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Paper ThAT7.6

Xu, Yicheng (Beijing Jiaotong University), Zhu, Li (Beijing Jiaotong University), Zhang, Zhouhao (Beijing Jiaotong University)

End-To-End Learning for Passenger Flow Prediction Using Train Regulation Task Loss

Scheduled for presentation during the Regular Session "Rail Traffic Management I" (ThAT7), Thursday, September 26, 2024, 12:10−12:30, Salon 15

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 8, 2024

Keywords Theory and Models for Optimization and Control, Rail Traffic Management

Abstract

列车自动调节对于提高 城市轨道交通的效率和乘客满意度 公交,动态客流是一个关键参数 指导列车监管决策。解决 客流预测挑战,机器学习 基于该方法在城市轨道交通中应用广泛,具有 在模型过程中主要关注预测准确性 训练。然而,这种对预测的独家强调 准确性往往忽略了提高的总体目标 列车调节的效率和准确性,需要 在城市轨道交通系统中具有优先权。本文 介绍一种基于图神经网络 (GNN) 的端到端 针对客流的学习方法,我们设计了一种 将基于规则的损失函数作为准则 预测模型。此外,模型预测控制 (MPC)框架被纳入损失设计 功能。这种创新策略有效地缓解了 需要复杂的特征工程并同时进行 简化系统复杂性。广泛的仿真结果 提供

 

 

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