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

Eunjeong Ko,Eun Kim,Jinyoung Ahn

doi:10.3390/s16020147

Eunjeong Ko, Eun Kim + Show 1 more

Open Access

https://doi.org/10.3390/s16020147

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Journal: Sensors (Basel, Switzerland)	Publication Date: Jan 25, 2016
Citations: 21	License type: CC BY 4.0

Affiliation: Konkuk University

Abstract

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.

Highlights

IntroductionAccurate traffic flow prediction is receiving significant attention in the research of IntelligentTransportation Systems (ITSs) [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]
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 Markov random field (MRF) and the clique parameters are obtained by example-based learning
Traffic flow prediction that provides short- and long-term forecasts using real-time data is an essential component in controlling traffic in IntelligentTransportation Systems (ITSs)

Summary

Introduction

Accurate traffic flow prediction is receiving significant attention in the research of IntelligentTransportation Systems (ITSs) [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]. Accurate traffic flow prediction is receiving significant attention in the research of Intelligent. The short- and long-term traffic forecasting, i.e., to provide the traffic flows of the or several periods of time in the future, using real-time data is essential for providing traffic control in ITSs. Through traffic flow prediction, ITSs can control and manage traffic conditions. Parametric approaches have been well established using mathematical models to describe the traffic state and its trends; these approaches are referred to as model-driven approaches. Numerous parametric methods have been investigated, including the simplest method using historical averages. Among these parametric methods, the most representative ones have been developed by Wang and Papageorgiou [2] and Ramezani et al [3]

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Results

Conclusion