Why do people rideshare? An experimental study

Amirkiaee, S. Yasaman; Evangelopoulos, Nicholas

doi:10.1016/j.trf.2018.02.025

Cited by 128 publications

(81 citation statements)

References 29 publications

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“…The internet and mobile-phone applications, however, provide an opportunity to promote carpooling [8]. Companies such as BlaBlaCar, Zimride, and Lyftshare are accelerating the market [17].…”

Section: A Short History Of Carpoolingmentioning

confidence: 99%

“…Hence, we identified a gap in the literature linking carpooling to psychological theories. The concept that attitudes have a strong influence on ridesharing was demonstrated by a study of Amirkiaee and Evangelopoulos [8]. Neoh et al [4] found that judgmental factors, such as saving money, reliability, and the desire to reduce congestion, all had small effect sizes, but the effect was the desire to reduce congestion.…”

Section: Judgmental Factors (Internal)mentioning

confidence: 99%

“…That paper analyzed studies published in or before 2013. Since then, new technologies and innovations emerged and the "sharing-economy" is on the rise [8]. Changes in society and technology require changes in policy recommendations to adapt to current trends.…”

Section: Introductionmentioning

confidence: 99%

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Why Do They Ride with Others? Meta-Analysis of Factors Influencing Travelers to Carpool

2019

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Carpooling can be viewed as a simple intervention to reduce congestion, environmental problems, and land use for parking spaces. The present study assembled 18 studies on carpooling from all over the world that were published during the last five years (2014–2018) for a meta-analysis. By calculating effect sizes of 20 different factors, the study aimed to understand user characteristics, motives, and barriers to carpooling, and to gain insights about carpool interventions. Our results indicate that carpooling is very weakly related to socio-demographic variables, and that psychological factors are becoming more important, including monetary and time benefits, reducing congestion, and environmental concerns. Policy-makers can increase carpooling by offering cheaper parking or special parking spaces for carpoolers and introducing high-occupancy vehicle (HOV) lanes. Not surprisingly, fuel prices influence mode choice. The overall findings support previous results, but we found judgmental factors becoming more important for the choice to carpool. We conclude that carpooling services still fail to include many potential users and to serve users adequately. The challenge of meeting the needs of all users requires new approaches to designing carpool concepts, systems, and encounters.

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Section: A Short History Of Carpoolingmentioning

confidence: 99%

Section: Judgmental Factors (Internal)mentioning

confidence: 99%

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Why Do They Ride with Others? Meta-Analysis of Factors Influencing Travelers to Carpool

2019

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“…Among the various emerging alternatives, solutions such as peer-to-peer (P2P) ride-hailing (e.g. Uber, Lyft, Didi Chuxing), have rapidly emerged and gained ground as alternative modes of getting transportation, providing partial solutions to mobility issues [4]. Another interesting emerging solution is the concept of Mobility-as-a-Service (MaaS).…”

Section: Introductionmentioning

confidence: 99%

Business Platforms for Autonomous Vehicles Within Urban Mobility

Antonialli

Gandia

Sugano

et al. 2019

WIT Transactions on the Built Environment

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With mobility becoming a key factor affecting citizens' well-being and quality of life, innovative schemes such as peer-to-peer (P2P) ride-hailing and Mobility-as-a-Service (MaaS) could have significant impacts on the urban commute. Furthermore, the insertion of autonomous vehicles (AVs) promises a positive impact on cities' transit worldwide. Ergo, companies are now combining bundles of products and services emphasizing their "sales of use" rather than of products: such integration may be realized by having multi-sided business platforms. The purpose of this study is to create future scenarios for AV business platforms within urban mobility contexts. Based on an exploratory descriptive qualitative research design, we collected data on academic and grey literature. Four scenarios were plotted with real-world examples currently present in urban mobility cases, extrapolated to a future reality where AVs are considered a transport mode: (A) business-to-consumer (B2C) autonomous ridesharing; (B) P2P autonomous ridesharing; (C) B2C multimodal mobility; (D) B2C + P2P multimodal mobility. Scenarios A and B entail unimodal mobility via autonomous ridesharing, whereupon in the former, a single firm owns a fleet and subsidizes the consumer segment, while in the latter, ordinary peers own the AVs, which are offered to other peers via Uber-like applications. Scenarios C and D entail multimodal mobility from point X to Y, where in the former, a single firm owns the multimodal fleet and subsidizes the consumer segment, while Scenario D offers a bundle of B2C and P2P solutions under the MaaS paradigm. We conclude that in scenarios A and B, value creation can be solely explained by business platforms theory; while in scenarios C and D, business platforms theory is tied to business ecosystem theory, to explain value creation and distribution. Lastly, besides the unlikelihood of scenario C, any of the other three have the potential to become a reality when AVs become an urban transport mode.

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“…Recently, there is a small but growing body of studies with respect to ridesharing activities to explain travel behavior within the transportation domain. Some researchers have conducted empirical studies [8,11,[20][21][22][23][24][25][26][27][28]; some researchers have studied travelers' route choice and mode decision making process when ridesharing activities are incorporated into the morning commute problem [29][30][31] and the traffic assignment problem [32][33][34][35][36][37]. As for empirical studies, for instance, Morency [8] used travel data from four large-scale origin-destination (OD) surveys to study the evolution of the ridesharing market in the Greater Montreal Area, and found that commuters were at one time more inclined (or forced) to share car seats and then less people chose to share rides with others (because travelers wanted more freedom to travel as they wish with the rapid urban development and economic growth); Caulfield [20] conducted a logistic regression analysis to examine the characteristics of the individuals that rode shares in Dublin, and found that females and those in couples were most likely to rideshare; Erdogan et al [21] studied the demand for ridesharing in a university campus context by using a commuter survey data, developed ordered probit models to investigate interest in ridesharing, and found that taste heterogeneity significantly affected propensity to rideshare; Lee et al [25] conducted a self-reported online survey among Uber users in Hong Kong, used the structural equation modeling technique to analyze the empirical results, and found that perceived risks, perceived benefits, trust in the platform, and perceived platform qualities significantly influenced users' intention to participate in Uber; Stiglic et al [26] used an extensive computational study to investigate the potential benefits as well as synergies of the seamless integration of ride-sharing and public transit, and found that such a system could significantly enhance mobility and increase the use of public transport.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Modal Route Choice Model with Ridesharing and Public Transit

Hua

Huang

2018

Sustainability

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With the extensive use of smart-phone applications and online payment systems, more travelers choose to participate in ridesharing activities. In this paper, a multi-modal route choice model is proposed by incorporating ridesharing and public transit in a single-origin-destination (OD)-pair network. Due to the presence of ridesharing, travelers not only choose routes (including main road and side road), but also decide travel modes (including solo driver, ridesharing driver, ridesharing passenger, and transit passenger) to minimize travelers’ generalized travel cost (not their actual travel cost due to the existence of car capacity constraints). The proposed model is expressed as an equivalent complementarity problem. Finally, the impacts of key factors on ridesharing behavior in numerical examples are discussed. The equilibrium results show that passengers’ rewards and toll charge of solo drivers on main road significantly affect the travelers’ route and mode choice behavior, and an increase of passengers’ rewards (toll) motivates (forces) more travelers to take environmentally friendly travel modes.

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Why do people rideshare? An experimental study

Cited by 128 publications

References 29 publications

Why Do They Ride with Others? Meta-Analysis of Factors Influencing Travelers to Carpool

Why Do They Ride with Others? Meta-Analysis of Factors Influencing Travelers to Carpool

Business Platforms for Autonomous Vehicles Within Urban Mobility

A Multi-Modal Route Choice Model with Ridesharing and Public Transit

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