不完全数据下基于时空相关性拥堵预测方法
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摘要
交通拥堵预测是智慧交通一个重要组成部分,但是大量的交通数据无法以公开的方式获取。在不完全数据下,提出了一种基于时空相关性的交通拥堵预测方法。该方法采用改进的核密度估计法,使得预测过程中不依赖大量历史数据进行训练,直接利用部分采集到的数据精准地实时地对交通拥堵进行预测。在真实数据集上对提出的交通拥堵预测方法进行验证,实验结果表明了该方法在实时交通预测上的可行性。
Traffic congestion prediction is an important part of smart traffic, but a large amount of traffic data cannot be obtained in a public way. Based on incomplete data, a method based on spatial-temporal correlativity for traffic congestion prediction is proposed. The improved kernel density estimation method is adopted, so that the prediction process does not rely on a large amount of historical data for training, and the traffic congestion can be accurately predicted in real time with the partially collected data. The proposed traffic congestion prediction method is validated in the real data set, and the experimental results show the feasibility of this method in real-time traffic prediction.
Traffic congestion prediction is an important part of smart traffic, but a large amount of traffic data cannot be obtained in a public way. Based on incomplete data, a method based on spatial-temporal correlativity for traffic congestion prediction is proposed. The improved kernel density estimation method is adopted, so that the prediction process does not rely on a large amount of historical data for training, and the traffic congestion can be accurately predicted in real time with the partially collected data. The proposed traffic congestion prediction method is validated in the real data set, and the experimental results show the feasibility of this method in real-time traffic prediction.
引文
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[2]刘静,关伟.交通流预测方法综述[J].公路交通科技,2004,21(3):82-85.
[3]Ghosh B,Basu B,O’mahony M.Multivariate short-term traffic flow forecasting using time-series analysis[J].IEEETransactions on Intelligent Transportation Systems,2009,10(2):246-254.
[4]Sheather S J,Jones M C.A reliable data-based bandwidth selection method for kernel density estimation[J].Journal of the Royal Statistical Society,1991,53(3):683-690.
[5]李松,刘力军,翟曼.改进粒子群算法优化BP神经网络的短时交通流预测[J].系统工程理论与实践,2012,32(9):2045-2049.
[6]Polson N G,Sokolov V O.Deep learning for short-term traffic flow prediction[J].Transportation Research Part C:Emerging Technologies,2017,79:1-17.
[7]Fouladgar M,Parchami M,Elmasri R,et al.Scalable deep traffic flow neural networks for urban traffic congestion prediction[C]//2017 International Joint Conference on Neural Networks,2017:2251-2258.
[8]陈娟,方滨兴,殷丽华,等.传感器网络中基于源节点有限洪泛的源位置隐私保护协议[J].计算机学报,2010,33(9):1736-1747.
[9]薛姣,刘向宇,杨晓春,等.一种面向公路网络的位置隐私保护方法[J].计算机学报,2011,34(5):865-878.
[10]彭志宇,李善平.移动环境下LBS位置隐私保护[J].电子与信息学报,2011,33(5):1211-1216.
[11]Vlahogianni E I,Karlaftis M G,Golias J C.Short-term traffic forecasting:where we are and where we’re going[J].Transportation Research Part C:Emerging Technologies,2014,43:3-19.
[12]禹文豪,艾廷华.核密度估计法支持下的网络空间POI点可视化与分析[J].测绘学报,2015,44(1):82-90.
[13]Elgammal A,Duraiswami R,Harwood D,et al.Background and foreground modeling using nonparametric kernel density estimation for visual surveillance[J].Proc IEEE,2002,90(7):1151-1163.
[14]Borruso G.Network density and the delimitation of urban areas[J].Transactions in GIS,2003,7(2):177-191.
[15]Borruso G.Network density estimation:a GIS approach for analysing point patterns in a network space[J].Transactions in GIS,2008,12(3):377-402.
[16]Yuan J,Zheng Y,Zhang C,et al.T-drive:driving directions based on taxi trajectories[C]//Sigspatial International Conference on Advances in Geographic Information Systems,2010:99-108.
[17]林树宽,于伶姿,乔建忠,等.基于GPS轨迹数据的拥堵路段预测[J].东北大学学报(自然科学版),2015,36(11):1530-1534.