An Evaluation and Analysis on the Resilience of the Urban Local Road Network for Recurrent Congestions
-
摘要: 为了缓解常发性拥堵引发的城市噪音、能源消耗和废气排放等现状，使路网具备抵抗短时激增车流的能力，将宏观基本图与性能时序图相结合对局部路网韧性进行量化。针对韧性属性，提出了鲁棒性指数、损失面积比、恢复快速性、流量峰值差和临界密度差5个评价指标，反映路网在性能下降、稳定和恢复阶段的韧性特性。引入Kendall法检验各赋权法的一致性，并基于CRITIC的多属性决策获得最优权重，提出了组合赋权和模糊逻辑相结合的城市局部路网韧性综合评价方法，结合李克特量表法对综合韧性得分进行分级。以长沙市局部路网为例，设计韧性改善方案，针对常发性拥堵路段上的交叉口进行信号配时优化；通过VISSIM仿真并计算得到各方案的韧性指标。研究结果显示：方案8，10和16能有效吸收短时激增车流并与路网状态相适应，所有方案中方案14的韧性得分最高。局部路网综合韧性得分具有随着优化路段数的增加而增长的趋势，但并不是线性递增。信号配时优化改变了路网韧性属性，并降低了部分路段对城市局部路网韧性的负面影响。不同评价方法下的韧性得分排名存在部分差异，流量峰值差与脆弱性指数的评价排名更接近，损失面积比与韧性损失值的评价排名更接近。所提出的指标不局限于单一韧性属性，能更全面、客观地反映干扰下路网的响应过程。Abstract: 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.
-
图 4 信号配时优化前后交通状况对比
Figure 4. Comparison of the traffic conditions before and after signal timing improvement
表 1 城市局部路网韧性评价指标
Table 1. Indexes to evaluate urban local road network resilience
来源 响应指标 指标属性 时序图 鲁棒性指数RI 逆向 恢复快速性RR 正向 损失面积比RLA 逆向 宏观基本图 流量峰值差DPF 正向 临界密度差DCD 正向 
下载: 导出CSV
表 2 韧性等级及取值范围
Table 2. Resilence levels and the value ranges
韧性等级 取值范围 极弱 r≤20 弱 20<r≤40 中等 40<r≤60 较强 60<r≤80
下载: 导出CSV
表 3 基于互联网地图速度数据反推交通流量（样本2021-04-20 T18: 51）
Table 3. Estimated traffic flows based on the speed data from the internet map (Sample 2021-04-20 T18: 51)
路名 行车速度/ (km/h) 饱和度 机动车流/ (pcu/h) 密度/ (pcu/km) 五一大道 10 0.88 2 603 260 湘江中路 15 0.78 2 326 155 黄兴中路 10 0.84 2 389 239 芙蓉中路 15 0.78 2 326 155 解放西路 25 0.60 1 011 40
下载: 导出CSV
表 4 指标权重及级别阈值
Table 4. Weights of indexes and their thresholds of grades
等级 极弱
（20%）弱
（40%）中
（60%）较强
（80%）权重 RI（/km/h） 0.041 0.114 0.616 0.886 0.256 RR（/km/h2） 0.020 0.288 0.366 0.405 0.224 RLA 0.113 0.254 0.512 0.826 0.226 DPF/[pcu/(h·ln)] 0.206 0.271 0.520 0.834 0.138 DCD /[pcu/(km·ln)] 0.209 0.335 0.560 0.631 0.156
下载: 导出CSV
表 5 不同排列组合方案下路网的韧性指标
Table 5. Indexes of the road network resilience with different permutations and combination schemes
编号 优化路段ID RR（/km/h2） RI（/km/h） RLA DPF/ [pcu/(h·ln)] DCD/ [pcu/(km·ln)] 1 未优化 8.650 7.154 0.154 0.000 0.000 2 492 10.184 6.341 0.185 2.626 1.004 3 -509 10.644 7.060 0.213 26.351 -0.455 4 683 6.024 6.546 0.201 -6.657 0.371 5 -568 21.886 2.119 0.085 2.287 -0.948 6 492、-509 8.582 6.861 0.189 26.513 0.435 7 492、683 8.015 6.386 0.159 1.536 0.538 8 492、-568 < 0.001 < 0.001 < 0.001 8.180 -0.084 9 -568、-509 6.313 2.744 0.104 11.789 -0.281 10 -568、683 < 0.001 < 0.001 < 0.001 4.121 0.561 11 -509、683 6.750 5.507 0.177 14.032 -0.158 12 683、-568、-509 0.443 0.816 0.037 33.112 -0.165 13 492、-509、683 7.573 6.878 0.198 30.041 1.407 14 492、-568、683 6.064 1.098 0.076 26.513 0.435 15 492、-509、-568 8.864 3.309 0.113 19.949 -0.514 16 492、-568、683、-509 < 0.001 < 0.001 < 0.001 -1.397 -0.627
下载: 导出CSV
表 6 路网综合韧性评分与等级划分
Table 6. Comprehensive resilience scores and classifications of the road network resilience
编号 优化路段ID b1 b2 b3 b4 综合韧性 等级 1 未优化 0.394 0.315 0.124 0.168 51 中韧性 2 492 0.276 0.344 0.000 0.380 60 中韧性 3 -509 0.638 0.000 0.002 0.360 52 中韧性 4 683 0.476 0.368 0.156 0.000 44 中韧性 5 -568 0.254 0.040 0.335 0.371 66 较强韧性 6 492，-509 0.482 0.000 0.171 0.347 58 中韧性 7 492，683 0.160 0.460 0.224 0.156 58 中韧性 8 492，-568 0.224 0.215 0.078 0.482 66 较强韧性 9 -568，-509 0.065 0.347 0.589 0.000 60 较强韧性 10 -568，683 0.224 0.138 0.000 0.638 71 较强韧性 11 -509，683 0.136 0.610 0.255 0.000 52 中韧性 12 683，-568，-509 0.228 0.152 0.000 0.620 70 较强韧性 13 492，-509，683 0.482 0.058 0.166 0.294 55 中韧性 14 492，-568，683 0.010 0.215 0.265 0.511 76 较强韧性 15 492，-509，-568 0.156 0.077 0.477 0.290 68 较强韧性 16 492，-568，683，-509 0.518 0.000 0.000 0.482 59 中韧性
下载: 导出CSV
-
[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.1006-3862.2016.06.016LI 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.1674-4861.2020.06.011GU 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.htmLYU 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.2095-0985.2021.03.027ZHAO 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.2021.03.004HUANG 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.htmZHANG 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.htmHUANG 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.htmWANG 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 -
点击查看大图
图(4) / 表(6)
计量
- 文章访问数: 1160
- HTML全文浏览量: 431
- PDF下载量: 54
- 被引次数: 0