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In order to realize the rational delivery and effective dispatch of urban public self-vehicles, according to the randomness and time-varying of public bicycle demand, a demand forecast based on random forest and spatiotemporal clustering is proposed, and based on this, the user-based site rebalancing price incentive mechanism is implemented. Combining the demand of public bicycles with time factors, meteorological factors, associated sites, and other variables, using logarithmic optimization to reduce the impact of outliers, establish a random forest regression model. Secondly, based on this, a dynamic price incentive model is constructed to realize the rebalancing of user-based rental vehicles. The validity and feasibility of the dynamic price incentive model are verified by taking the historical data of public bicycle operation in the Bay Area as an example.

Shared Bicycle Scheduling Model Based on Price Incentive Mechanism