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http://dx.doi.org/10.7470/jkst.2016.34.3.278

Methodology for Real-time Detection of Changes in Dynamic Traffic Flow Using Turning Point Analysis  

KIM, Hyungjoo (The Cho Chun Shik Graduate School for Green Transportation, KAIST)
JANG, Kitae (The Cho Chun Shik Graduate School for Green Transportation, KAIST)
KWON, Oh Hoon (Department of Transportation Engineering, Keimyung University)
Publication Information
Journal of Korean Society of Transportation / v.34, no.3, 2016 , pp. 278-290 More about this Journal
Abstract
Maximum traffic flow rate is an important performance measure of operational status in transport networks, and has been considered as a key parameter for transportation operation since a bottleneck in congestion decreases maximum traffic flow rate. Although previous studies for traffic flow analysis have been widely conducted, a detection method for changes in dynamic traffic flow has been still veiled. This paper explores the dynamic traffic flow detection that can be utilized for various traffic operational strategies. Turning point analysis (TPA), as a statistical method, is applied to detect the changes in traffic flow rate. In TPA, Bayesian approach is employed and vehicle arrival is assumed to follow Poisson distribution. To examine the performance of the TPA method, traffic flow data from Jayuro urban expressway were obtained and applied. We propose a novel methodology to detect turning points of dynamic traffic flow in real time using TPA. The results showed that the turning points identified in real-time detected the changes in traffic flow rate. We expect that the proposed methodology has wide application in traffic operation systems such as ramp-metering and variable lane control.
Keywords
bayesian approach; dynamic traffic flow; real-time detection; traffic flow data; turning point analysis;
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