Test the generalization ability of instant delivery model and optimize the algorithm

With the acceleration of urbanization, how to efficiently integrate multi-city traffic data and the accuracy of route prediction has become a hot research topic. This study focuses on this and proposes an optimization method based on the DRL4Route network, which is an advanced route planning network combined with deep reinforcement learning technology, with powerful complex data processing and dynamic decision-making capabilities. On this basis, the data sets from different cities are innovatively fused. Through repeated experiments and comparisons, different interval segments of the fused data are deeply analyzed, and the best interval segment is finally determined by using cross-validation and performance evaluation. It is found that the network model can fully exploit the value of the data and realize more accurate time prediction and route prediction in this interval. In order to further verify the effectiveness and universality of the method, the data of each city is optimized by gradually increasing the data zone segment. The experimental results show that with the reasonable increase of data segments, the line prediction of each city is improved by 0.79%-4.12%. The results of this research can provide a new algorithmic idea for future data increase, which can optimize the best model under a certain amount of data, provide a new idea for future data analysis, and also provide data reference for researchers in related fields.