A behavioral choice model of the use of car-sharing and ride-sourcing services

Dias, Felipe F.; Lavieri, Patrícia S.; Garikapati, Venu; Astroza, Sebastián; Pendyala, Ram M.; Bhat, Chandra R.

doi:10.1007/s11116-017-9797-8

Cited by 330 publications

(226 citation statements)

References 12 publications

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“…Some studies have examined the factors influencing the use of ridesharing services, which include perceptions of availability and safety [3], travel cost and time of travel [31], gasoline prices [32], and some demographic variables (e.g., age, education level, and income level) [33,34]. …”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Relationship between Ridesharing and Public Transit Use in the United States

Zhang

2018

IJERPH

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Car travel accounts for the largest share of transportation-related greenhouse gas emissions in the United States (U.S.), leading to serious air pollution and negative health effects; approximately 76.3% of car trips are single-occupant. To reduce the negative externalities of cars, ridesharing and public transit are advocated as cost-effective and more environmentally sustainable alternatives. A better understanding of individuals’ uses of these two transport modes and their relationship is important for transport operators and policymakers; however, it is not well understood how ridesharing use is associated with public transit use. The objective of this study is to examine the relationships between the frequency and probability of ridesharing use and the frequency of public transit use in the U.S. Zero-inflated negative binomial regression models were employed to investigate the associations between these two modes, utilizing individual-level travel frequency data from the 2017 National Household Travel Survey. The survey data report the number of times the respondent had used ridesharing and public transit in the past 30 days. The results show that, generally, a one-unit increase in public transit use is significantly positively related to a 1.2% increase in the monthly frequency of ridesharing use and a 5.7% increase in the probability of ridesharing use. Additionally, the positive relationship between ridesharing and public transit use was more pronounced for people who live in areas with a high population density or in households with fewer vehicles. These findings highlight the potential for integrating public transit and ridesharing systems to provide easier multimodal transportation, promote the use of both modes, and enhance sustainable mobility, which are beneficial for the environment and public health.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring the Relationship between Ridesharing and Public Transit Use in the United States

Zhang

2018

IJERPH

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“…40 This study presents an approach to model car-sharing membership as part of a portfolio of mobility tools allowing to account for both the interdependencies with other mobility tools as well as the effect of individual attitudes on carsharing membership. In particular, the approach is supposed to shed new light 45 on travelers' actual motivations to become car-sharing members and help to better understand the actual role, car-sharing plays in its members' travel behavior.…”

Section: Introductionmentioning

confidence: 99%

“…car and motorcycle ownership [38,39] or car and season ticket ownership [30]. [40] also used a bivariate ordered Probit model to study the frequency of use of car-sharing and ridesourcing services. All studies mentioned above found significant correlations across choice outcomes meaning that common unobserved factors are at work.…”

mentioning

confidence: 99%

Modeling car-sharing membership as a mobility tool: A multivariate Probit approach with latent variables

Becker

Loder

Schmid

et al. 2017

Travel Behaviour and Society

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Individual travel behavior is to a large extent shaped by the respective portfolio of available mobility tools such as cars, season-tickets or a car-sharing membership. However, the choices of different mobility tools are interdependent and are also affected by individual attitudes. This paper presents an approach to jointly model the choice of four different mobility tools -including car-sharing. Using data from the Swiss transportation micro census of 2005 and 2010, itis shown that car-sharing is used as a supplement to a public transportationoriented lifestyle, but is also used by car owners. The results further indicate that personal attitudes have a substantial effect on the choice of mobility tools and should therefore be accounted for when modeling such decisions.

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“…For each OD pair rs, γ i , i ∈ N rs (t) is a sample of path travel time C k rs (t) if the RV trip v i takes route k and departs at time t. Based on Assumption 1, the probability of v i being on path k is p k rs (t). We are now ready to present an estimator for NDL using trajectory-level RV data in Equation 13.…”

Section: Estimating Ndl With Trajectory-level Rv Datamentioning

confidence: 99%

“…A survey is usually conducted by researchers or public agencies to query riders' or drivers' opinions associated with ride-sourcing services. For example, survey on people's willingness to use ride-sourcing services [13], and survey on riders' incentives to switch between Uber and Lyft [11].ii) Zone-to-zone travel time. Zone-to-zone travel time can be estimated from RV trajectories.…”

mentioning

confidence: 99%

Measuring and reducing the disequilibrium levels of dynamic networks with ride-sourcing vehicle data

Qian

2020

Transportation Research Part C: Emerging Technologies

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A behavioral choice model of the use of car-sharing and ride-sourcing services

Cited by 330 publications

References 12 publications

Exploring the Relationship between Ridesharing and Public Transit Use in the United States

Exploring the Relationship between Ridesharing and Public Transit Use in the United States

Modeling car-sharing membership as a mobility tool: A multivariate Probit approach with latent variables

Measuring and reducing the disequilibrium levels of dynamic networks with ride-sourcing vehicle data

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