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Related Experiment Video

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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Published on: January 20, 2023

Features that influence bike sharing demand.

Alexandra Cortez-Ordoñez¹, Pere-Pau Vázquez², Jose Antonio Sanchez-Espigares³

¹ViRVIG Group Department of Computer Science, UPC-BarcelonaTECH, C/ Jordi Girona 1-3, 08034 - Barcelona, Spain.

|September 23, 2024

Summary

Predicting bike arrivals and departures in urban bike sharing systems (BSS) is key for efficient operations. This study found that temporal factors like month and weather conditions significantly influence bike sharing demand, more than station location features.

Keywords:

Bike Sharing Systems Bike demand forecasting Feature importance

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Area of Science:

Urban Planning
Transportation Science
Data Science

Background:

Bike Sharing Systems (BSS) are increasingly popular for sustainable urban transport.
Customer satisfaction in BSS relies on bike and dock availability, necessitating efficient rebalancing operations.
Predicting user demand is crucial for effective BSS management and urban transport planning.

Purpose of the Study:

To identify key features influencing bike arrivals and departures in Barcelona's BSS.
To evaluate the predictive power of temporal, weather, and spatial features on BSS demand.
To enhance the operational efficiency of BSS through improved demand forecasting.

Main Methods:

Utilized a Random Forest model for predictive analysis.
Analyzed a one-year dataset of bike sharing activity in Barcelona.
Incorporated features including weather data, station characteristics, and nearby spatial facilities.

Main Results:

Temporal features, specifically months, and weather variables like temperature and pressure, were strong predictors of bike arrivals and departures.
Holidays and station altitude also showed significant influence on demand.
Spatial features within a 200-meter radius had a minimal impact on prediction accuracy.

Conclusions:

Demand forecasting for BSS can be significantly improved by focusing on temporal and weather-related factors.
While station characteristics matter, their spatial context appears less critical than dynamic environmental and temporal influences.
Optimizing BSS rebalancing strategies requires prioritizing predictions based on identified key features.