Integrating ridesourcing services with public transit: An evaluation of traveler responses combining revealed and stated preference data

Yan, Xiang; Levine, Jonathan; Zhao, Xingping

doi:10.1016/j.trc.2018.07.029

Cited by 141 publications

(49 citation statements)

References 31 publications

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“…With the massive adoption of smart phones and social technology, ridesharing services are operating in a dynamic, wide, and articulated environment, and facing a variety of challenges, hence making the research and development of such services a very hot topic in the transport community. For instance, some recent research investigates the advantages of introducing meeting points in terms of cost savings and congestion mitigation (Stiglic et al 2015), the consideration of riders' satisfaction and privacy rights (Avodji et al 2016), the integration of ridesharing in multi-modal systems (Liu et al 2018, Yan et al 2018, the offering of tailored pricing schemes (Sayarshad & Gao 2018) (e.g. for regular travelers such as commuters (Liu & Li 2017, Ma & Zhang 2017), the study of the changes in travel patterns induced by such ridesharing systems (Dong et al 2018), and the consideration of ridesplitting as a binary classification problem (Chen, Zahiri & Zhang 2017).…”

Section: Related Workmentioning

confidence: 99%

Real-time city-scale ridesharing via linear assignment problems

Simonetto

Monteil

Gambella

2019

Transportation Research Part C: Emerging Technologies

144

123

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In this paper, we propose a novel, computational efficient, dynamic ridesharing algorithm. The beneficial computational properties of the algorithm arise from casting the ridesharing problem as a linear assignment problem between fleet vehicles and customer trip requests within a federated optimization architecture. The resulting algorithm is up to four times faster than the state-of-the-art, even if it is implemented on a less dedicated hardware, and achieves similar service quality. Current literature showcases the ability of state-ofthe-art ridesharing algorithms to tackle very large fleets and customer requests in almost near real-time, but the benefits of ridesharing seem limited to centralized systems. Our algorithm suggests that this does not need to be the case. The algorithm that we propose is fully distributable among multiple ridesharing companies. By leveraging two datasets, the New York city taxi dataset and the Melbourne Metropolitan Area dataset, we show that with our algorithm, real-time ridesharing offers clear benefits with respect to more traditional taxi fleets in terms of level of service, even if one considers partial adoption of the system. In fact, e.g., the quality of the solutions obtained in the state-of-the-art works that tackle the whole customer set of the New York city taxi dataset is achieved, even if one considers only a proportion of the fleet size and customer requests. This could make real-time urban-scale ridesharing very attractive to small enterprises and city authorities alike. However, in some cases, e.g., in multi-company scenarios where companies have predefined market shares, we show that the number of vehicles needed to achieve a comparable performance to the monopolistic setting increases, and this raises concerns on the possible negative effects of multi-company ridesharing.

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Section: Related Workmentioning

confidence: 99%

Real-time city-scale ridesharing via linear assignment problems

Simonetto

Monteil

Gambella

2019

Transportation Research Part C: Emerging Technologies

144

123

View full text Add to dashboard Cite

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“…Leveraging on the estimated behavioural models, three of the stated preference studies included in Table 1 also include a scenario analysis. Al-Ayyash et al (2016) and Yan et al (2018) show how the predicted shares of their pooled on-demand alternative would decrease in scenarios where more additional passengers/pick-ups are expected. These studies, however, do not include an individual alternative.…”

Section: Literature Reviewmentioning

confidence: 99%

What are the determinants of the willingness to share rides in pooled on-demand services?

et al. 2020

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Simulation studies suggest that pooled on-demand services (also referred to as Demand Responsive Transport, ridesharing, shared ride-hailing or shared ridesourcing services) have the potential to bring large benefits to urban areas while inducing limited time losses for their users. However, in reality, the large majority of users request individual rides (and not pooled rides) in existing on-demand services, leading to increases in motorised vehicle miles travelled. In this study, we investigate to what extent fare discounts, additional travel time, and the (un)willingness to share the ride with (different numbers of) other passengers play a role in the decision of individuals to share rides. To this end, we design a stated preference study targeting Dutch urban individuals. In our research, we (1) disentangle the sharing aspect from related time-cost trade-offs (e.g. detours), (2) investigate preference heterogeneity regarding the studied attributes and identify distinct market segments, and (3) simulate scenarios to understand the impact of the obtained parameters in the breakdown between individual and pooled services. We find that less than one third of respondents have strong preferences against sharing their rides. Also, we find that different market segments vary not only in their values of the willingness to share, but also in how they perceive this willingness to share (per-ride or proportional to the in-vehicle time). Further, the scenario analysis demonstrates that the share of individuals who are willing to share rides depends primarily on the time-cost trade-offs, rather than on the disutility stemming from pooling rides per se.

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“…The effects of TNCs on public transit ridership are similarly equivocal, with several survey-based studies finding ridesourcing can either replace transit ( Rayle et al., 2016 ) or complement it ( Yan et al., 2018 ; Zhang and Zhang, 2018 ; Hall et al., 2018 ) as an effective extension of an otherwise fixed network (several working papers also suggest replacement ( Alemi et al., 2018b ; Norris and Xiong, 2019 ; Manville et al., 2018 ) or complementarity ( Feigon and Murphy, 2016 ; Schweiterman and Livingston, 2018 )). Importantly, two of these previous studies report heterogeneous effects—a more positive transit correlation is found either (1) in bigger cities as well as cities with smaller transit agencies ( Hall et al., 2018 ) or (2) as a function of high population density and households with fewer vehicles ( Zhang and Zhang, 2018 )—and a working paper finds commuter rail usage increases while city bus usage declines, on average, with variation in effects across cities ( Babar and Burtch, 2017 ).…”

Section: Introductionmentioning

confidence: 99%