Resilience Assessment of Urban Road Network Based on Day-to-Day Traffic Assignment
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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.
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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 
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