Research on congestion propagation in large urban city networks has been mainly based on microsimulations of link-level traffic dynamics. However, both the unpredictability of travel behaviors and high complexity of accurate physical modeling remain challenging and simulation results may be far time consuming and not realistic. The main purpose of this paper is to reveal the hidden information during the process of congestion formation by exploring empirical data from large-scale urban networks. Specifically, we aim at studying the spatiotemporal relation of congested links, observing congestion propagation from an macroscopic perspective, and finally identifying critical congestion regimes to aid the design of peripheral control strategies. To achieve these goals, we use maximum connected component of congested links to capture congestion propagation in the city by utilizing a large dataset of 20,000 taxis with GPS data from Shenzhen of China. We present the empirical macroscopic fundamental diagrams of congested regions observed in propagation and quantify the critical congestion regimes. The findings show that the proposed methodology can effectively distinguish the congestion pockets from the rest of the network and efficiently track the congestion evolution in linear time . 3
REF investigates the spatiotemporal relation of congested road segments, and performs an empirical observation on the propagation of congestion.
Empirical Observations of Congestion Propagation and Dynamic Partitioning with Probe Data for Large-Scale Systems
Transportation Research Record
Engineering