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Using Geopandas for locating virtual stations in a free-floating bike sharing system
Claudio Rojas1, Rodrigo Linfati2, Robert F Scherer3
1Departamento de Ingeniería Industrial, Universidad de Concepción, Edmundo Larenas 215, 4070043, Concepción, Chile.
Heliyon
|January 23, 2023
Summary
This study optimizes bike-sharing station locations in Latin American cities using geospatial data and mathematical programming. The system improves service availability by over 37% and can significantly reduce emissions and operational costs.
Area of Science:
- Urban planning
- Transportation science
- Computer science
Background:
- Free-floating bike-sharing systems enhance urban mobility.
- Efficient bike station localization is key to managing demand and availability.
Purpose of the Study:
- To develop an efficient geospatial data wrangling methodology for virtual bike station localization.
- To optimize bike station placement in a Latin American city to meet forecasted demand.
Main Methods:
- Implementation in Python utilizing Geopandas and LocalSolver libraries.
- A binary integer mathematical programming model supports decision-making.
- Instances are derived from intercity travel surveys reflecting real travel demand.
Main Results:
- The decision support system recommends virtual bike station locations for peak hours.
- Improved general availability of the bike-sharing system by over 37%.
- Potential reduction of up to 80% in CO2 emissions and nearly 50% in operational costs through user participation in relocation.
Conclusions:
- The developed methodology provides an effective approach to optimizing bike-sharing system operations.
- Virtual station localization strategies significantly enhance service availability and sustainability.
- Integrating user participation in relocation offers substantial environmental and economic benefits.
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