Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads.But how to alleviate sparsity and further enhance accuracy is still challenging.Addressing at these issues...Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads.But how to alleviate sparsity and further enhance accuracy is still challenging.Addressing at these issues,we propose to fuse spatio-temporal contexts into deep factorization machine(STC_DeepFM)offline for pick-up area recommendation,and within the area to recommend pick-up points online using factorization machine(FM).Firstly,we divide the urban area into several grids with equal size.Spatio-temporal contexts are destilled from pick-up points or points-of-interest(POIs)belonged to the preceding grids.Secondly,the contexts are integrated into deep factorization machine(DeepFM)to mine high-order interaction relationships from grids.And a novel algorithm named STC_DeepFM is presented for offline pick-up area recommendation.Thirdly,we devise the architecture of offline-to-online(O2O)recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and efficiency.Some experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts,different recommendation models,and the O2O architecture.The results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods,and the O2O architecture achieves excellent real-time performance.展开更多
针对以往的工作很少考虑兴趣点(point of interest,POI)和道路属性之间联系导致获取效果难以满足需求的问题,本文提出一种基于车辆轨迹数据和POI特征的多道路属性识别方法。首先对车辆轨迹进行地图匹配,通过挖掘匹配后的轨迹数据获取道...针对以往的工作很少考虑兴趣点(point of interest,POI)和道路属性之间联系导致获取效果难以满足需求的问题,本文提出一种基于车辆轨迹数据和POI特征的多道路属性识别方法。首先对车辆轨迹进行地图匹配,通过挖掘匹配后的轨迹数据获取道路特征。通过设置道路缓冲区,获取缓冲区内的POI信息,然后采用关联规则挖掘算法计算相关POI加权支持度和道路重要性指标作为道路上下文指标。最后对道路特征和道路上下文指标进行特征向量构建,引入DeepFM(factorization machine)深度学习模型提出基于车辆轨迹和POI特征的多道路属性识别方法(TrajPOI-MAR)识别道路限速和单双向属性。使用滴滴出租车成都GPS数据集在进行实验并与决策树和多任务学习框架等算法比较,实验证明本文提出模型在方法在性能上得到一定程度提升,限速识别和单双向识别的F1值分别为0.8928和0.9458,均优于基线算法。展开更多
基金supported by the National Natural Science Foundation of China(41871320,61873316)the Key Project of Hunan Provincial Education Department(19A172)+1 种基金the Scientific Research Fund of Hunan Provincial Education Department(18K060)the Postgraduate Scientific Research Innovation Project of Hunan Province(CX20211000).
文摘Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads.But how to alleviate sparsity and further enhance accuracy is still challenging.Addressing at these issues,we propose to fuse spatio-temporal contexts into deep factorization machine(STC_DeepFM)offline for pick-up area recommendation,and within the area to recommend pick-up points online using factorization machine(FM).Firstly,we divide the urban area into several grids with equal size.Spatio-temporal contexts are destilled from pick-up points or points-of-interest(POIs)belonged to the preceding grids.Secondly,the contexts are integrated into deep factorization machine(DeepFM)to mine high-order interaction relationships from grids.And a novel algorithm named STC_DeepFM is presented for offline pick-up area recommendation.Thirdly,we devise the architecture of offline-to-online(O2O)recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and efficiency.Some experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts,different recommendation models,and the O2O architecture.The results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods,and the O2O architecture achieves excellent real-time performance.
文摘针对以往的工作很少考虑兴趣点(point of interest,POI)和道路属性之间联系导致获取效果难以满足需求的问题,本文提出一种基于车辆轨迹数据和POI特征的多道路属性识别方法。首先对车辆轨迹进行地图匹配,通过挖掘匹配后的轨迹数据获取道路特征。通过设置道路缓冲区,获取缓冲区内的POI信息,然后采用关联规则挖掘算法计算相关POI加权支持度和道路重要性指标作为道路上下文指标。最后对道路特征和道路上下文指标进行特征向量构建,引入DeepFM(factorization machine)深度学习模型提出基于车辆轨迹和POI特征的多道路属性识别方法(TrajPOI-MAR)识别道路限速和单双向属性。使用滴滴出租车成都GPS数据集在进行实验并与决策树和多任务学习框架等算法比较,实验证明本文提出模型在方法在性能上得到一定程度提升,限速识别和单双向识别的F1值分别为0.8928和0.9458,均优于基线算法。
