Assessing service and price sensitivities, and pivot elasticities of public bikeshare system users through monadic design and ordered logit regression

Kaviti, Shruthi; Venigalla, Mohan

doi:10.1016/j.trip.2019.100015

Cited by 12 publications

(2 citation statements)

References 27 publications

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“…Data from Washington, DC, found that among casual users, people of color were less likely to use the more expensive twenty-four-hour pass versus the single-trip fare (Kaviti, Venigalla, and Lucas 2019a). This is consistent with other research from the same city that found that white users were less sensitive to price (Kaviti and Venigalla 2019). However, it is important to recognize the correlation of race and income.…”

Section: Access To and Use Of Vehicle Sharing By Different Populationssupporting

confidence: 93%

Are Shared Vehicles Shared by All? A Review of Equity and Vehicle Sharing

Dill

McNeil

2020

Journal of Planning Literature

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Vehicle sharing services (bikeshare, carshare, and e-scooters) offer the potential to improve mobility and accessibility for disadvantaged populations. This article reviews research related to equity and vehicle sharing, focusing on race/ethnicity, income, gender, age, and disability. We find evidence of disparities in use of shared vehicles, which is only partly explained by lack of physical proximity. Some studies reveal additional barriers to use, particularly for bikesharing. More research on barriers is necessary, particularly for carsharing. We identify several programs aimed at reducing barriers, though evidence of their impact is necessary to inform practice.

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Section: Access To and Use Of Vehicle Sharing By Different Populationssupporting

confidence: 93%

Are Shared Vehicles Shared by All? A Review of Equity and Vehicle Sharing

Dill

McNeil

2020

Journal of Planning Literature

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“…For SPM, operators should understand that higher fares will indeed reduce potential demand for the service. Other studies have found elasticities for price in relation to demand for shared bicycle systems in the range of up to −0.8 for low-income users [67]. In addition, in the case of the SAV, the elasticity estimate with respect to the waiting time was −0.25, which was more inelastic than expected, and may be due to the short nature of the on-campus trips.…”

Section: Elasticities Of Demandmentioning

confidence: 74%

Shared Autonomous Vehicles Competing with Shared Electric Bicycles: A Stated-Preference Analysis

et al. 2022

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Understanding the factors that affect the uptake of emerging transport modes is critical for understanding if and how they will be used once they are implemented. In this study, we undertook a stated-preference analysis to understand the factors that affect the use of shared autonomous vehicles and shared personal mobility (micromobility) as competing modes on a university campus in Korea. We applied a binary logit model, which included time and cost variables as well as the perceptions of convenience (in-car congestion and availability) and safety. For autonomous vehicles, the cost- and time-related demand elasticities were estimated to be −0.45 and −0.25, respectively, while the cost elasticity for shared electric bicycles was −0.42. The elasticities of perceived convenience (availability) and safety for the shared electric bicycle system were estimated to be 0.72 and 0.29, respectively. Finally, the elasticity for perceived convenience (in-car congestion) of the shared autonomous vehicle was 0.42. Our results show that there is an innate preference for shared autonomous vehicles when these are compared to shared personal mobility, and that the effect of subjective variables (convenience and safety) on the use of emerging transport modes is as important as traditional cost and time variables.

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Will temporarily free bike sharing change transport behavior forever? Evidence from a free rides’ promotion on trip demand

Weschke

2024

Transportation

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The relation between price and transport demand is one of the main aspects of transport mode choice. While price elasticities are well known for conventional transport modes like driving or public transport, only few studies exist dealing with fares and prices for rather new (shared) modes like bike sharing. In particular, hardly no evidence is available on the impact of the usage fee on trip demand for urban bike share systems. Therefore, the present paper develops an empirical approach to estimate the impact of a temporarily introduced 30-day free bike share rides promotion in Boston, MA in summer 2022. Based on daily trip data of bike sharing systems in Boston, MA and Washington, D.C., a difference-in-differences model is estimated to analyze the impact of the free fare. Results show that trip demand rise by up to 55% due to the waived usage fee during the time of the promotion. Furthermore, model results reveal that trip demand stays at a 20% increased level even three months after the end of the fare free program.

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Assessing service and price sensitivities, and pivot elasticities of public bikeshare system users through monadic design and ordered logit regression

Cited by 12 publications

References 27 publications

Are Shared Vehicles Shared by All? A Review of Equity and Vehicle Sharing

Are Shared Vehicles Shared by All? A Review of Equity and Vehicle Sharing

Shared Autonomous Vehicles Competing with Shared Electric Bicycles: A Stated-Preference Analysis

Will temporarily free bike sharing change transport behavior forever? Evidence from a free rides’ promotion on trip demand

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