title: Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data.
abstract: Traffic state estimation from the floating car system is a challenging problem. The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%.
authors.affiliation: Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things, Room 213, Transportation Building, Southeast University, No. 2 Si Pai Lou, Nanjing, 210096, P.R. China.
authors.forename: Bin
authors.initials: B
authors.lastname: Ran
authors.affiliation: Department of Transportation Engineering, Beijing Institute of Technology, Beijing, 100081, P.R. China.
authors.forename: Li
authors.initials: L
authors.lastname: Song
authors.affiliation: Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things, Room 213, Transportation Building, Southeast University, No. 2 Si Pai Lou, Nanjing, 210096, P.R. China.
authors.forename: Jian
authors.identifier: http://orcid.org/0000-0002-9086-7622
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things, Room 213, Transportation Building, Southeast University, No. 2 Si Pai Lou, Nanjing, 210096, P.R. China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Cheng
authors.affiliation: Department of Transportation Engineering, Beijing Institute of Technology, Beijing, 100081, P.R. China.
authors.forename: Huachun
authors.initials: H
authors.lastname: Tan
mesh_terms: D000465:Algorithms; D001336:Automobiles; D001359:Aviation; D003198:Computer Simulation; D014186:Transportation