The inconsistency between the spatial and temporal distribution of service vehicles and travel demands has a substantial detrimental effect on the operational efficiency of ride-hailing platforms.This leads to a reduction in platform and service vehicle revenue as well as an inferior passenger experience.To address this issue,this study proposes an innovative approach that incorporates spatial,temporal,and weather-influencing factors to accurately calculate spatiotemporal flow differences.To improve prediction accuracy,a double-layer deep forest model is developed.The double-layer deep forest model improves the accuracy of model prediction by integrating two deep forest models with different input data.Building on the predicted spatiotemporal flow differences,a dual-mode hybrid dispatching algorithm that combines online local dispatching with offline global dispatching is devised.Online local dispatching utilizes integrated parallelism principles and n-stage solution modes to achieve real-time vehicle dispatching,whereas offline global dispatching uses a genetic-matching algorithm.A genetic algorithm is used to determine the best route and matching values for the corresponding vehicle subspaces.Subsequently,an iterative Kuhn-Munkres algorithm is developed,and the mechanism to determine the best matching for all vehicles and subspaces is updated.The experimental results demonstrate the superiority of the proposed prediction model over existing approaches.The model achieves an increase in the average variance of 0.13,an average determinability coefficient enhancement of 0.16,an average absolute error reduction of 2.39,and an average mean-square error improvement of 100.44.Furthermore,the proposed dispatching algorithm reduces the global supply-demand difference by 57.16％and significantly enhances the driver's order acceptance rate by 88.4％.
万方数据
维普
