Resilience Assessment of Urban Road Network Based on Day-to-Day Traffic Assignment
-
摘要: 为有效评价重大扰动事件下的路网性能，提出以日变交通配流（day-to-day traffic assignment，DTA）为基础的城市道路网络韧性评估模型. 明确考虑重大扰动事件下交通流动态变化特性，构建了综合考虑出行者认知更新、行为惯性等因素影响的DTA模型，设计了启发式求解算法；定义了基于DTA的路网可达性指标，构建了可全面评价扰动事件生命周期内系统性能的韧性指标与评估模型，并在Nguyen & Dupuis网络上进行算例研究. 结果表明：在扰动事件后的前10天，路网韧性波动变化，此后随着交通流分布趋于稳定，路网韧性单调上升，从第10天的0.323上升到第50天的0.794，上升了145.77%；与传统随机用户均衡（ stochastic user equilibrium，SUE）模型相比，DTA模型获得的路网可达性与韧性指标存在显著差异，SUE模型下路网可达性随时间单调上升，而DTA模型下路网可达性在前15天剧烈波动，随后才单调增加，表明要获得准确的路网韧性指标，必须首先准确假定出行决策行为和相应配流模型；出行者行为惯性、路段通行能力退化程度与恢复速率以及路网拥挤程度等因素均对交通流量分布产生显著影响，进而影响路网可达性最终导致路网韧性指标发生显著变化，表明实际应用中应在充分调查的基础上合理标定相关参数.Abstract: In order to effectively evaluate road network performance under major disruptive events, on the basis of a day-to-day traffic assignment (DTA) model, an urban road network resilience assessment model is proposed. With explicit consideration on the dynamic characteristics of traffic flow under a major disruptive event, a DTA model that comprehensively cover the influencing factors including travelers’ cognitive update and behavioral inertia is constructed, and then a heuristic solution algorithm is designed. Based on the DTA model, a road network accessibility index is defined, and a resilience metric as well as an evaluation model are established, which can fully measure the system performance during the disruptive event life cycle. Finally a case study is performed on the Nguyen and Dupuis network. The results show that, after the disruptive event, the road network resilience fluctuates in the first 10 days; then as the traffic flow distribution tends to be stable, it increases monotonically from 0.323 on the 10th day to 0.794 on the 50th day, an increase by 145.77%. Compared to classical stochastic user equilibrium (SUE) model, there are significant differences in both road network accessibility and resilience indicators obtained from DTA model. The road network accessibility index under SUE model monotonically increases with time, while that index under DTA model fluctuates sharply in the first 15 days, after then increasing monotonically. It indicates that, in order to acquire accurate road network resilience metric, travel decision behaviors and corresponding traffic assignment model must be accurately assumed in the first place. All factors including travelers’ behavior inertia, the degradation degree and recovery rate of link capacity, and road network congestion degree have a significant impact on the distribution of traffic flow, which in turn affect road network accessibility index and ultimately result in obvious changes in road network resilience metric. As a result, relevant parameters should be reasonably calibrated under full investigation over practical applications.
-
图 4 随时间变化的路径流量、期望理解出行时间及实际出行时间
Figure 4. Time-varying route flow，mean perceived travel time，and actual travel time
图 6 使用不同配流模型获得的路网可达性与韧性
Figure 6. Road network accessibility and resilience using different traffic assignment models
表 1 路段路径关联关系
Table 1. Link-route incidence relationship
OD对 路径编号 路段序列 （1，2） 1 2→18→11 2 2→17→7→9→11 3 2→17→7→10→15 4 2→17→8→14→15 5 1→5→7→9→11 6 1→5→7→10→15 7 1→5→8→14→15 8 1→6→12→14→15 （1，3） 9 2→17→7→10→16 10 2→17→8→14→16 11 1→5→7→10→16 12 1→5→8→14→16 13 1→6→13→19 14 1→6→12→14→16 （4，2） 15 3→5→7→9→11 16 3→5→7→10→15 17 3→5→8→14→15 18 3→6→12→14→15 19 4→12→14→15 （4，3） 20 3→5→7→10→16 21 3→5→8→14→16 22 3→6→12→14→16 23 4→12→14→16 24 4→13→19 25 3→6→13→19 
下载: 导出CSV
-
FATURECHI R, MILLER-HOOKS E. Measuring the performance of transportation infrastructure systems in disasters:a comprehensive review[J]. Journal of Infrastructure Systems, 2015, 21(1): 1-15. ZHOU Y M, WANG J W, YANG H. Resilience of transportation systems:concepts and comprehensive review[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(12): 4262-4276. PATRIARCA R, BERGSTRÖM J, GRAVIO G D, et al. Resilience engineering:current status of the research and future challenges[J]. Safety Science, 2018, 102(2): 79-100. MURRAY-TUITE P M. A comparison of transportation network resilience under simulated system optimum and user equilibrium conditions[C]// Proceedings of the 2006 Winter Simulation Conference.Monterey: IEEE, 2006: 1398-1405. TWUMASI-BOAKYE R, SOBANJO J. Resilience of regional transportation networks subjected to hazard-induced bridge damages[J]. Journal of Transportation Engineering, 2018, 144（10）: 04018062.1-04018062.13 HENRY D, RAMIREZ-MARQUEZ J E. Generic metrics and quantitative approaches for system resilience as a function of time[J]. Reliability Engineering and System Safety, 2012, 99(3): 114-122. BAROUD H, BARKER K, RAMIREZ-MARQUEZ J E, et al. Importance measures for inland waterway network resilience[J]. Transportation Research Part E:Logistics and Transportation Review, 2014, 62: 55-67. doi: 10.1016/j.tre.2013.11.010 BAROUD H, RAMIREZ-MARQUEZ J E, BARKER K, et al. Stochastic measures of network resilience:applications to waterway commodity flows[J]. Risk Analysis, 2014, 34(7): 1317-1335. doi: 10.1111/risa.12175 FARHADI N, PARR S A, MITCHELL K N, et al. Use of nationwide automatic identification system data to quantify resiliency of marine transportation systems[J]. Transportation Research Record, 2016, 2549(1): 9-18. doi: 10.3141/2549-02 NOGAL M, O’CONNOR A, CAULFIELD B, et al. Resilience of traffic networks:from perturbation to recovery via a dynamic restricted equilibrium model[J]. Reliability Engineering and System Safety, 2016, 156(1): 84-96. NOGAL M, HONFI D. Assessment of road traffic resilience assuming stochastic user behavior[J]. Reliability Engineering and System Safety, 2019, 185: 72-83. doi: 10.1016/j.ress.2018.12.013 严新平,熊伟. 非常态事件下城市交通的解决方案研究[J]. 交通运输系统工程与信息,2008,8(6): 78-84.YAN Xinping, XIONG Wei. Urban traffic schemes under the influence of the emergency accidents[J]. Journal of Transportation Systems Engineering and Information Technology, 2008, 8(6): 78-84. HE Xiaozheng, LIU H X. Modeling the day-to-day traffic evolution process after an unexpected network disruption[J]. Transportation Research Part B:Methodological, 2012, 46(1): 50-71. doi: 10.1016/j.trb.2011.07.012 张玺,刘海旭,蒲云. 交通事件影响下的路径行程时间变化分析[J]. 西南交通大学学报,2013,48(5): 928-933.ZHANG Xi, LIU Haixu, PU Yun. Analysis of route travel time variation of road network suffering traffic incidents[J]. Journal of Southwest Jiaotong University, 2013, 48(5): 928-933. ZHANG X G, MAHADEVAN S, SANKARARAMAN S, et al. Resilience-based network design under uncertainty[J]. Reliability Engineering and System Safety, 2018, 169(1): 364-379. JHA M, MADANAT S, PEETA S. Perception updating and day-to-day travel choice dynamics in traffic networks with information provision[J]. Transportation Research Part C: Emerging Technologies, 1998, 6(3): 189-212. doi: 10.1016/S0968-090X(98)00015-1 刘天亮,黄海军,陈剑. 考虑风险规避和认知更新的日常择路行为演进[J]. 交通运输工程学报,2008,8(4): 90-94,103.LIU Tianliang, HUANG Haijun, CHEN Jian. Evolution of day-to-day route choice behavior considering risk aversion and perception updating[J]. Journal of Traffic and Transportation Engineering, 2008, 8(4): 90-94,103. 黄海军. 城市交通网络平衡分析——理论与实践[M]. 北京: 人民交通出版社, 1994. 张玺,郭洪洋,刘海旭,等. 基于认知更新的随机动态分配模型[J]. 交通运输系统工程与信息,2013,13(1): 118-123,178. doi: 10.1016/S1570-6672(13)60095-1ZHANG Xi, GUO Hongyang, LIU Haixu, et al. Perception updating based stochastic dynamic assignment model[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(1): 118-123,178. doi: 10.1016/S1570-6672(13)60095-1 张玺. 基于网络效率的日变路网脆弱性识别方法[J]. 交通运输系统工程与信息,2017,17(2): 176-182.ZHANG Xi. Day-to-day road network vulnerability identification based on network efficiency[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(2): 176-182. LUATHEP P, SUMALEE A, HO H, et al. Large-scale road network vulnerability analysis:a sensitivity analysis based approach[J]. Transportation, 2011, 38(5): 799-817. doi: 10.1007/s11116-011-9350-0 吕彪,刘一骝,刘海旭. 协同考虑脆弱性与可靠性的城市道路网络设计[J]. 西南交通大学学报,2019,54(5): 1093-1103.LÜ Biao, LIU Yiliu, LIU Haixu. Urban road network design with balance between vulnerability and reliability[J]. Journal of Southwest Jiaotong University, 2019, 54(5): 1093-1103. NGUYEN S, DUPUIS C. An efficient method for computing traffic equilibria in networks with asymmetric transportation costs[J]. Transportation Science, 1984, 18(2): 185-202. doi: 10.1287/trsc.18.2.185 -
下载:
点击查看大图
图(7) / 表(1)
计量
- 文章访问数: 911
- HTML全文浏览量: 424
- PDF下载量: 83
- 被引次数: 0