Tool: Definition of bike-sharing service regions

We focus on the problem of defining optimal bike-sharing service regions that maximize demand coverage under the constraints of construction and operational budgets. Strategically located shared-bike stations can expand coverage, enhance accessibility, and reduce dependence on private vehicles, when integrated with the public transport network. However, limited resources (both capital and operational), such as constraints on bike procurement, station installation, maintenance, and re-balancing make it infeasible to satisfy all demand and require strategic prioritization. Furthermore, in real-world networks there is a coexistence of multi-modal and standalone trip needs: Stations must support both direct bike trips and connections to public transport. This dual role complicates sizing and capacity decisions.

The objective of the model is to maximize covered demand under resource constraints, including investment limitations on station infrastructure, fleet size and daily operational budget. We explicitly consider multi-modal travel patterns and multiple path possibilities for each origin-destination (OD) pair. By incorporating k-order shortest paths, we propose an integer programming optimization model. Our approach, developed by INRIA, prioritizes optimal paths for users while strategically allowing slightly suboptimal paths to enhance coverage and reduce operational costs (e.g., re-balancing). To solve the model, we employ a mathematical solver, Gurobi, and conduct initial tests on synthesized data.