Mining Topological Dependencies of Recurrent Congestion in Road Networks

Abstract

The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.

Impact Analysis of Accidents on the Traffic Flow Based on Massive Floating Car Data

“…In contrast to simulated data, real data is used for the detection of e.g. structural dependencies of the underlying road network in (Tempelmeier et al, 2019) and (Tempelmeier et al, 2021). In order to identify those dependencies the authors detect temporally co-occurring outliers, especially congestion, in the usual traffic flow.…”

Mapping to cells: a map-independent approach for traffic congestion detection

“…In fact, traffic congestion is an accumulation of negative impacts from vehicle deceleration. Existing studies consider traffic congestion as an instantaneous value and use the roadway aggregation speeds 7 , the traffic congestion index 8,9 and the Traffic Performance Index (TPI) 10 , which are considered to be able to comprehensively reflect the road traffic conditions and other indicator variables thresholds to identify or classify traffic congestion, which ignores the impact of speed fluctuations.…”