Improving Sustainable Mobility with a Variable Incentive Model for Bike-Sharing Systems Based on Agent-Based Social Simulation

Abstract

Bike-sharing systems (BSS) have been implemented in numerous cities around the world to reduce the traffic generated by motorized vehicles, due to the benefits they bring to the city, such as reducing congestion or decreasing pollution generation. Caused by their impact on urban mobility, the research community has increased their interest in their study, trying to understand user behavior and improving the user experience. This paper has the goal of analyzing the impact of different policies of incentives on the user experience and their impact on the BSS service. An agent-based simulation model has been developed using data collected from the BSS service of Madrid, so-called BiciMad. Route generation has been calculated based o n OpenStreetMaps. The system has been evaluated, analyzing the results generated on different incentive policies. The main conclusion is that variable incentives outperform the current incentive policy of the service. Finally, a sensitivity analysis is presented to validate the proper variability of results for the model parameters.