Tork, Nastaran (University of Minnesota Twin Cities), Khani, Alireza (University of Minnesota)

Incentive‑Compatible Mechanism for Integrated Bike Rebalancing and Local Store Deliveries

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Abstract

This study proposes an incentive-compatible framework that enlists regular bike sharing users to perform either station rebalancing or last-mile deliveries within a single, daytime platform. Users disclose walking and biking tolerances, receive bundled task offers generated by a binary optimization model, and bid for their preferred task in a reverse second-price auction. Payoffs reflect a weighted task value that blends inventory impact with an early-delivery bonus.

A two-hour simulation with 2017 Nice Ride Minnesota data confirms the framework’s practicality. Assuming a 90% participation rate, users assigned to rebalancing tasks walked only 8% less than usual while biking 38% less. Every assigned delivery met its deadline, and the redistribution of bikes noticeably narrowed the gap between under- and over-stocked stations. Budget analysis shows that adjusting the weights of the task value factors strongly influences which tasks receive funding and, consequently, the system’s overall benefit.

These findings suggest that crowdsourced daytime rebalancing, paired with on-bike deliveries, can relieve fleet imbalances and help to cut vehicle miles with minimal user burden.