Lightweight GCN Encoder and Sequential Decoder for Multi-Candidate Carpooling Route Planning in Road Network

Yucen Gao, Li Ma, Zhemeng Yu, Songjian Zhang, Jun Fang, Xiaofeng Gao, Guihai Chen

Abstract: Carpooling Route Planning (CRP) has become an important issue with the growth of low-carbon traffic systems. We investigate a meaningful and challenging scenario for CRP in industry, where each passenger may have several potential positions to get on and off the car. Traditional graph search algorithms or indexing methods usually consume a lot of time and space or perform poorly.In this paper, we propose an end-to-end encoder-decoder model to plan a route for each many-to-one carpooling order with various data-driven mechanisms such as graph partitioning and feature crossover. The encoder is a filter-integrated Graph Convolution Network with external information fusion combining a supervised pre-training classification task, while the latter mimics a pointer network with a rule-based mask mechanism and a domain feature crossover module. We validate the effectiveness and efficiency of our model based on both synthetic and real-world datasets.