The planning process for bike sharing systems is often complex, involving multiple stakeholders and several considerations: finding hotspots in the potential demand, and dimensioning the system, requires an intimate knowledge of urban mobility patterns and specific local features of the city. The significant costs associated with dynamic rebalancing of bike sharing systems, i.e. with moving bikes across the city to correct the demand imbalance and ensure that they are available where and when they are needed, make correct planning even more critical for the economic viability of the system. In this work, we consider urban environment data from multiple sources and different cities in Europe and the United States to design an automated planning pipeline to place stations in an area with no direct knowledge of the demand. The first step in the planning is to build models of activity patterns and correlate them with features of the urban environment such as land use and mass transit availability; these statistical models can then be used to expand an existing network or even create an entirely new one in a different city. A use case in New York City shows that our system can effectively plan a bike sharing system expansion, providing a valuable first step for the planning process and allowing system designers to identify gaps in existing systems and the locations of potential demand hotspots.

Automatic bike sharing system planning from urban environment features

Chiariotti F.;
2023

Abstract

The planning process for bike sharing systems is often complex, involving multiple stakeholders and several considerations: finding hotspots in the potential demand, and dimensioning the system, requires an intimate knowledge of urban mobility patterns and specific local features of the city. The significant costs associated with dynamic rebalancing of bike sharing systems, i.e. with moving bikes across the city to correct the demand imbalance and ensure that they are available where and when they are needed, make correct planning even more critical for the economic viability of the system. In this work, we consider urban environment data from multiple sources and different cities in Europe and the United States to design an automated planning pipeline to place stations in an area with no direct knowledge of the demand. The first step in the planning is to build models of activity patterns and correlate them with features of the urban environment such as land use and mass transit availability; these statistical models can then be used to expand an existing network or even create an entirely new one in a different city. A use case in New York City shows that our system can effectively plan a bike sharing system expansion, providing a valuable first step for the planning process and allowing system designers to identify gaps in existing systems and the locations of potential demand hotspots.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3498626
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