提出可区分不同路线选择的出行时间预测模型，解决城市路网动态路径影响预测难题。
Generalising Travel Time Prediction To Varying Route Choices In Urban Networks
- 基于图神经网络构建通用出行时间预测框架，显式建模路线选择与出行时间关系。
- 在真实城市路网数据上实现高精度流量与出行时间预测，显著优于传统方法。
- 适合交通规划、智能导航系统研究者使用，尤其关注路径选择多样性影响场景。
以往基于图神经网络的全域出行时间预测方法主要针对典型且重复的出行模式，仅能近似单一需求实现情况，难以捕捉路径选择变化的影响。本文提出通用出行时间预测器（GenTTP），能够有效区分不同路径选择，提供精准的流量与出行时间预测。该框架学习复杂时空交通模式及微观路径选择与出行时间间的关联。填补了现有模型在不同路径分配下无法泛化预测的空白：相同需求在不同路径分布下可能产生截然不同的网络整体结果。
原文摘要 · Abstract (English)
Previous methods that predict system-wide travel time, predominantly grounded in graph neural networks, remain limited to typical and recurring demand patterns. While they successfully predict future congestion following daily commute, they inherently approximate a single demand realisation and fail to capture varying route choices. In this work, we propose a Generalised Travel Time Predictor (GenTTP) that successfully differentiates route choices and offers accurate flow and travel time predictions. Our framework learns to uncover complex spatiotemporal traffic patterns and microscopic relationships between route choices and the resulting travel times. This addresses a critical gap: the lack of travel time prediction models that generalise across varying route assignments, where the same demand can produce substantially different network-wide outcomes depending on how travellers are distributed over available paths.
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