基于数据预测的区域道路状态模糊判别

区域路网交通状态判别是实施区域交通管理控制和交通诱导的基础。为有效且有前瞻性地描述区域路网拥挤状况，提出了1种基于时间序列数据预测和主成分分析相结合的模糊综合定量评价方法。以路段平均速度和交通流量为描述交通拥挤状况的参数，利用时间序列预测模型对数据进行预测；将路网中各路段的平均旅行时间作为总延误的影响因素；再利用主成分分析法确定各个路段对区域拥挤的影响权重；最后运用模糊综合评价法对区域路网拥挤状况进行评估。以山西省临汾市实际路网为例，通过 Vissim 交通仿真软件和 SPSS 数据统计分析软件对算法进行了仿真验证。仿真结果表明，该算法能够有效地预判城市区域的交通状况，为交通管理、控制和诱导提供准确的依据。

Assessment of Regional Traffic Road Condition Based on Time Series Prediction

Assessment of regional traffic road condition is the basis of traffic management and travel route guidance . In order to evaluate the regional traffic road condition effectively ,this paper proposes a fuzzy comprehensive quantitative method and its algorithm .This method is the combination of time series prediction and principal component analysis . First ,a time series prediction model is applied to predicting the average speed and traffic volume .These predicted speeds and volumes are used as model parameters to describe the traffic congestion condition .Then ,the principal component a-nalysis is used to determine the contribution weight of each roadway section to the congestion of the network .Finally ,the fuzzy comprehensive evaluation quantitative method is used to evaluate the congestion state of regional road network .To verify the proposed method ,the method was used to analyze the roadway network of Linfen City in Shanxi Province by u -sing Vissim and SPSS .Simulation results show that the algorithm can effectively predict the traffic conditions in urban ar -eas and provide an accurate basis for traffic management and travel route guidance .