Lightweight GCN Encoder and Sequential Decoder for Multi-Candidate Carpooling Route Planning in Road Network
Authors: 
Yucen Gao, Li Ma, Zhemeng Yu, Songjian Zhang, Jun Fang, Xiaofeng Gao, Guihai ChenAuthors Info & Claims
Yucen Gao, Li Ma, Zhemeng Yu, Songjian Zhang, Jun Fang, Xiaofeng Gao, Guihai ChenAuthors Info & ClaimsPages 302 - 310
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.
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- Lightweight GCN Encoder and Sequential Decoder for Multi-Candidate Carpooling Route Planning in Road Network
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Published In
May 2024
1928 pages
ISBN:9798400701726
DOI:10.1145/3589335
- General Chairs:
- Tat-Seng Chua,
- Chong-Wah Ngo,
- Program Chairs:
- Ravi Kumar,
- Hady W. Lauw,
- Roy Ka-Wei Lee
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Published: 13 May 2024
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