基于即时共享率和预测需求密度的一对多车辆共乘匹配

In this paper, an optimization model of the one (driver)-to-many (riders) mode is proposed for the ride-sharing problem with continuous timeframes. The objective of the proposed optimization incorporates both the sharing rates at the current timeframe and the predicted demand densities at trip destinations. This approach characterizes the instantaneous utility and potential utility of trips, thereby improving the overall service rate and quality during the operation period. We then design a demand prediction algorithm. In the feature engineering phases, time-indexed features and spatial-indexed features are extracted, and sequential features at various time intervals are generated. Based on deep learning method, we construct the spatio-temporal auto-sequence net (STAS-Net) capable of processing both spatio-temporal and sequential features, thereby providing highly accurate demand prediction. Finally, the proposed model and algorithm are applied to a real-world scenario to verify their effectiveness. The results indicate that compared with the traditional model, the matching solution generated by the proposed model improves the service rate by 9.57% and reduces the travel distance by 11.54%. Meanwhile, the designed prediction algorithm, STAS-Net, outperforms other prediction algorithms with a mean absolute error of 2.84 and a mean square error of 20.35.