Mapping Information Flows in Urban Traffic Networks: A Transfer Entropy Approach.

Abstract

This study applies Transfer Entropy (TE) to floating car data from the urban road network of Catania (Italy) to investigate directional dependencies in traffic flows and identify relevant patterns of information transfer. Unlike traditional correlation-based approaches, TE captures asymmetric directed information dependencies between road segments, enabling the identification of predictive relationships among congestion states across the network. We constructed TE-based influence networks using traffic speed data collected over 15 min intervals across 86 road segments over six weeks. The analysis examined information transfer using history lengths of 1, 2, and 3 observations, corresponding to 15, 30, and 45 min of past information, revealing distinct patterns between time windows. Key findings include the identification of recurring information-transfer relationships across multiple history lengths, and road segments with comparatively high outgoing or incoming information strength. Notably, the morning and evening time windows differ in network density, total strength and highest-ranked nodes, although some of these patterns depend on the discretisation and history length adopted, highlighting the dynamic nature of urban traffic systems. This approach shifts focus from traditional traffic prediction to network prioritisation, allowing the identification of candidate locations for enhanced traffic monitoring with minimal data requirements. The methodology provides practical insights for optimising sensor placement and informing further traffic-management analysis based on predictive influence patterns rather than congestion levels alone.