Dynamic vehicle redistribution and online price incentives in shared mobility systems

Abstract: This paper considers a combination of intelligent repositioning decisions and dynamic pricing for the improved operation of shared mobility systems. The approach is applied to London's Barclays Cycle Hire scheme, which the authors have simulated based on historical data. Using model-based predictive control principles, dynamically varying rewards are computed and offered to customers carrying out journeys. The aim is to encourage them to park bicycles at nearby under-used stations, thereby reducing the expected cost of repositioning them using dedicated staff. In parallel, the routes that repositioning staff should take are periodically recomputed using a model-based heuristic. It is shown that a trade-off between reward payouts to customers and the cost of hiring repositioning staff could be made, in order to minimize operating costs for a given desired service level.

• The paper introduces a dynamic repositioning algorithm that optimizes truck routes based on real-time demand, reducing manual redistribution.
• It presents a dynamic incentive pricing model that motivates users to relocate bicycles, thereby enhancing service efficiency.
• Simulation results using London data demonstrate that combining these strategies cuts operational costs and improves system performance, especially during off-peak times.

Overview of Combining Dynamic Repositioning and Pricing in Shared Mobility Systems

This paper presents an integrated approach to managing shared mobility systems, specifically focusing on Public Bicycle Sharing (PBS) schemes through the combination of intelligently routed vehicle repositioning and dynamic pricing incentives. The research is applied and tested within the context of the London Barclays Cycle Hire scheme, leveraging historical data to optimize system performance. The dual-method strategy highlighted in the paper shows the potential to reduce operational costs by minimizing the necessity of manual bicycle redistribution and enhancing the service level through customer participation.

The paper's innovative contribution is detailed in two primary methods:

1. Routing Algorithm for Repositioning Trucks: The researchers developed a dynamic heuristic-based algorithm implemented on a time-expanded network to efficiently route multiple trucks. This algorithm accounts for real-time operations, adjusting truck paths to maximize utility based on expected future demand and current system states. The routing considers trade-offs between manual repositioning and truck operational costs, aiming to offer as many journey opportunities for users as possible.
2. Dynamic Incentive Pricing Scheme: The paper introduces an incentive-based system encouraging users to return bicycles to under-used stations. These incentives are formulated based on the predicted future system states and are aimed at optimizing the distribution of bicycles without solely relying on manual repositioning. The system is designed to account for user behavior, utilizing a model that assumes customers weigh the value of their time against offered incentives when deciding whether to change their destination stations.

Numerical Results

While empirical studies are conducted primarily through simulations based on historical data from London’s PBS, the authors offer insightful findings:

• The study suggests that using price incentives alone can improve service levels significantly, though the effect is more pronounced during off-peak times, such as weekends.
• Incremental additions of trucks beyond a certain threshold result in diminishing returns concerning service level improvements.
• Utilizing both strategies, a balance can be attained between service level performance and reductions in operational expenses, even in highly variable demand conditions like commuting hours.

Implications and Future Directions

The research can have significant implications for urban transportation policy and the operational management of shared mobility systems. By relying less on costly physical redistribution of vehicles, municipalities and private operators can manage their PBS systems more sustainably. The proposed dynamic pricing scheme also provides valuable insights into incorporating economic behavior and customer incentives in the transportation model.

Future research could explore a field study to validate the simulation results further and refine the dynamic pricing mechanism with a comprehensive understanding of customer behavior patterns. Additionally, the model could be extended to consider the effects of external variables such as weather and city events on demand patterns, providing a more robust decision-making tool within the shared mobility context.

In conclusion, the paper provides a mathematical and operational framework that enriches the strategic management of shared mobility systems, reflecting the prospects of economizing resource allocation and maximizing user satisfaction in contemporary urban settings.