A Lightweight Encoder-Decoder Framework for Carpooling Route Planning

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Carpooling Route Planning</i> (CRP) has become an important issue with the growth of low-carbon traffic systems. We investigate a novel, meaningful and challenging scenario for CRP in industry, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-Candidate Carpooling Route Planning</i> (MCRP) problem, where each passenger may have several potential positions to get on and off the car. We surprisingly notice that this problem can be easily generalized for similar services such as express, takeout, or crowdsensing services, which means MCRP is a new fundamental combinatorial optimization problem. Traditional graph search algorithms or indexing methods are usually time and space consuming or perform poorly, which are not suitable for solving the problem. 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.