lam, A.R. Pinjari, K.K. Srinivasan, G. Ramadurai....

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  • ...These findings corroborate the results from many prior studies, including, for example, Rayle et al. (6), Dias et al. (1), Lavieri et al. (8), Kooti et al. (14), Vinayak et al. (9), Alemi et al. (10), and Lavieri and Bhat (13)....

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  • ...In yet another recent survey-based study, Lavieri and Bhat (13) analyzed data collected from a sample of commuters in the Dallas-Fort Worth area to identify the psycho-social influencers that motivate the use of ride-hailing services....

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  • ...While ride-hailing 1Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, Austin, TX 2School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 3The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 4Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 5Department of Civil Engineering, Centre for infrastructure, Sustainable Transportation and Urban Planning (CiSTUP), Indian Institute of Science, Bengaluru, Karnataka, India 6Department of Civil Engineering, Indian Institute of Technology Madras, IIT Madras, Chennai, Tamil Nadu, India Corresponding Author: Chandra R. Bhat, bhat@mail.utexas.edu services may have shifted some travel away from the personal automobile, particularly in a few dense urban markets, they have generally served as a substitute for transit and traditional taxi services (3), both of which are traditional means of transportation with very low mode shares to begin with....

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Journal ArticleDOI
TL;DR: In this paper, the authors investigate how the frequency of use of ride-hailing varies across segments of the California population and under various circumstances, finding that individuals with higher willingness to pay to reduce their travel time use ridehailing more often.
Abstract: The availability of ridehailing services, such as those provided by Uber and Lyft in the U.S. market, as well as the share of trips made by these services, are continuously growing. Yet, the factors affecting the frequency of use of these services are not well understood. In this paper, we investigate how the frequency of use of ridehailing varies across segments of the California population and under various circumstances. We analyze data from the California Millennials Dataset (N = 1975), collected in fall 2015 through an online survey administered to both millennials and members of the preceding Generation X. We estimate an ordered probit model with sample selection and a zero-inflated ordered probit model with correlated error terms to distinguish the factors affecting the frequency of use of ridehailing from those affecting the adoption of these services. The results are consistent across models: sociodemographic variables are important predictors of service adoption but do not explain much of the variation in the frequency of use. Land use mix and activity density respectively decrease and increase the frequency of ridehailing. The results also confirm that individuals who frequently use smartphone apps to manage other aspects of their travel (e.g. to select a route or check traffic) are more likely to adopt ridehailing and use it more often. This is also true for long-distance travelers, in particular, those who frequently travel by plane for leisure purposes. Individuals with higher willingness to pay to reduce their travel time use ridehailing more often. Those with stronger preferences to own a personal vehicle and those with stronger concerns about the safety/security of ridehailing are less likely to be frequent users. These results provide new insights into the adoption and use of ridehailing that could help to inform planning and forecasting efforts.

154 citations


"Modeling the Evolution of Ride-Hail..." refers background in this paper

  • ...More recently, a number of studies have been conducted using survey data collected in the State of California, with a particular focus on millennials (Alemi et al., 2018; Alemi et al., 2019; Circella et al., 2018)....

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25 Jul 2018

148 citations


"Modeling the Evolution of Ride-Hail..." refers background or methods in this paper

  • ...333 trips per month; (3) ‘‘1–3 days per month’’ = 2 trips per month; (4) ‘‘1 day per week’’ = 4 trips per month; and (5) ‘‘2 or more days per week’’ = 16 trips per month....

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  • ...84 Transportation Research Record 2675(3)...

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  • ...(3) and is likely to continue in the near future as the services evolve and make their offerings more appealing....

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  • ...Ride-hailing services have experienced dramatic growth in the past several years, with the total number of rides now exceeding total local bus ridership across the country (3)....

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  • ...and traditional taxi services (3), both of which are traditional means of transportation with very low mode shares to begin with....

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