Modeling the demand for a shared-ride taxi service: An application to an organization-based context

Al-Ayyash, Zahwa; Abou-Zeid, Maya; Kaysi, Isam

doi:10.1016/j.tranpol.2016.02.013

Cited by 38 publications

(17 citation statements)

References 20 publications

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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%

“…In their model, time and cost attributes had importance weights of over twice as much as the other studied attributes (e.g., pick-up time or weather). Additional service attributes were investigated in Al-Ayyash et al (2016), who used stated preference data. Al-Ayyash et al (2016) found that the number of co-riders that can be assigned per vehicle is also an important factor regarding the willingness to adopt a pooled trip.…”

Section: Introductionmentioning

confidence: 99%

“…Additional service attributes were investigated in Al-Ayyash et al (2016), who used stated preference data. Al-Ayyash et al (2016) found that the number of co-riders that can be assigned per vehicle is also an important factor regarding the willingness to adopt a pooled trip. The percentage of individuals willing to use the presented pooled on-demand service was 7-8% higher if the trip could be shared with a maximum of two additional passengers rather than if it could be shared with up to five additional passengers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…In the last decade and a half, ICLV models have been increasingly applied in many fields to obtain a deeper understanding of the decision process of individuals [19,25,[28][29][30]. Within the field of transportation, ICLV models have been deployed to investigate travel mode choice [31,32], route choice [33], shared-ride transportation service choice [34], flight choice [35], and so on. All the same, the reviewed literature shows that the ICLV model has not been applied to investigate the choice of transportation ticketing channels.…”

Section: Model On Choicementioning

confidence: 99%

Modeling and Analysis of Ticketing Channel Choice for Intercity Bus Passengers: A Case Study in Beijing, China

Jia

Zhang

et al. 2019

Journal of Advanced Transportation

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Buses represent the main mode for intercity passenger transportation in China. In recent years, a multichannel ticketing strategy has been widely employed in the bus passenger transportation industry. However, the mechanisms and key drivers of the channels through which bus passengers purchase tickets are underexplored. Thus, the aim of this study is to empirically apply an integrated choice and latent variable (ICLV) approach to analyze ticketing channel choice behavior and the heterogeneous preferences of bus passengers. The variables incorporated in the model include the socioeconomic characteristics of passengers, trip attributes, and latent attitudes with 12 ordinal indicators. Based on the data of 1800 participants collected from the city of Beijing, China, this study develops a ticketing channel choice ICLV model merging a discrete choice model with a structural equation model. The key factors that affect the channel preference are further discussed through a comparison with a conventional multinomial logit (MNL) model. The results reveal that the three attitudinal variables have a significant influence on ticketing channel choice. Furthermore, this study indicates that perceptual differences exist due to various socioeconomic and trip characteristics. Personal privacy is a major obstacle that prevents passengers from choosing online channels, especially for older passengers and those with lower education.

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“…A study on travel preference in the online taxi was conducted in Lebanon. The study used stated preference data and the model was applied to a case study of the students at the American University of Beirut in Lebanon [5]. Herman stated that service and performance influence Customer Satisfaction, with performance being a result of activities from a work plan.…”

Section: Introductionmentioning

confidence: 99%

Study of Online Taxi Choice Model in Indonesia

Sitinjak¹,

Djakfar²,

Wicaksono³

2020

TOCIEJ

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Background: Online taxis, a travel mode with personalized comfort based on mobile Internet technology, are now rapidly expanding in Indonesia. Objectives: This study aims to analyze the influence of the personal characteristics and travel characteristics on the choice of an online taxi or conventional taxi and to analyze the online taxi performance in the cities of Surabaya and Malang, Indonesia. Methods: A Stated Preference questionnaire was designed and a Google Forms survey was conducted to obtain data; 200 responses were collected. The binary logit model was employed to analyze the influence of the personal characteristics and travel characteristics on the choice modal. Importance-Performance Analysis was proposed to understand the performance of online taxis. Results: By applying the binary logit model, it was found that for variables based on a personal characteristic, age and occupation are the most influential variables that affect the decision to use an online-taxi, while for travel characteristic variables, travel cost is the most influential factor in traveler’s choice-making. According to the sensitivity analysis, the midpoint for travel cost was found to be Rp 9.606,20, and 13,43 minutes for pick up time. Through Importance-Performance Analysis, it was found that no attributes need to be improved. Conclusion: The estimated logit model can describe the personalized travel mode choice well. The users of an online taxi are people of young age and non-working group. If the gap price and the gap pick up time more than midpoint, the majority of travelers will most likely shift to conventional taxis. The online taxi performance has already met passenger satisfaction.

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Modeling the demand for a shared-ride taxi service: An application to an organization-based context

Cited by 38 publications

References 20 publications

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

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

Modeling and Analysis of Ticketing Channel Choice for Intercity Bus Passengers: A Case Study in Beijing, China

Study of Online Taxi Choice Model in Indonesia

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