考虑时空关联度的车辆出行目的地预测——以深圳市福田中心区为例  

Vehicle Travel Destination Prediction Considering SpatiotemporalCorrelation Degree: A Case of Futian Central District in Shenzhen

作　　者：唐易 刘诗昆 丘建栋 TANG Yi;LIU Shi-kun;QIU Jian-dong(Shenzhen Urban Transport Planning Center Co.,Ltd,Shenzhen 518057,China;School of Intelligent Systems Engineering,Sun Yat-sen University,Shenzhen 518107,China;Guangdong Provincial Key Laboratory of Intelligent Transportation System,Shenzhen 518107,China)

机构地区：[1]深圳市城市交通规划设计研究中心股份有限公司,深圳518057 [2]中山大学智能工程学院,深圳518107 [3]广东省智能交通系统重点实验室,深圳518107

出　　处：《科学技术与工程》2025年第11期4761-4768,共8页Science Technology and Engineering

基　　金：广东省城市交通数字孪生企业重点实验室项目(2022B1212020005)。

摘　　要：为满足新时期愈加精细化的个体级交通管理与出行服务需求,在传统基于历史轨迹的出行目的地预测方法基础上,提出一种综合考虑时空关联度的车辆出行目的地预测方法。基于视频AI识别与车辆卫星定位等数据,识别车辆停留点,以此切分车辆全天出行轨迹,建立历史车辆出行轨迹库;研究车辆出行时空特征,提出车辆出行轨迹时间关联度与空间关联度的计算方法,以时空关联度为权重构建车辆出行目的地预测模型;以深圳市福田中心区的车辆出行为例,选取包含私家车、出租汽车等4个典型特征的车辆出行轨迹,建立模型预测准确度评价函数,分析不同类型出行、不同轨迹完成度的出行目的地预测准确度,并与基于历史轨迹预测方法进行对比。结果表明:不同类型车辆出行目的地预测准确度与轨迹完成度基本呈正相关,当轨迹完成度达到80%时,出行预测准确度基本达到80%以上;相比传统基于历史轨迹的预测方法,考虑时空关联度的预测方法预测准确度更高,特别是针对无固定通勤出行特征的出租车,出行目的地预测准确度提高了16%以上,研究成果能够更好适应全局的交通管理需要。To meet the increasingly refined individual-level traffic management and travel service needs in the new era,a vehicle travel destination prediction method that comprehensively considers temporal and spatial correlation was proposed based on the traditional prediction method based on historical trajectories.Using data from video AI recognition and vehicle satellite positioning,the vehicle stopping points were identified to segment the vehicle's full-day travel trajectories and establish a historical vehicle travel trajectory database.By studying the temporal and spatial characteristics of vehicle travel,a calculation method for the temporal and spatial correlation of vehicle travel trajectories was proposed,and a vehicle travel destination prediction model was constructed using temporal and spatial correlation as weights.Taking the vehicle travel in Futian Central District of Shenzhen as an example,four typical vehicle travel trajectories including private cars and taxis were selected to establish a model prediction accuracy evaluation function.The prediction accuracy of travel destinations for different types of travel and different degrees of trajectory completion was analyzed and compared with the historical trajectory-based prediction method.The results show that the prediction accuracy of travel destinations for different types of vehicles is basically positively correlated with the degree of trajectory completion.When the trajectory completion rate reaches 80%,the accuracy of travel prediction basically reaches over 80%.Compared with the traditional prediction method based on historical trajectories,the prediction method considering temporal and spatial correlation has higher prediction accuracy,especially for taxis services with no fixed commuting travel characteristics.The prediction accuracy of travel destinations has been improved by more than 16%.The research results can better meet the needs of global traffic management.

关 键 词：出行目的地预测 时空关联度 出行轨迹 轨迹完成度 视频AI识别数据 

分 类 号：U491[交通运输工程—交通运输规划与管理]