A convergent cross-mapping approach for unveiling congestion spatial causality in urban traffic networks

Abstract

Spatial causality in urban traffic networks explores how events or conditions in one location affect those in another. Unveiling congestion spatial causality is crucial for identifying congestion-inducing bottlenecks in traffic networks and offering valuable insights for traffic network management and control. This study introduces the traffic-convergent-cross-mapping (T-CCM) method, a state-space-reconstruction approach from the dynamic system perspective, to identify causality among roads within urban traffic networks using time series data. Simultaneously, it effectively addresses the intricate challenges of uncertainty and interdependency among sensors caused by traffic flow dynamics. Empirical findings from real-world (PeMS-Bay area) traffic speed data validate the effectiveness of the T-CCM method in detecting causality. This study reveals bidirectional causal effects between downstream and upstream roads in short-term congestion generation and dissipation periods, which can pinpoint congestion origins and inform quick traffic management response. Furthermore, it elucidates the long-term causality impacts between distant roads, particularly with regard to traveler choices and road land use attributes, guiding infrastructure investment and public transit improvements.