Determination of the Influence Range of Urban Traffic Congestion and Identification of Key Road Sections
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摘要:
为研究非点源交通拥塞的影响范围及关键路段识别，引入风险场强理论、区域生长法对烟羽模型进行适应性改进，以确定最大、核心影响范围及识别关键路段. 首先，对非点源交通拥塞的“源-路径-汇”进行界定，以2021年贵阳市的交通调查数据、交通状态数据等为基础，构建非点源交通拥塞的“源”风险场强模型；考虑不同土地利用性质的交通影响力及不同道路等级调整系数并进行标定，建立基于不同“源”风险的最大影响范围识别模型，引入区域生长法建立路段核心区影响范围确定模型；最终，以路段交通量、长度、时间构建交通拥塞路段损伤性评价指数并对交通拥塞程度进行定量化研究，对原有模型、改进模型及实际情况进行对比分析. 结果表明：不同土地利用性质和不同道路等级对路段交通拥塞影响程度不同；改进后的不同“源”风险影响范围识别模型有效性较强，且预测精度提高3.54%，更接近实际情况；考虑区域生长法的路段交通拥塞核心区影响范围准确性更高，与实际情况仅相差2.66%. 综上所述，本研究为城市非点源交通拥塞累积性风险区范围划定和风险关键路径确定提供一种新的思路，对进一步开展不同交通拥塞发生类型的治理提供理论依据.
Abstract:In order to study the influence range of non-point source traffic congestion and the identification of key road sections, the risk field strength theory and the regional growth method were introduced to adapt the plume model, and the maximum and core influence areas were determined and key road sections were identified. First, this paper defines the "source-path-sink" of non-point source traffic congestion, and constructs the "source" risk field strength of non-point source traffic congestion based on the traffic survey data and traffic status data in Guiyang in 2021. The model, considering the traffic influence of different land use properties and the adjustment coefficient of different road grades and calibration, establishes the identification model of the maximum influence range based on different "source" risks, and introduces the regional growth method to establish a model for determining the influence range of the core area of the road section; finally, Based on the traffic volume, length and time of the road section, the damage evaluation index of the traffic congestion road section is constructed, and the quantitative research on the degree of traffic congestion is carried out, and the original model, the improved model and the actual situation are compared and analyzed. The results show that: 1) Different land use properties and different road grades have different influences on traffic congestion in road sections; 2) The improved identification model of different "source" risk influence areas is more effective, and the prediction accuracy is increased by 3.54%, which is closer to reality 3) Considering the regional growth method, the accuracy of the influence range of the core area of the road section traffic congestion is higher, which is 2.66% different from the actual situation. In summary, this study provides a new idea for the delineation of the cumulative risk area of urban non-point source traffic congestion and the determination of risk critical paths, and provides a theoretical basis for further governance of different types of traffic congestion.
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Key words:
- traffic engineering /
- regional growth method /
- risk assessment /
- traffic congestion /
- damage
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图 3 不同土地利用性质的“源—路径—汇”风险示意
Figure 3. “Source-path-sink” risk indication of different land use properties
图 4 交通拥塞“源”风险核心范围确定方法
Figure 4. Method for determining the core scope of traffic congestion “source” risk
图 5 原有模型预测、改进模型预测及实际情况对比分析
Figure 5. Original model prediction, improved model prediction and comparative analysis of actual situation
图 6 原有模型预测、改进模型预测及实际情况路段拥塞对比分析
Figure 6. Contrast analysis of original model prediction, improved model prediction and actual road congestion
表 1 研究区域内高峰小时道路基本情况
Table 1. Basic conditions of roads during peak hours in the study area
编号 道路名称 道路等级 车道数 交通量/
（pcu·h−1）通行能力/
（pcu·h−1）饱和度（v/c） 平均行车速度/
（km·h−1）1 延安中路 主干路 双向8车道 3208 3718 0.863 29.87 2 延安东路 主干路 双向8车道 3250 3718 0.874 29.35 3 宝山北路 城市快速路 双向8车道 + 潮汐车道 3995 4837 0.826 41.23 4 宝山南路 城市快速路 双向8车道 + 潮汐车道 4019 4837 0.831 40.85 5 市南路 城市快速路 双向8车道 3902 4375 0.892 42.31 6 瑞金中路 主干路 双向6车道 2670 2990 0.893 28.06 7 瑞金南路 主干路 双向6车道 2727 2990 0.912 27.86 8 人民大道 主干路 双向6车道 2637 2990 0.882 28.15 9 中山西路 主干路 双向6车道 2450 2990 0.836 29.86 10 中山东路 主干路 双向6车道 2448 2990 0.819 30.34 11 中华中路 主干路 双向8车道 3093 3718 0.832 28.36 12 中华南路 主干路 双向8车道 3142 3718 0.845 29.12 13 富水北路 支路 单向4车道 1547 1673 0.925 25.36 14 富水中路 主路 单向3车道 1218 1345 0.906 23.98 15 遵义路 次干路 双向6车道 1708 2055 0.831 29.26 16 新华路 次干路 双向6车道 1662 2055 0.809 28.75 17 西湖路 次干路 双向4车道 1200 1455 0.825 27.56 18 博爱路 支路 双向2车道 452 504 0.897 20.28 19 文化路 支路 双向2车道 448 504 0.889 19.32 20 观水路 次干路 双向4车道 1312 1455 0.902 28.14 21 南明南路 支路 双向2车道 431 504 0.856 18.97 22 都司高架桥路 主干路 双向6车道 2676 2990 0.895 29.43 
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表 2 交通拥塞风险路段损伤性指数划分
Table 2. Damage index division of road sections with traffic congestion risk
名称 指标描述 关系表述 权重 分值 风险等级 代表色 交通拥塞风险路段损伤性指数$ V_{w,x,y,{\textit{z}}} $ 实际交通量$ q_{s} $和自由流情况下交通量$ q_f $的关系 qs≤0.3qf 1/3 20 很低 ● 绿色0.3qf<qs≤0.6qf 40 较低 ● 蓝色0.6qf<qs≤0.7qf 60 中等 ● 黄色0.7qf<qs≤0.8qf 80 较高 ● 橙色0.8qf<qs≤1.0qf 100 很高 ● 红色道路拥塞的长度lf和道路的实际长度ls关系 lf≤0.04ls 1/3 20 很低 ● 绿色0.04ls<lf≤0.08ls 40 较低 ● 蓝色0.08ls<lf≤0.12ls 60 中等 ● 黄色0.12ls<lf≤0.15ls 80 较高 ● 橙色0.15ls<lf 100 很高 ● 红色实际出行时间ts和自由流情况下出行时间tf的关系 ts≤0.3tf 1/3 20 很低 ● 绿色0.3tf<ts≤0.5tf 40 较低 ● 蓝色0.5tf<ts≤0.6tf 60 中等 ● 黄色0.6tf<ts≤0.7tf 80 较高 ● 橙色0.7tf<ts≤1.0tf 100 很高 ● 红色交通拥塞综合评价指数C 0≤C<2.0 20 很低 ● 绿色2.0≤C<4.0 40 较低 ● 蓝色4.0≤C<6.0 60 中等 ● 黄色6.0≤C<8.0 80 较高 ● 橙色C≥8.0 100 很高 ● 红色
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表 3 模型参数标定
Table 3. Parameter Calibration and Maximum Influence Scope of Different Land Use Properties
不同土地利用类型描述 城市道路
类型交通量扩散系数$ \zeta $ 不同道路等级的调整系数$ \eta $ 不同土地利用性质的交通影响力$ I_{d} $ 实际情况最大影响范围/m 原有模型预测最大影响范围/m 改进模型1的最大影响范围/m 改进模型2的最大影响范围/m 改进模型1精度提升/% 改进模型2精度提升/% 医院 城市快速路 0.95 1.00 1.50×10−8 958 1002 979 965 2.40 3.86 主干路 0.95 1.35×10−8 886 932 865 872 2.82 3.61 次干路 0.75 1.08×10−8 691 756 719 709 5.35 6.80 商业区
（中心）城市快速路 1.00 1.00 1.82×10−8 1045 1098 1072 1062 2.49 3.44 主干路 0.95 1.43×10−8 912 972 931 925 4.50 5.15 次干路 0.75 1.15×10−8 712 782 685 701 6.04 8.29 学校 城市快速路 0.90 1.00 1.40×10−8 784 812 767 772 1.40 2.04 主干路 0.95 1.60×10−8 857 898 845 862 3.38 4.20 次干路 0.75 1.03×10−8 583 619 599 592 3.43 4.63
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