A comparison study on the growth pattern of traffic oscillations in car-following experiments

Zheng, Shi-Teng; Jiang, Rui; Tian, Junfang; Li, Xiaopeng; Jia, Bin; Gao, Ziyou; Yu, Son-Cheol

doi:10.1080/21680566.2022.2123866

Cited by 2 publications

(2 citation statements)

References 49 publications

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“…Finally, traffic congestion spreading has also been investigated as a contagion process (e.g., [49], [50], [51], [52], [10]). In this approach, the road network is abstracted and represented as a graph consisting of a collection of nodes (road junctions/intersections in our case) and links (road segments).…”

Section: Related Workmentioning

confidence: 99%

“…Fundamentally, the question is on how an inherently challenging process like traffic congestion propagates and dissipates over time in urban transport systems. It is known that traffic oscillation itself has two components: formation and propagation [9], [10]. The former can be caused by lane-changing activity or any kind of moving bottleneck [9], [11].…”

Section: Introductionmentioning

confidence: 99%

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Modeling Traffic Congestion Spreading Using a Topology-Based SIR Epidemic Model

Kozhabek,

Chai,

Zheng

2024

IEEE Access

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The continuous urbanisation and increase in vehicle ownership have increasingly exacerbated traffic congestion problems. In this paper, we advocate the use of epidemic theory to model the spreading of traffic congestion in urban cities. Specifically, we use the Susceptible-Infected-Recovered (SIR) model but propose to explicitly consider the road network structure in the model to understand the contagion process of road congestion. This departs from the classical SIR model where homogeneous mixing based on the law of mass action is assumed. For this purpose, we adopt the N-intertwined modeling framework for the SIR model based on continuous-time Markov chain analysis. In our evaluation, we used two real-world traffic datasets collected in California and Los Angeles. We compare our results against both classical and average-degreebased SIR models. Our results show better agreement between the model and actual congestion conditions and shed light on how congestion propagates across a road network. We see the potential application of insights gained from this work on the development of traffic congestion mitigation strategies.INDEX TERMS Congestion spread modeling, epidemics, SIR model, topology,traffic congestion, urban road networks.

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