Tracking the source of congestion based on a probabilistic Sensor Flow Assignment Model

Abstract

Tracking the source of congestion, namely where the congested traffic flow comes from and goes to, is a key prerequisite to understanding the causes of traffic congestion and facilitates more efficient strategies. In this paper, we track the congestion source by estimating the path flow passing through the congested link. A probabilistic sensor flow assignment model is first developed to infer the whereabouts of each vehicle converging into the congestion. Unlike classical path flow estimation methods, we view path flow as the assigned results of sensor flows rather than OD flows. With this new perspective, an assigned rule, which incorporates route choice preference of drivers and spatial–temporal constraint of vehicular trajectory, is constructed to output more realistic assignments. Moreover, as this model finds most possible destination-path combinations rather than partial paths as assigned results, the complete trip of tracking vehicles, including both driving paths and ODs, can be reconstructed. With the reconstructed trips, disaggregated and hybrid path flow estimation methods are developed to track the source of traffic congestion on the bottleneck link.

The open-source pNEUMA dataset is employed to test the proposed and benchmark methods. It demonstrates that our methods can produce a more realistic traffic pattern for congestion tracking. Significant improvements in estimation accuracy have been achieved with the use of sensor flow assignment model. The proposed disaggregated method has also been tested with a city-scale road network. Experiment results demonstrate that our method is more robust to the uncertainty caused by possible destinations than benchmark.