Citation: | ZHAO Xueting, HU Liwei, KOU Fangling. Determination of Influence Range of Urban Traffic Congestion and Identification of Key Road Sections[J]. Journal of Southwest Jiaotong University, 2024, 59(6): 1389-1397. doi: 10.3969/j.issn.0258-2724.20220413 |
In order to determine the influence range of non-point source traffic congestion and identify key road sections, the risk field strength theory and the regional growth method were introduced to improve the plume model. The maximum and core influence ranges were determined, and key road sections were identified. First, the “source-path-sink” of non-point source traffic congestion was defined, and the “source” risk field strength model of non-point source traffic congestion was constructed based on the traffic survey data and traffic status data in Guiyang in 2021. By considering the traffic influence of different land use properties and the adjustment coefficient of different road grades for calibration, the identification model of the maximum influence range was established based on different “source” risks, and the regional growth method was introduced to establish a model for determining the influence range of the core area of the road section. Finally, based on the traffic volume, length, and time of the road section, the damage evaluation index of the road section with traffic congestion was constructed, and quantitative research on the degree of traffic congestion was carried out. The original model, the improved model, and the actual situation were compared. The results show that different land use properties and different road grades have different influences on traffic congestion in road sections. The improved identification model of influence range based on different “source” risks is more effective, and the prediction accuracy is increased by 3.54%, which is closer to the actual situation. With the regional growth method, the accuracy of the influence range of the core area of the road section with traffic congestion is higher, which is 2.66% different from the actual situation. In summary, a new idea for the delineation of the cumulative risk range of urban non-point source traffic congestion and the determination of key risk paths is provided, also serving as a theoretical basis for further governance of different types of traffic congestion.
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