An identification model of urban critical links with macroscopic fundamental diagram theory

Abstract

How to identify the critical links of the urban road network for actual traffic management and intelligent transportation control is an urgent problem, especially in the congestion environment. Most previous methods focus on traffic static characteristics for traffic planning and design. However, actual traffic management and intelligent control need to identify relevant sections by dynamic traffic information for solving the problems of variable transportation system. Therefore, a city-wide traffic model that consists of three relational algorithms, is proposed to identify significant links of the road network by using macroscopic fundamental diagram (MFD) as traffic dynamic characteristics. Firstly, weightedtraffic flow and density extraction algorithm is provided with simulation modeling and regression analysis methods, based on MFD theory. Secondly, critical links identification algorithm is designed on the first algorithm, under specified principles. Finally, threshold algorithm is developed by cluster analysis. In addition, the algorithms are analyzed and applied in the simulation experiment of the road network of the central district in Hefei city, China. The results show that the model has good maneuverability and improves the shortcomings of the threshold judged by human. It provides an approach to identify critical links for actual traffic management and intelligent control, and also gives a new method for evaluating the planning and design effect of the urban road network.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 51308021). The authors would like to thank Wanbao Gao and Qiang Shu (Hefei Gelv Information Technology Co., Ltd) for assisting with their investigation and simulation data extraction effort in the Central District, Hefei city, China.

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Correspondence to Haiyang Yu.

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Wanli Dong received the ME degree in transportation engineering (traffic information and control system) from Hefei University of Technology, China in 2008. She is currently working toward the PhD degree in the School of Transportation Science and Engineering, Beihang University, China. Her research interests include intelligent transportation systems, traffic information analysis and traffic control studies.

Yunpeng Wang is a professor in the School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beihang University, China. His research interests include intelligent transportation systems, traffic safety, and connected vehicle.

Haiyang Yu is an assistant professor in School of Transportation Science and Engineering, Beihang University, China. His research interests include intelligent transportation systems, traffic information analysis and deep learning studies.

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Dong, W., Wang, Y. & Yu, H. An identification model of urban critical links with macroscopic fundamental diagram theory. Front. Comput. Sci. 11, 27–37 (2017). https://doi.org/10.1007/s11704-016-6080-7

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Keywords

  • urban road network
  • critical links
  • intelligent transportation system
  • macroscopic fundamental diagram