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Sensors 2016, 16(2), 147;

3D Markov Process for Traffic Flow Prediction in Real-Time

Visual Information Processing Laboratory, Department of Internet & Multimedia Engineering, Konkuk University, Seoul 05029, Korea
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 8 August 2015 / Revised: 15 January 2016 / Accepted: 15 January 2016 / Published: 25 January 2016
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [4252 KB, uploaded 25 January 2016]   |  


Recently, the correct estimation of traffic flow has begun to be considered an essential component in intelligent transportation systems. In this paper, a new statistical method to predict traffic flows using time series analyses and geometric correlations is proposed. The novelty of the proposed method is two-fold: (1) a 3D heat map is designed to describe the traffic conditions between roads, which can effectively represent the correlations between spatially- and temporally-adjacent traffic states; and (2) the relationship between the adjacent roads on the spatiotemporal domain is represented by cliques in MRF and the clique parameters are obtained by example-based learning. In order to assess the validity of the proposed method, it is tested using data from expressway traffic that are provided by the Korean Expressway Corporation, and the performance of the proposed method is compared with existing approaches. The results demonstrate that the proposed method can predict traffic conditions with an accuracy of 85%, and this accuracy can be improved further. View Full-Text
Keywords: traffic flow forecasting; Markov process; spatio-temporal domain; vehicle detection sensor; heat map traffic flow forecasting; Markov process; spatio-temporal domain; vehicle detection sensor; heat map

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Ko, E.; Ahn, J.; Kim, E.Y. 3D Markov Process for Traffic Flow Prediction in Real-Time. Sensors 2016, 16, 147.

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