Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4999540 | Annual Reviews in Control | 2017 | 24 Pages |
Abstract
Based on our proposed characterization, we present the current state of TSE research and proposed future research directions. Some of the findings of this article are summarized as follows. We present model-driven approaches commonly used. We summarize the recent usage of detailed disaggregated mobile data for the purpose of TSE. The use of these models and data will raise a challenging problem due to the fact that conventional macroscopic models are not always consistent with detailed disaggregated data. Therefore, we show two possibilities in order to solve this problem: improvement of theoretical models, and the use of data-driven or streaming-data-driven approaches, which recent studies have begun to consider. Another open problem is explicit consideration of traffic demand and route-choice in a large-scale network; for this problem, emerging data sources and machine learning would be useful.
Keywords
PTMBVPKFTSMMasfTSEEnKFCTMTTILWRPhase transition modelPDEFDMCDREKFADAsTraffic state estimationTraffic dataFinite difference methodGPSAdvanced driver assistance systemNumerical schemeParticle FilterKalman filterextended Kalman filterConservation lawCell transmission modelTraffic flow modelFundamental diagramHamilton–JacobiEnsemble Kalman FilterMachine learning
Related Topics
Physical Sciences and Engineering
Engineering
Control and Systems Engineering
Authors
Toru Seo, Alexandre M. Bayen, Takahiko Kusakabe, Yasuo Asakura,