Impact of pricing and transit disruptions on bikeshare ridership and revenue

Kaviti, Shruthi; Venigalla, Mohan; Zhu, Shanjiang; Lucas, Kimberly; Brodie, Stefanie

doi:10.1007/s11116-018-9904-5

Cited by 25 publications

(19 citation statements)

References 15 publications

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“…A report by Venigalla et al (2018) and research paper by Kaviti et al (2018) discussed the impact of the launch of $2/ trip STF by CaBi on its revenue and ridership at the jurisdiction level. These two studies examined the interrelationship of revenue and ridership with other system variables such as supply (as measured by the number of stations and bike racks or docks), jurisdiction, seasonality, transit disruptions, day of week and precipitation.…”

Section: Studies Related To Impact Of Pricing On Usagementioning

confidence: 99%

“…Though notable changes were observed in revenues aggregated at the jurisdiction level, the paper could not verify if the changes observed in revenues after the introduction of STF were, in fact, attributable to the introduction of STF. The analysis performed by Kaviti et al (2018) was primarily based on ridership and revenue data aggregated by month and jurisdiction, which has no fidelity at the daily level and station level. Furthermore, in normalizing revenues and ridership on a 'per-dock' basis, the analysis by Kaviti et al (2018) not only included new stations with sparse ridership but also diluted the true impact of the introduction of single-trip fare at stations that have high ridership.…”

Section: Studies Related To Impact Of Pricing On Usagementioning

confidence: 99%

“…The analysis performed by Kaviti et al (2018) was primarily based on ridership and revenue data aggregated by month and jurisdiction, which has no fidelity at the daily level and station level. Furthermore, in normalizing revenues and ridership on a 'per-dock' basis, the analysis by Kaviti et al (2018) not only included new stations with sparse ridership but also diluted the true impact of the introduction of single-trip fare at stations that have high ridership. Therefore, disaggregate analysis of the data at station level (i.e., analysis of individual trips and revenue transactions by station) could provide additional valuable insights on the impact of STF.…”

Section: Studies Related To Impact Of Pricing On Usagementioning

confidence: 99%

“…The results show providing helmets, reducing subscription fees, and adding flexible subscriptions to users may have contributed to a 50% increase in Citycycle ridership in just six months. Kaviti et al (2018) studied the impact of introducing single-trip fare (STF) for $2 on CaBi ridership and revenue. The results showed that introducing this new fare option increased the monthly ridership for first-time casual users and all casual users by 79% and 41%, respectively.…”

Section: Studies Related To Impact Of Pricing On Usagementioning

confidence: 99%

“…Pucher et al (2010) and De Nazelle et al (2011) have addressed the infrastructure and pricing policy implications on cycling usage. Despite the importance of pricing to bikeshare patronage, few studies focused on the impact of a well-defined pricing strategy on revenue and ridership (Venigalla et al 2018(Venigalla et al , 2019Kaviti 2018;Kaviti et al 2018;Kaviti and Venigalla 2019).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Impact of Bikesharing Pricing Policies on Usage and Revenue: An Evaluation Through Curation of Large Datasets from Revenue Transactions and Trips

Venigalla

Kaviti

Brennan

2020

J. Big Data Anal. Transp.

Self Cite

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A sustainable and robust stream of revenues is an essential element of the economic sustainability of bikeshare systems. To this effect, there is an unrelenting need to quantify and understand the impacts of pricing policies and operational considerations on a bikeshare system's revenue and ridership. A notable gap exists in literature on studies related to the impact of changes in pricing policy on ridership and revenue. The primary objective of this research was to assess the impact of the introduction of a $2 per trip single-trip fare (STF) product for casual users by Capital Bikeshare (CaBi), the bikeshare system in the Washington DC metro area, on its ridership and revenue. Two-year ridership and revenue transaction datasets of CaBi were used in the analysis. A substantial data curation effort was undertaken to fuse elements between the two large transactional datasets. The effort not only facilitated the impact assessment but also enhanced the value of ridership dataset by identifying trips made by casual users by the type of fare product they purchased. The casual bikeshare user revenues were traced to individual bikeshare stations where trips originated, which allowed the comparison between revenues and ridership 'before' and 'after' the launch of STF at the station level. Over 22 million records on individual bikeshare trips and revenue transactions for 3 years and 330 bikeshare stations were analyzed. The results showed a statistically significant increase in casual user ridership after the introduction of STF. There was a statistically significant decrease in revenue per ride. Statistical tests indicated that these changes might be attributable to the introduction of STF. The methods used in this study are transferable. They can be used for curating ridership data and studying the impacts of bikeshare pricing policy changes on system usage and revenues at various public bikesharing systems with similar characteristics as Capital Bikeshare.

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Section: Studies Related To Impact Of Pricing On Usagementioning

confidence: 99%

Section: Studies Related To Impact Of Pricing On Usagementioning

confidence: 99%

Section: Studies Related To Impact Of Pricing On Usagementioning

confidence: 99%

Section: Studies Related To Impact Of Pricing On Usagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Impact of Bikesharing Pricing Policies on Usage and Revenue: An Evaluation Through Curation of Large Datasets from Revenue Transactions and Trips

Venigalla

Kaviti

Brennan

2020

J. Big Data Anal. Transp.

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The Role of Bike Sharing in Promoting Transport Resilience

Liu

Coffman

et al. 2021

Netw Spat Econ

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A resilient transport network, which is significant for urban sustainability and security, is characterized by its ability to recover from disruptions subject to natural and man-made disasters. Bike sharing could act as a viable alternative in the case of public transit disruptions given its flexibility and various social, environmental, and economic benefits. This study aims to estimate quantitatively the potential of bike sharing to promote transport resilience, by using autoregressive negative binomial time series model to investigate the effects of public transit closures on bike sharing demand in Washington, D.C. area during 2015–2017. We find that (1) bike sharing can act as a supplementary mode to enhance urban transport resilience in the case of complete transit closure; (2) the proximity of bike sharing docks to metro stations has a powerful effect on propensity to use a bike sharing program; and (3) extreme weather is one of major barriers to bicycling. Planners can enhance resilience of urban transport networks by fully considering the capacity and usage of bike sharing docks, as well as the coherence of metro stations and bike sharing docks, in distributing and rebalancing activities.

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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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Impact of pricing and transit disruptions on bikeshare ridership and revenue

Cited by 25 publications

References 15 publications

Impact of Bikesharing Pricing Policies on Usage and Revenue: An Evaluation Through Curation of Large Datasets from Revenue Transactions and Trips

Impact of Bikesharing Pricing Policies on Usage and Revenue: An Evaluation Through Curation of Large Datasets from Revenue Transactions and Trips

The Role of Bike Sharing in Promoting Transport Resilience

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

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