基于GPS轨迹数据的拥堵路段预测
发布时间：2019-05-10 06:26
【摘要】：基于真实的GPS轨迹数据,对城市拥堵路段进行预测.在此过程中,摒弃传统的基于交通流预测和拥堵识别的方法,提出一种新的基于拥堵向量和拥堵转移矩阵的拥堵路段预测方法.该方法同时考虑路段拥堵的时间周期性和时空相关性,通过对出租车GPS轨迹数据进行挖掘和训练,建立拥堵向量和拥堵转移矩阵,实现对拥堵路段的预测.真实数据集上的实验验证了所提的拥堵路段预测方法的有效性.
[Abstract]:Based on the real GPS trajectory data, the congested sections of the city are predicted. In this process, a new method based on congestion vector and congestion transfer matrix is proposed, which abandons the traditional methods based on traffic flow prediction and congestion identification. At the same time, the time periodicity and temporal and spatial correlation of congestion are considered in this method. By mining and training the GPS trajectory data of taxi, the congestion vector and congestion transfer matrix are established to predict the congested road section. Experiments on real data sets verify the effectiveness of the proposed method for predicting congested sections.
【作者单位】： 东北大学信息科学与工程学院;
【基金】：国家自然科学基金资助项目(61272177)
【分类号】：U491;TP311.13
[Abstract]:Based on the real GPS trajectory data, the congested sections of the city are predicted. In this process, a new method based on congestion vector and congestion transfer matrix is proposed, which abandons the traditional methods based on traffic flow prediction and congestion identification. At the same time, the time periodicity and temporal and spatial correlation of congestion are considered in this method. By mining and training the GPS trajectory data of taxi, the congestion vector and congestion transfer matrix are established to predict the congested road section. Experiments on real data sets verify the effectiveness of the proposed method for predicting congested sections.
【作者单位】： 东北大学信息科学与工程学院;
【基金】：国家自然科学基金资助项目(61272177)
【分类号】：U491;TP311.13
【参考文献】
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1 姜桂艳;Q，
本文编号：2473411
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