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Journal Article

Identifying the most influential roads basedon traffic correlation networks

Authors

Guo,  S.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Zhou,  D.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

/persons/resource/Jingfang.Fan

Fan,  Jingfang
Potsdam Institute for Climate Impact Research;

Tong,  Q.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Zhu,  T.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Lv,  W.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Li,  D.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Havlin,  S.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

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8592oa.pdf
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Citation

Guo, S., Zhou, D., Fan, J., Tong, Q., Zhu, T., Lv, W., Li, D., Havlin, S. (2019): Identifying the most influential roads basedon traffic correlation networks. - European Physical Journal - Data Science, 8, 28.
https://doi.org/10.1140/epjds/s13688-019-0207-7


Cite as: https://publications.pik-potsdam.de/pubman/item/item_23320
Abstract
Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows.