A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data

• Published in: PloS one Vol. 10; no. 12; p. e0145348
• Main Authors: Wang, Xiaomeng, Peng, Ling, Chi, Tianhe, Li, Mengzhu, Yao, Xiaojing, Shao, Jing
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.12.2015; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Automobiles - statistics & numerical data; Cities - statistics & numerical data; Cluster Analysis; Clustering; Data Mining; Demographic aspects; Floating; Flow velocity; Global positioning systems; GPS; Markov Chains; Markov processes; Model accuracy; Models, Statistical; Pattern analysis; Principal components analysis; Probability; Remote sensing; Roads & highways; Segments; Studies; Traffic congestion; Traffic flow; Traffic information; Traffic models; Urban environments; Vehicle Emissions; Vehicles; China; Beijing China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0145348

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.