Travellers’ willingness to share rides in autonomous mobility on demand systems depending on travel distance and detour

König, Alexandra; Grippenkoven, Jan

doi:10.1016/j.tbs.2020.06.010

Cited by 39 publications

(24 citation statements)

References 61 publications

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“…The coefficient of detour time for ride-sharing is insignificant, which is inconsistent with the previous studies (König and Grippenkoven, 2020;Ke et al, 2021). The reason may be that people would mind sharing in-vehicle spaces rather than the detour time in the context of COVID-19.…”

Section: Estimation Resultscontrasting

confidence: 80%

“…In this paper, three levels of 5, 12, and 20 CNY were determined according to the parking rates established by Beijing Municipal Commission of Development and Reform (2018). • Detour time: Detour was specified as an influencing factor of travelers' willingness to embrace ride-sharing (König and Grippenkoven, 2020). Li et al (2019) reported that most shared rides made detours <30 min, and the average detour time was 10 min.…”

Section: Methodology Surveymentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Role of Shared Mobility in Alleviating Private Car Dependence and On-Road Carbon Emissions in the Context of COVID-19

Zhang

Shao

Wang

et al. 2022

Front. Environ. Sci.

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Shared mobility is becoming increasingly popular worldwide, and travelers show more complex choice preferences during the post-pandemic era. This study explored the role of shared mobility in the context of coronavirus disease (COVID-19) by comparing the travel mode choice behavior with and without shared mobility. Considering the shared mobility services of ride-hailing, ride-sharing, car-sharing, and bike-sharing, the stated preference survey was designed, and the mixed logit model with panel data was applied. The results show that if shared mobility is absent, approximately 50% of motorized mobility users and 84.62% of bike-sharing adopters will switch to using private car and public transport, respectively. The perceived pandemic severity positively affects the usage of car-sharing and bike-sharing, while it negatively affects the ride-sharing usage. Under different pandemic severity levels, the average probabilities of private car choice with and without shared mobility are 38.70 and 57.77%, respectively; thus, shared mobility would alleviate the dependence on private car in post-pandemic future. It also helps to decrease the on-road carbon emissions when the pandemic severity is lower than 53. These findings suggest policymakers to maintain the shared mobility ridership and simultaneously contain the pandemic. Additionally, pricing discount and safety enhancement are more effective than reducing detour time to protect ride-sharing against COVID-19.

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Section: Estimation Resultscontrasting

confidence: 80%

Section: Methodology Surveymentioning

confidence: 99%

Exploring the Role of Shared Mobility in Alleviating Private Car Dependence and On-Road Carbon Emissions in the Context of COVID-19

Zhang

Shao

Wang

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Mileage capacity is important for traveling. König and Grippenkoven (2020) verified that long travel times are a huge usage barrier. Similarly, 70% of the respondents had a 12-km range from the university on e-bike trips (Nematchoua et al, 2020).…”

Section: Mileage Capacitymentioning

confidence: 95%

Factors Affecting Electric Bike Adoption: Seeking an Energy-Efficient Solution for the Post-COVID Era

Yasir

Ahmad

et al. 2022

Front. Energy Res.

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Sustainability think tanks such as the United Nations Organization have a strong focus on achieving economic and environmental sustainability goals globally. On the road to sustainable development, electric bike (E-bike) adoption is crucial. Nevertheless, research on the factors associated with E-Bike use, especially the psychological, financial, and capacity factors, has remained unexplored. This paper extends the theory of planned behavior with six novel factors related to individual choices to analyze E-bike adoption behavior. A sample of 507 Chinese bike riders is collected through the snowball sampling technique. The sample is estimated through structural equation modeling. The key findings are as follows: first, speed capacity, mileage capacity, and real-time camera positively drove E-bike adoption intention. Second, price differentiation negatively affected E-bike adoption intention. Third, the theory of planned behavior factors, including perceived relative advantage, cost savings, subjective norms, perceived behavioral control, and attitudes toward E-bike adoption, proved to be drivers of E-bike adoption intention. Finally, cost savings are the most critical factor of E-bike adoption intention, whereas perceived behavior control is the least critical factor. These results will help green transportation companies and emerging economies promote E-bike adoption to reach the environmental sustainability goals of the United Nations.

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“…Women, especially non-Hispanic Caucasians and individuals with high income are highly unwilling to share vehicular space with a stranger (Wang et al, 2020). With that in mind, researchers examined if and how much people are willing to pay to avoid sharing rides with strangers through various constructs including willingness to accept (WTA) (Konig and Grippenkoven, 2020) and willingness to share (WTS) (Lavieri and Bhat, 2019).…”

Section: Willingness Constructsmentioning

confidence: 99%

“…The estimates of WTS in their study indicated that peoples' willingness to pay to not have an additional passenger in their journey increases significantly for leisure travel (89.71 cents) than for commute travel (48.71 cents). As an indicator for the willingness to choose shared rides, Konig and Grippenkoven (2020) computed the refusal rate (% of shared rides refused by the respondents) instead of WTS.…”

Section: Willingness Constructsmentioning

confidence: 99%

Understanding acceptance of Autonomous Mobility Services using statistical and deep learning approaches

Bala¹

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The emergence of vehicle automation and its subsequent growth has led to new transport service offerings, generally known as Autonomous Mobility Services (AMS), that have the potential to facilitate or even replace human-operated vehicles. AMS contains different forms of potential mobility options which may contradict current transport modal concepts in terms of functionalities. For example, an autonomous shuttle bus which is a form of autonomous transit may serve similarly as an autonomous taxi/robo-taxi in terms of functionalities, coinciding with the concept of Shared Autonomous Mobility Services (SAMS). Even if the functionalities or operational principles are different, peoples' perceptions of sharing rides in any of these services may be alike. Apart from these confusions in functionalities mentioned above, peoples' attitudes and acceptance of AMS, once it's implemented in any form in the public road environment, remains a significant research aspect. To address these issues, this thesis tried to first clearly distinguish different types of AMS. Second, it tried to assemble the progress till now in acceptance-related research of AMS while reviewing the previous study approaches, outcomes, policy implications, and future research directions. Third, this study attempted to understand the acceptance of AMS using statistical and deep learning approaches leveraging both survey and social media data. Fourth, this study tried to present the consequent applicabilities and limitations of using both types of data sources for autonomous vehicle acceptance research. Eventually, this thesis intends to present an overall idea of the AMS acceptance research with future directions leveraging both data sources in an individualistic or combined manner.

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Travellers’ willingness to share rides in autonomous mobility on demand systems depending on travel distance and detour

Cited by 39 publications

References 61 publications

Exploring the Role of Shared Mobility in Alleviating Private Car Dependence and On-Road Carbon Emissions in the Context of COVID-19

Exploring the Role of Shared Mobility in Alleviating Private Car Dependence and On-Road Carbon Emissions in the Context of COVID-19

Factors Affecting Electric Bike Adoption: Seeking an Energy-Efficient Solution for the Post-COVID Era

Understanding acceptance of Autonomous Mobility Services using statistical and deep learning approaches

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