基于ST-GNN和路径优化优化两个乘客的共享订单
Xue Xing1, Yuqi Peng1, Le Wan1
1Jilin University of Chemical Technology, The School of Information and Control Engineering, Jilin, China.
PloS one
|December 9, 2025
概括
本研究介绍了一种使用空间时间图神经网络 (ST-GNN) 和多目标路径规划来提高乘车共享效率的双优化框架. 新方法增强实时匹配,优化路线,减少绕道和排放.
科学领域:
- 智能运输系统 智能运输系统
- 优化城市流动性 优化城市流动性
- 数据科学数据科学数据科学
背景情况:
- 由于复杂的时空需求和交通,城市共享乘车在实时订单匹配和路径规划方面面临挑战.
- 现有的算法很难将这些动态特征整合到现实世界约束下的多目标优化中.
研究的目的:
- 提出一个新的双优化框架,以实现高效的城市动态共享.
- 通过整合时空动态和多目标优化来增强实时订单匹配和路径规划.
主要方法:
- 使用沃罗诺伊多边形构建了一个适应需求的城市空间结构.
- 开发了一个空间时间图神经网络 (ST-GNN),具有多头注意力和变压器机制,以学习城市动态.
- 集成的ST-GNN嵌入式与改进的Dijkstra算法用于多目标路径规划 (距离,绕道,排放).
主要成果:
- 在一个大规模的真实世界数据集上实现了86.6%的匹配成功率.
- 每个订单的平均碳排放量减少0.34公斤CO2.
- 保持了0.1202.2的低平均绕道率.
结论:
- 拟议的框架有效地加强了复杂的乘车共享场景中的时空协作.
- 为智能共享移动系统提供实用和高效的解决方案,促进优化城市交通和低碳旅行.
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