What influences people to choose ridesharing? An overview of the literature

Si, Hongyun; Shi, Jing; Hua, Wenwen; Cheng, Long; Vos, Jonas De; Li, Wenxiang

doi:10.1080/01441647.2023.2208290

Cited by 14 publications

(4 citation statements)

References 94 publications

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“…As discussed in [21], achieving high efficiency may necessitate a sizable fleet, potentially leading to a heavily subsidised taxi-like service and, in extreme cases, may even exacerbate road congestion. Therefore, conducting thorough analyses of regions using multi-source data [40] becomes imperative, considering factors such as peak and off-peak hours, socioeconomic conditions [41], and the availability of public transportation. Subsequently, these services could be used to complement and enhance public transportation rather than entirely replace it or to introduce new on-demand services by identifying regions that could benefit from it [42].…”

Section: Discussionmentioning

confidence: 99%

Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems

Melo,

Gomes,

Abbasi

et al. 2024

Sustainability

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Transport-on-demand services, such as demand-responsive transport (DRT), involve a flexible transportation service that offers convenient and personalised mobility choices for public transport users. Integrating DRT with mobile data and data analytics enhances understanding of travel patterns and allows the development of improved algorithms to support design-optimised services. This study introduces a replicable framework for DRT that employs an on-demand transport simulator and routing algorithm. This framework is supported by a mobile data set, enabling a more accurate service design grounded on actual demand data. Decision-makers can use this framework to understand traffic patterns better and test a DRT solution before implementing it in the actual world. A case study was conducted in Porto, Portugal, to demonstrate its practicality and proof of concept. Results show that the DRT solution required 135% fewer stops and travelled 81% fewer kilometres than the existing fixed-line service. Findings highlight the potential of this data-driven framework for urban public transportation systems to improve key performance metrics in required buses, energy consumption, travelled distance, and stop frequency, all while maintaining the number of served passengers. Under specific circumstances, embracing this approach can offer a more efficient, user-centric, and environmentally sustainable urban transportation service.

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Section: Discussionmentioning

confidence: 99%

Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems

Melo,

Gomes,

Abbasi

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Ridesharing motivation may also be impacted by environmental concerns (Raza et al, 2023). Recent summaries of the literature, suggests that there is still much to learn about ridesharing in general, and research still needs to investigate how these findings relate to SAVs specifically (Si et al, 2023).…”

Section: Measuring Mava-constructsmentioning

confidence: 99%

Unveiling the Dynamics of Shared Autonomous Vehicle Acceptance: Insights into the General Acceptance Factor, Social Preferences, and Design Components

Aasvik,

Ulleberg,

Hagenzieker

2024

Preprint

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The technology behind shared autonomous vehicles (SAVs) is developing rapidly and may revolutionize public transport in metropolitan areas. To take full advantage of the potential benefits, it is paramount to understand the public acceptance of this new technology. One of the leading models for explaining technology uptake is the UTAUT (Unified theory of acceptance and use of technology). This model is vast and has received numerous suggested extensions and revisions, even being developed into the Multi-Level Model of Autonomous Vehicle Acceptance (MAVA). More research is needed to consolidate the model to best measure the acceptance of SAVs, and to determine which extensions capture the unique social situation arising within SAVs. The current study used survey data from 1902 respondents to perform a principal component analysis (PCA) of key constructs suggested by the MAVA. We found that these items were reducible to a single general acceptance factor (GAF), with three additional constructs measuring interpersonal security, sociability, and attractivity. The GAF was, by a large margin, the most efficacious predictor of intention to use SAVs. The overlap between GAF and intention to use may suggest that these are best conceptualized as a single component. The GAF could be further reduced to as little as two predictors, trust and usefulness, accounting for over 70 % of the variance in intention to use. There is, however, also an argument to be made that the other three components of SAV acceptance may be important for capturing different nuances of the service. Interaction terms show that there is differences between genders in their rating of sociability, and how this impacts intentions to use SAVs. Our results have important implications for future research within the field. It cements the importance of trust and usefulness and corroborates the claim that acceptance of SAVs is best represented by a single latent component. However, more research should investigate the individual level moderating effects on the other components, as this may unlock new insights about how best to design a future SAV service.

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“…For example, machine learning is being utilized to accurately predict traffic spatiotemporal dynamics or calculate the estimated time of arrival, thus resulting in improved user satisfaction and likely in higher positive impacts [8]. Past studies have focused on dynamic ridesharing services that tend to match up drivers and riders on very short notice, or even en route [9], and motivating factors and barriers for the adoption of ridesharing services [10][11][12][13]. The most recent literature review on this subject [10] identified three main categories of factors that influence the uptake of ridesharing: (a) demographic characteristics [14], (b) psychological factors [14] and (c) situational factors which refer to external objective factors (e.g., policies, COVID- 19).…”

Section: Introductionmentioning

confidence: 99%

An Evaluation and Prioritization Framework for Pilot First- and Last-Mile Ridesharing Services

Mitropoulos,

Kortsari,

Fotiou

et al. 2023

Sustainability

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Ridesharing is part of the innovative shared transport regime which aims to maximize the utilization of mobility resources. Gaining knowledge of ridesharing’s impacts and how to assess them can significantly improve such services and thus contribute to their adoption among broader groups of travelers and to travel behavior change. This paper presents the framework deployed for assessing the impacts of a first-/last-mile ridesharing pilot in Athens, Greece, and capturing stakeholders’ (i.e., a researcher organization, a public authority and an infrastructure provider) point of view about planning objectives. Four impact areas are defined in total, and Key Performance Indicators (KPIs) are used. In parallel, in order to understand the stakeholder priorities when designing ridesharing services, the Analytical Hierarchical Process is implemented to estimate weights for each impact area. Increasing rail ridership is considered the top priority for all stakeholders during the planning phase for a first-/last-mile ridesharing service, which may have various implications for future initiatives. In total, 28 participants used the ridesharing service as drivers and passengers during the demonstration period. Results show that although a ridesharing service is expected to be an asset in daily transport for city travelers, the technological constraints currently burden its usage. However, as supported by demo results and travelers’ experience, there is great potential of ridesharing to contribute to a sustainable transport system and serve as a first- and last-mile solution to public transport.

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What influences people to choose ridesharing? An overview of the literature

Cited by 14 publications

References 94 publications

Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems

Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems

Unveiling the Dynamics of Shared Autonomous Vehicle Acceptance: Insights into the General Acceptance Factor, Social Preferences, and Design Components

An Evaluation and Prioritization Framework for Pilot First- and Last-Mile Ridesharing Services

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