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2020 | OriginalPaper | Chapter

Critical Section Identification in Road Traffic Network Based on Spatial and Temporal Features of Traffic Flow

Authors : Fei Su, Xiaofang Zou, Yong Qin, Shaoyi She, Hang Su

Published in: Green, Smart and Connected Transportation Systems

Publisher: Springer Singapore

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Abstract

The capacity of critical section is one of the important reasons for leading to urban road traffic congestion. The identification of critical section has great significance to alleviate traffic congestion, and can provide support for traffic planning, network transformation, residents’ travel plans and so on. Based on the spatial and temporal features of traffic flow, the critical section is defined as the one which has more contribution to the overall network and has greater influence on other sections in this paper. In the section importance measurement framework, space-time distribution is used to explain the contribution of one section to the network, space-time influence is measured to describe its influence on other sections, and the critical section is given by the ranking of section importance. At last, the proposed model is applied in a subset of Beijing’s road network, and the results show that the model is practical and feasible, and can identify critical section in road traffic network effectively.

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Literature
1.
Hossain P, Sayed NSB (2005) Network performance improvement under stochastic events with long-term effects. Transportation 32:65–85CrossRef
2.
Sanso B, Milot L (1999) Performability of a congested urban transportation network when accident information is available. Transp Sci 33(1):68–79CrossRef
3.
Nicholson A, Du ZP (1997) Degradable transportation systems: an integrated equilibrium model. Transp Res Part B: Methodol 31(3):209–223CrossRef
4.
D’Este GM, Taylor MAP (2001) Modelling network vulnerability at the level of the national strategic transport network. J East Asia Soc Transp Stud 4(2):1–14
5.
Tu YF, Yang C, Chen XH (2010) Analysis of road network topology vulnerability and critical links. J Tongji Univ (Nat Sci) 38(3): 364–367, 379
6.
Hou LW, Jiang F (2004) Study on the relative importance of links in urban roads network. Syst Eng Theory Methodol Appl 13(5):425–428MathSciNet
7.
Huang ZF (2009) Order of link trait based on significance and reliability. J Chongqing Jiaotong Univ (Nat Sci) 28(4):742–744
8.
Zhao Y, Li H, Wang F (2014) Identifying key sections in urban road network based on k-shell. Syst Eng 32(5):105–110
9.
Scott DM, Novak DC, Aultman-Hall L et al (2006) Network robustness index: a new method for identifying critical links and evaluating the performance of transportation networks. J Transp Geogr 14(3):215–227CrossRef
10.
Taylor MAP, D’Este GM, Srinckerhoff P et al (2003) Concepts of network vulnerability and applications to the identification of critical elements of transport infrastructure. Australasian Transport Research Forum, Wellington, New Zealand
11.
Zou ZY, Li L, Gao JZ et al (2011) A network efficiency measure for congested transportation networks. In: The 2011 international conference on computational intelligence and software engineering, Wuhan, pp 843–846
12.
Liao JQ (2014) An evaluation algorithm for important links of the transportation network based on connect reliability. Mod Comput 11:1–11
13.
Zhong R (2013) Research of key nodes and important sections of the road network. Beijing University of Posts and Telecommunications, Beijing
14.
Su F, Dong H, Jia L, Sun X (2017) On urban road traffic state evaluation index system and method. Mod Phys Lett B 31(1):1650428MathSciNetCrossRef
15.
Wang YF, He HL (2007) Spatial data analysis method. Science Press, Beijing, pp 56–57
16.
Neuman EJ, Mizruchi MS (2010) Structure and bias in the network autocorrelation model. Soc Netw 32(4):290–300CrossRef
17.
Peeters D, Thomas I (2009) Network autocorrelation. Geogr Anal 41(4):436–443CrossRef
18.
Kamarianakis Y, Prastacos P (2005) Space-time modeling of traffic flow. Comput Geosci 31(2):119–133CrossRef
19.
Montis AD, Caschili S, Chessa A (2011) Time evolution of complex networks: commuting systems in insular Italy. J Geogr Syst 13(1):49–65CrossRef
20.
Wang J, Cheng T, Heydecker B et al (2010) STARIMA for journey time prediction in London. In: The 5th IMA conference on math in transport, UCL, London, 12–14 April 2010
21.
Box G, Jenkins G (1976) Time series analysis: forecasting and control. Wiley, San Francisco, USA, pp 256–268MATH
22.
Chandra SR, Al-Deek H (2008) Cross-correlation analysis and multivariate prediction of spatial time series of freeway traffic speeds. Transp Res Rec 2061:64–76CrossRef
23.
Wu CF (2015) Research on the evaluation of urban road traffic state based on the combination method. Jiangxi University of Science and Technology, Ganzhou, pp 26–30
24.
Petter H (2003) Congestion and centrality in traffic flow on complex networks. Adv Complex Syst 6(2):163–176CrossRef
Metadata
Title
Critical Section Identification in Road Traffic Network Based on Spatial and Temporal Features of Traffic Flow
Authors
Fei Su
Xiaofang Zou
Yong Qin
Shaoyi She
Hang Su
Copyright Year
2020
Publisher
Springer Singapore
Book
Green, Smart and Connected Transportation Systems

Print ISBN: 978-981-15-0643-7

Electronic ISBN: 978-981-15-0644-4

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-0644-4_96

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