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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Taylor A P M, Sekhar V C S, D’Este M G. Application of accessibility based methods for vulnerability analysis of strategic road networks. Networks and Spatial Economics, 2006, 6(3–4): 267–291
Chandra S, Quadrifoglio L. Critical street links for demand responsive feeder transit services. Computers & Industrial Engineering, 2013, 66(3): 584–592
Burgholzer W, Bauer G, Posset M, Jammernegg W. Analysing the impact of disruptions in intermodal transport networks: a microsimulation-based model. Decision Support Systems, 2013, 54(4): 1580–1586
Taylor A P M, D’Este M G. Concepts of network vulnerability and applications to the identification of critical elements of transport infrastructure. In: Proceedings of the 26th Australasian Transport Research Forum Wellington. 2003
Jenelius E, Petersen T, Mattsson G L. Importance and exposure in road network vulnerability analysis. Transportation Research Part A: Policy and practice, 2006, 40(7): 537–560
Scott M D, Novak C D, Hall A L, Guo F. Network robustness index: a new method for identifying critical links and evaluating the performance of transportation networks. Journal of Transport Geography, 2006, 14(3): 215–227
Qiang Q, Nagurney A. A unified network performance measure with importance identification and the ranking of network components. Optimization Letters, 2008, 2(1): 127–142
Ji X F. Method of bottleneck links identification in road network based on rough set. Journal of Highway and Transportation Research and Development, 2009, 26(9): 120–124
Sullivan L J, Novak C D, Hall A L, Scott M D. Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: a link-based capacity-reduction approach. Transportation Research Part A: Policy and practice, 2010, 44(5): 323–336
Luathep P, Sumalee A, Ho HW, Kurauchi F. Large-scale road network vulnerability analysis: a sensitivity analysis based approach. Transportation, 2011, 38(5): 799–817
Tu Y F, Yang C, Chen X H. Road network topology vulnerability analysis and application. Transport, 2012, 166(2): 95–104
Schintler A L, Kulkarni R, Gorman S, Stough R. Using raster-based GIS and graph theory to analyze complex networks. Networks and Spatial Economics, 2007, 7(4): 301–313
Xu F F, He Z C, Sha Z R. Impacts of traffic management measures on urban network microscopic fundamental diagram. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(2): 185–190
Ma X L, Yu H Y, Wang Y P, Wang Y H. Large-scale transportation network congestion evolution prediction using deep learning theory. PloS One, 2015, 10(3): e0119044
Ma X L, Tao Z M, Wang Y H, Yu H Y, Wang Y P. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 2015, 54: 187–197
Daganzo C F. Urban gridlock: macroscopic modeling and mitigation approaches. Transportation Research Part B: Methodological, 2007, 41(1): 49–62
Geroliminis N, Daganzo F C. Macroscopic modeling of traffic in cities. In: Proceedings of TRB 86th Annual Meeting. 2007
Geroliminis N, Daganzo F C. Existence of urban-scale macroscopic fundamental diagrams: some experimental findings. Transportation Research Part B: Methodological, 2008, 42(9): 759–770
Zhang L L, Garoni M T, Gier J D. A comparative study of macroscopic fundamental diagrams of arterial road networks governed by adaptive traffic signal systems. Transportation Research Part B: Methodological, 2013, 49: 1–23
Geroliminis N, Zheng N, Ampountolas K. A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks. Transportation Research Part C: Emerging Technologies, 2014, 42: 168–181
Yerra M B, Levinson M D. The emergence of hierarchy in transportation networks. The Annals of Regional Science, 2005, 39(3): 541–553
Levinson M D, Yerra M B. Self-organization of surface transportation networks. Transportation Science, 2006, 40(2): 179–188
Ji Y X, Geroliminis N. On the spatial partitioning of urban transportation networks. Transportation Research Part B: Methodological, 2012, 46(10): 1639–1656
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.
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.
Electronic supplementary material
About this article
Cite this article
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
- urban road network
- critical links
- intelligent transportation system
- macroscopic fundamental diagram