Wang Xiaomeng, Peng Ling, Chi Tianhe, Li Mengzhu, Yao Xiaojing, Shao Jing
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.
University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China.
PLoS One. 2015 Dec 28;10(12):e0145348. doi: 10.1371/journal.pone.0145348. eCollection 2015.
Urban-scale traffic monitoring plays a vital role in reducing traffic congestion. Owing to its low cost and wide coverage, floating car data (FCD) serves as a novel approach to collecting traffic data. However, sparse probe data represents the vast majority of the data available on arterial roads in most urban environments. In order to overcome the problem of data sparseness, this paper proposes a hidden Markov model (HMM)-based traffic estimation model, in which the traffic condition on a road segment is considered as a hidden state that can be estimated according to the conditions of road segments having similar traffic characteristics. An algorithm based on clustering and pattern mining rather than on adjacency relationships is proposed to find clusters with road segments having similar traffic characteristics. A multi-clustering strategy is adopted to achieve a trade-off between clustering accuracy and coverage. Finally, the proposed model is designed and implemented on the basis of a real-time algorithm. Results of experiments based on real FCD confirm the applicability, accuracy, and efficiency of the model. In addition, the results indicate that the model is practicable for traffic estimation on urban arterials and works well even when more than 70% of the probe data are missing.
城市规模的交通监测在缓解交通拥堵方面发挥着至关重要的作用。由于其成本低且覆盖范围广，浮动车数据（FCD）成为一种收集交通数据的新方法。然而，在大多数城市环境中，稀疏的探测数据在干道可用数据中占绝大多数。为了克服数据稀疏问题，本文提出一种基于隐马尔可夫模型（HMM）的交通估计模型，其中路段的交通状况被视为一个隐藏状态，可以根据具有相似交通特征的路段状况来估计。提出一种基于聚类和模式挖掘而非邻接关系的算法，以找到具有相似交通特征的路段聚类。采用多聚类策略来实现聚类精度和覆盖范围之间的权衡。最后，基于实时算法设计并实现了所提出的模型。基于真实浮动车数据的实验结果证实了该模型的适用性、准确性和效率。此外，结果表明该模型对于城市干道的交通估计是可行的，即使超过70%的探测数据缺失时也能良好运行。
