基于气象-交通多通道数据融合的短时交通流速度预测模型  

Short-term traffic flow velocity prediction model based on multi-channel data fusion of meteorological and traffic

作　　者：马飞[1] 杨治杰 王江博 孙启鹏[1] 鲍博 郭庆元 黄凯 MA Fei;YANG Zhi-jie;WANG Jiang-bo;SUN Qi-peng;BAO Bo;GUO Qing-yuan;HUANG Kai(School of Economics and Management,Chang'an University,Xi'an 710064,Shaanxi,China;Xi'an Meteorology,Xi'an 710016,Shaanxi,China;Xi'an Traffic Information Center,Xi'an 710018,Shaanxi,China)

机构地区：[1]长安大学经济与管理学院,陕西西安710064 [2]西安市气象局,陕西西安710016 [3]西安市交通信息中心,陕西西安710018

出　　处：《交通运输工程学报》2024年第6期183-196,共14页Journal of Traffic and Transportation Engineering

基　　金：国家自然科学基金项目(72104034,72104037);陕西省自然科学基础研究计划资助项目(2022JM-426,2023-JC-QN-0793);陕西省交通运输厅科技项目(23-12K);陕西省教育厅重点科学研究计划项目(21JP007);西安市科技计划项目(24SFSF0009);教育部人文社会科学研究(23YJCZH179);中央高校基本科研业务费专项资金项目(300102234613);陕西省社会科学基金项目(2024R009)。

摘　　要：为提升气象-交通多因素影响下短时交通流预测精度,综合考虑气温、湿度和交通拥堵指数等气象-交通特征数据的融合集成,提出了一种基于格拉姆角场-卷积神经网络-长短期记忆(GAF-CNN-LSTM)的短时交通流速度预测模型;利用格拉姆角场将气温、湿度、历史交通流速度的时间序列数据转换为图像数据,利用RGB多通道颜色编码形成气象-交通特征图像,通过RGB多通道中颜色叠加变化反映气象-交通多因素特征数据融合;将转换形成的特征图像输入卷积神经网络提取气象-交通因素融合特征;通过长短期记忆神经网络提取气象-交通各因素的时序信息,构建短时交通流速度预测模型;选取西安市未央区的气温、湿度等气象数据和历史交通流速度数据,根据气温、湿度的变化趋势,设置湿度极小值与气温极小值、湿度极大值2个极值情景和气温、湿度非极值2个常规情景进行模型验证。分析结果表明:GAF-CNN-LSTM模型能够考虑气象-交通多因素特征数据融合,与移动平均模型、自回归移动平均模型、先知模型、随机森林模型、长短期记忆神经网络模型相比,均方误差、均方根误差和平均绝对百分比误差分别平均降低0.0446、0.1424和12.1%,决定系数平均提升了31.05%,预测精度最高,研究结果可为城市交通治理提供更加精准的决策依据。In order to improve the prediction accuracy of short-term traffic flow under the influence of meteorological and traffic factors,a short-time traffic flow velocity prediction model based on Gramian angular field-convolutional neural network-long short-term memory(GAF-CNN-LSTM)was proposed by considering the fusion of meteorological and traffic feature data such as temperature,humidity,and traffic congestion index.By utilizing the Gramian angular field,time series data of temperature,humidity,and historical traffic flow velocity were transformed into image data.RGB multi-channel color coding was employed to generate meteorological and traffic feature images,and the color superposition changes in the RGB muti-channel reflected the meteorological and traffic multi-factor feature data fusion.The transformed feature images were input into a convolutional neural network to extract fusion features of meteorological and traffic factors.An long short-term memory neural network was employed to capture the time series information of meteorological and traffic factors,thereby a short-term traffic flow velocity prediction model was constructed.Meteorological data,including temperature,humidity,and historical traffic flow velocity data from the Weiyang District in Xi'an were selected.According to the changing trend of temperature and humidity,two extreme scenarios of minimum humidity and minimum temperature with maximum humidity,and two conventional scenarios of non-extreme temperature and humidity were set to verify the model.Analysis results indicate that the GAF-CNN-LSTM model can consider the fusion of meteorological and traffic multi-factor feature data.Compared with moving average model,autoregressive integrated moving average model,prophet model,random forest model,and LSTM neural network model,the mean square error,root mean square error,and mean absolute percentage error of GAF-CNN-LSTM model reduce by 0.0446,0.1424,and 12.1%,respectively,while the determination coefficient shows an average improvement of 31.05%.The G

关 键 词：智能交通系统 短时交通流速度预测 格拉姆角场 多通道 神经网络 数据融合 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]