Generalising Travel Time Prediction To Varying Route Choices In Urban Networks

2026-05-07

AI Summary

This study addresses the limited generalizability of existing traffic prediction methods across diverse route choice scenarios, which fail to accurately capture network-wide travel time variations arising from differing path allocations under identical travel demand. To overcome this limitation, the paper proposes a Generalized Travel Time Predictor (GenTTP)—the first framework capable of generalizing travel time predictions across a wide spectrum of routing strategies. GenTTP leverages graph neural networks to jointly model spatiotemporal traffic dynamics and microscopic route choice behavior, effectively capturing complex interactions within the road network. Experimental results demonstrate that GenTTP significantly improves the accuracy of both travel time and traffic flow predictions under various path assignment scenarios, exhibiting strong generalization capabilities even for atypical and dynamically evolving travel behaviors.

Abstract

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.

Problem

• travel time prediction
• route choice
• traffic flow
• urban networks
• generalisation

Innovation

• travel time prediction
• route choice
• graph neural networks
• spatiotemporal traffic patterns
• generalisation