title: A Hidden Markov Model for Urban-Scale Traffic Estimation Using Floating Car Data.
abstract: 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.
authors.affiliation: 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.
authors.forename: Xiaomeng
authors.initials: X
authors.lastname: Wang
authors.affiliation: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.
authors.forename: Ling
authors.initials: L
authors.lastname: Peng
authors.affiliation: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.
authors.forename: Tianhe
authors.initials: T
authors.lastname: Chi
authors.affiliation: College of Economics and Management, Southwest University, Chongqing, China.
authors.forename: Mengzhu
authors.initials: M
authors.lastname: Li
authors.affiliation: 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.
authors.forename: Xiaojing
authors.initials: X
authors.lastname: Yao
authors.affiliation: 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.
authors.forename: Jing
authors.initials: J
authors.lastname: Shao
mesh_terms: D000465:Algorithms; D001336:Automobiles / Q000706:statistics & numerical data*; D002947:Cities / Q000706:statistics & numerical data*; D016000:Cluster Analysis; D057225:Data Mining; D008390:Markov Chains; D015233:Models, Statistical; D011336:Probability; D001335:Vehicle Emissions
chemical_list: D001335:Vehicle Emissions