Highlights We estimated hourly O-D demand in Lisbon Bike Sharing System. Hurdle models showed a better fit than GLM and Zero-augmented regression models. The weather and the hour distribution showed statistically significant effects. Intermodality effects also exhibited statistically significant effects. Urban planners can use our findings to design and expand robust BSS networks.
Abstract Bike Sharing Systems (BSS) have gained popularity all over the world. BSS success requires efficient location of stations and the rebalancing of the network, both of which depend on predicted demand and flows between stations. Therefore, predicting BSS demand is essential to optimize logistic operations, as well as network reliability and availability. The present work develops statistical models to predict the hourly origin–destination demand in the Lisbon BSS network. Models predict the number of trips that will occur between an origin station and a destination station (OD pair) at a certain hour, as well as the duration of such trips. This study also examines the effect of several explaining variables, including the weather, intermodality effects, effects of station location close to different types of points of interest and the time/hour effect. These effects were all tested using different statistical models, including the Generalized Linear Models (GLM) and zero-augmented ones: the hurdle model and the zero-inflated model. The hurdle model showed the best fit to the data and several explaining variables exhibited statistical significant effects. A discussion on the potential logistic and policy impacts of such findings is also provided, so that BSS network design and operations can incorporate them within the urban mobility system policy.
Predicting hourly origin–destination demand in bike sharing systems using hurdle models: Lisbon case study
2021-10-08
13 pages
Article (Journal)
Electronic Resource
English
Station-Level Hourly Bike Demand Prediction for Dynamic Repositioning in Bike Sharing Systems
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|Transportation Research Record | 2021
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