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Promoting public bike-sharing: A lesson from the unsuccessful Pronto system - PubMed

Promoting public bike-sharing: A lesson from the unsuccessful Pronto system

Feiyang Sun et al. Transp Res D Transp Environ. 2018 Aug.

Abstract

In 2014, Seattle implemented its own bike-sharing system, Pronto. However, the system ultimately ceased operation three years later on March 17th, 2017. To learn from this failure, this paper seeks to understand factors that encourage, or discourage, bike-sharing trip generation and attraction at the station level. This paper investigates the effects of land use, roadway design, elevation, bus trips, weather, and temporal factors on three-hour long bike pickups and returns at each docking station. To address temporal autocorrelations and the nonlinear seasonality, the paper implements a generalized additive mixed model (GAMM) that incorporates the joint effects of a time metric and time-varying variables. The paper estimates models on total counts of pickups and returns, as well as pickups categorized by user types and by location. The results clarify that effects of hilly terrain and the rainy weather, two commonly perceived contributors to the failure. Additionally, results suggest that users in the University District, presumably mostly university students, tend to use shared bikes in neighborhoods with a higher household density and a higher percentage of residential land use, and make bike-sharing trips regardless workdays or non-workdays. The paper also contributes to the discussion on the relationship between public transportation service and bike-sharing. In general, users tend to use bike-sharing more at stations that have more scheduled bus trips nearby. However, some bike-sharing users may shift to bus services during peak hours and rainy weather. Several strategies are proposed accordingly to increase bike ridership in the future.

Keywords: Bike-sharing; Built environment; Generalized additive mixed model; Pronto; Temporal factors.

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Figures

Fig. 1.
Fig. 1.

Spatial pattern: average daily pickups and returns.

Fig. 2.
Fig. 2.

Temporal trend: hourly trip counts.

Fig. 3.
Fig. 3.

Temporal trend: system weekly trip counts.

Fig. 4.
Fig. 4.

Estimated smoothing function of time metric.

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