考虑边界条件不确定性的城市交通拥塞风险分布迁移规律研究

为分析交通联通边界(交通量、交通扩散系数)对特定城市研究区域内交通拥塞迁移规律的影响机理。本文从新的角度考虑城市交通拥塞风险分布迁移规律，将污染质迁移数值模拟模型运用到城市道路交通拥塞风险迁移中。首先对贵阳某区域2020年6~7月的城市交通调查数据及爬取的部分高德地图数据进行处理，并将研究区域以交通联通边界、交通隔断边界、区域分区边界划分为区域1和区域2。在此基础上考虑交通联通边界并建立基于城市交通拥塞风险分布及迁移数值替代模型，并运用Monte Carlo方法对贵阳某研究区域的输入输出结果进行统计分析，并将实际路网数据值输入替代模型，将结果与路网真实情况和高德地图情况进行对比，所建立模型 的预测精度比高德地图高3.7%，且模拟交通拥塞风险迁移规律较高。结果表明，交通联通边界对城市交通拥塞迁移数值模拟模型预报有很大影响，统计分析研究区域模拟模型的结果，可以有效评估不同交通联通边界条件对研究区域交通拥塞风险分布情况和准确预报研究区域内遭受不同 交通拥塞程度的风险。综上可知，本文建立的模型能高效精准分析城市交通拥塞的风险分布迁移规律，可为城市路网分区分段分点治理交通拥塞提供新的参考。

Migration Law of Urban Traffic Congestion Risk Distribution Considering Uncertainty of Boundary Conditions

In order to analyze the influence mechanism of the traffic connection boundary (traffic volume, traffic diffusion coefficient) on the traffic congestion migration law in the study area of a specific city, this paper considers the distribution and migration law of urban traffic congestion risk from a new perspective that it applies the numerical simulation model of pollutant migration to urban road traffic congestion risk migration. First, the urban traffic survey data in a certain area of Guiyang City from June to July 2020 and crawled AutoNavi map data are processed. The study area is divided into Area 1 and Area 2. On this basis, considering the traffic connection boundary and establishing a numerical surrogate model based on urban traffic congestion risk distribution and migration, the Monte Carlo method is used to statistically analyze the input and output results of a study area in Guiyang. The actual road network data values are taken as input into the surrogate model. By comparing the output results with the practical results of the road network and the AutoNavi map, the established model is 3.7% more accurate than the AutoNavi map, and the migration law of simulated traffic congestion risk is higher. The results show that the traffic connection boundary has a great influence on the prediction of the numerical model of urban traffic congestion migration. Statistical analysis of the simulation results in the study area can effectively evaluate the impact of different traffic connection boundary conditions on the risk distribution of traffic congestion and accurately predict the risk of varying levels of traffic congestion within the country. The model established in this paper can efficiently and accurately analyze the riskdistribution and migration law of urban traffic congestion, and it can provide a new reference for urban road network subdivision and sub-point control of traffic congestion.