Volume 40 Issue 4
Aug.  2022
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CHEN Siyu, LI Jie, HU Yancheng, JIANG Yu. An Evaluation and Analysis on the Resilience of the Urban Local Road Network for Recurrent Congestions[J]. Journal of Transport Information and Safety, 2022, 40(4): 138-147. doi: 10.3963/j.jssn.1674-4861.2022.04.015
Citation: CHEN Siyu, LI Jie, HU Yancheng, JIANG Yu. An Evaluation and Analysis on the Resilience of the Urban Local Road Network for Recurrent Congestions[J]. Journal of Transport Information and Safety, 2022, 40(4): 138-147. doi: 10.3963/j.jssn.1674-4861.2022.04.015

An Evaluation and Analysis on the Resilience of the Urban Local Road Network for Recurrent Congestions

doi: 10.3963/j.jssn.1674-4861.2022.04.015
  • Received Date: 2022-02-16
    Available Online: 2022-09-17
  • To alleviate the state of urban noise, energy consumption, and carbon emission caused by recurrent traffic congestions, and to improve the ability to resist impacts of a short-term surge in traffic flow, macroscopic fundamental diagrams and performance profiles are combined to quantify the resilience of the urban local road network. Five evaluation indices, including robustness index, ratio of loss areas, rapid recovery, difference of peak flows, and difference of critical densities, are proposed to reflect characteristics of the resilience in the stages of performance degradation, stability, and recovery. The Kendall method is used to test the consistency of each weighting method, and the optimal weight is obtained based on the CRITIC for multi-attribute decision making. Furthermore, a combined method using weighting method and fuzzy logic is proposed to evaluate the resilience of the urban local road network, and the resilience score is graded by the Likert scale. Taking a local road network in the city of Changsha as a case study. Improvement schemes for the resilience are designed, and schemes of traffic signal timing are carried out and optimized to improve the resilience of recurrently congested intersections on key road sections. The evaluation indices of the resilience of the local road network are calculated based on the outputs of VISSIM simulations. The results show that scheme 8, 10, and 16 can effectively absorb the short-term surge in traffic flowand adapt to traffic states on the road network. The scheme 14 has the best performance out of all schemes. The comprehensive resilience score of the urban local road network presents an upward trend of non-linear growth with the increasing number of signal optimized sections. The optimization of traffic signal timing improves resilience properties of the local road network, and then reduces the negative impacts of some key sections on the resilience of urban local road network. Besides, different methods for evaluating the resilience make distinct ranking results.The ranking results based on difference of peak flows are more similar to the results of vulnerability indices, while the ranking results based on ratio of loss areas are more similar to the results of loss of resilience. The proposed evaluation indices, not confined to a single attribute of resilience, can reflect the response process of road network under disruption more comprehensively and objectively.

     

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  • [1]
    李亚, 翟国方, 顾福妹. 城市基础设施韧性的定量评估方法研究综述[J]. 城市发展研究, 2016, 23(6): 113-122. doi: 10.3969/j.issn.1006-3862.2016.06.016

    LI Y, ZHAI G F, GU F M. Review on methods of quantification of urban infrastructure resilience[J]. Urban Development Studies, 2016, 23(6): 113-122. (in Chinese) doi: 10.3969/j.issn.1006-3862.2016.06.016
    [2]
    GONCALVES L A P J, RIBEIRO P J G. Resilience of urban transportation systems. Concept, characteristics, and methods[J]. Journal of Transport Geography, 2020(85): 102727.
    [3]
    MURRAY-TUITE P M. A comparison of transportation network resilience under simulated system optimum and user equilibrium conditions[C]. 2006 Winter Simulation Conference, Monterey, CA, USA: IEEE, 2006.
    [4]
    FRECKLETON D, HEASLIP K, LOUISELL W, et al. Evaluation of resiliency of transportation networks after disasters[J]. Transportation Research Record: Journal of the Transportation Research Board, 2012, 2284(1): 109-116. doi: 10.3141/2284-13
    [5]
    LASKAR J I, SEN M K, DUTTA S, et al. A flood resilience analytics framework for housing infrastructure systems based on dempster-shafer(evidence)theory[J]. Journal of Performance of Constructed Facilities, 2021, 35(6): 04021073. doi: 10.1061/(ASCE)CF.1943-5509.0001615
    [6]
    AYDIN N Y, DUZGUN H S, WENZEL F, et al. Integration of stress testing with graph theory to assess the resilience of urban road networks under seismic hazards[J]. Natural Hazards, 2018, 91(1): 37-68. doi: 10.1007/s11069-017-3112-z
    [7]
    ZHU Y, XIE K, OZBAY K, et al. Data-driven spatial modeling for quantifying networkwide resilience in the aftermath of hurricanes irene and sandy[J]. Transportation Research Record, 2017, 2604(1): 9-18. doi: 10.3141/2604-02
    [8]
    BALAL E, VALDEZ G, MIRAMONTES J, et al. Comparative evaluation of measures for urban highway network resilience due to traffic incidents[J]. International Journal of Transportation Science and Technology, 2019, 8(3): 304-317. doi: 10.1016/j.ijtst.2019.05.001
    [9]
    顾金刚, 付强, 胡建伟. 基于排队时间指数的信号控制路口交通拥堵评价方法[J]. 交通信息与安全, 2020, 38(6): 80-86. doi: 10.3963/j.jssn.1674-4861.2020.06.011

    GU J G, FU Q, HU J W. Traffic congestion status evaluation for signal-controlled intersection based on queuing time index[J]. Journal of Transport Information and Safety, 2020, 38 (6): 80-86. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2020.06.011
    [10]
    TANG J Q, HEINIMANN H R. A resilience-oriented approach for quantitatively assessing recurrent spatial-temporal congestion on urban roads[J]. Plos One, 2018, 13(1): 1-22.
    [11]
    吕彪, 高自强, 管心怡, 等. 基于日变交通配流的城市道路网络韧性评估[J]. 西南交通大学学报, 2020, 55(6): 1181-1190. https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT202006007.htm

    LYU B, GAO Z Q, GUAN X Y, et al. Resilience assessment of urban road network based on day-to-day traffic assignment[J]. Journal of Southwest Jiaotong University, 2020, 55 (6): 1181-1190. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT202006007.htm
    [12]
    AMINI S, TILG G, BUSCH F, et al. Evaluating the impact of real-time traffic control measures on the resilience of urban road networks[C]. 21st International Conference on Intelligent Transportation Systems, Maui, Hawaii, USA: IEEE, 2018.
    [13]
    KIM S, YEO H. A flow-based vulnerability measure for the resilience of urban road network[J]. Procedia-Social and Behavioral Sciences, 2016(218): 13-23.
    [14]
    HOOGENDOORN S P, KNOOP V L, LINT H V, et al. Applications of the generalized macroscopic fundamental diagram[C]. Traffic and Granular Flow'13, Julich, Germany: Springer International Publishing, 2015.
    [15]
    TANG J Q, HEINIMANN H R, HAN K, et al. Evaluating resilience in urban transportation systems for sustainability: a systems-based bayesian network model[J]. Transportation Research Part C: Emerging Technologies, 2020(121): 102840.
    [16]
    赵映璎, 马维珍, 温海燕. 隧道施工应急系统韧性评价[J]. 土木工程与管理学报, 2021, 38(3): 167-172. doi: 10.3969/j.issn.2095-0985.2021.03.027

    ZHAO Y Y, MA W Z, WEN H Y. Toughness evaluation of tunnel construction emergency system[J]. Journal of Civil Engineering and Management, 2021, 38(3): 167-172. (in Chinese) doi: 10.3969/j.issn.2095-0985.2021.03.027
    [17]
    黄亚江, 李书全, 项思思. 基于AHP-PSO模糊组合赋权法的地铁火灾安全韧性评估[J]. 灾害学, 2021, 36(3): 15-20+40. doi: 10.3969/j.issn.1000-811X.2021.03.004

    HUANG Y J, LI S Q, XIANG S S. Evaluation of subway fire safety resilience based on AHP-PSO fuzzy combination weighting method[J]. Journal of Catastrophology, 2021, 36 (3): 15-20+40. (in Chinese) doi: 10.3969/j.issn.1000-811X.2021.03.004
    [18]
    BRUNEAU M, CHANG S E, EGUCHI R T, et al. A framework to quantitatively assess and enhance the seismic resilience of communities[J]. Earthquake Spectra, 2003, 19(4): 733-752. doi: 10.1193/1.1623497
    [19]
    SAFFARI E, YILDIRIMOGLU M, HICKMAN M. A methodology for identifying critical links and estimating macroscopic fundamental diagram in large-scale urban networks[J]. Transportation Research Part C: Emerging Technologies, 2020(119): 102743
    [20]
    张玉, 魏华波. 基于CRITIC的多属性决策组合赋权方法[J]. 统计与决策, 2012, (16): 75-77. https://www.cnki.com.cn/Article/CJFDTOTAL-TJJC201216025.htm

    ZHANG Y, WEI H B. Multiple attribute decision combination weighting method based on CRITIC[J]. Statistics & Decision, 2012, (16): 75-77. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJJC201216025.htm
    [21]
    黄晓丽, 刘耀龙, 段锦, 等. 基于灰色关联及模糊综合评价法的道路交通安全风险评价[J]. 数学的实践与认识, 2017, 47(7): 208-215. https://www.cnki.com.cn/Article/CJFDTOTAL-SSJS201707027.htm

    HUANG X L, LIU Y L, DUAN J, et al. The assessment of road traffic safety risk based on grey relation and fuzzy comprehensive evaluation method[J]. Mathematics in Practice and Theory, 2017, 47(7): 208-215. https://www.cnki.com.cn/Article/CJFDTOTAL-SSJS201707027.htm
    [22]
    LI Q. A novel Likert scale based on fuzzy sets theory[J]. Expert Systems with Applications, 2013, 40(5): 1609-1618. doi: 10.1016/j.eswa.2012.09.015
    [23]
    王璨, 冯炜. 城市道路路段通行能力计算方法探讨[J]. 华东公路, 2016, (1): 109-111. https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS201506017.htm

    WANG C, FEN W. Discussion on calculation method of capacity of urban road section[J]. East China Highway, 2016, (1): 109-111. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS201506017.htm
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