Automatic Near-Stationary Traffic State Identification Based on PELT Changepoint Detection
The existence of stationary traffic states has been widely used as a key assumption in studying traffic flow models and analyzing transportation network problems. However, many empirical studies do not properly use near-stationary states to establish or validate their models. In addition, there lacks a systematic method to identify near-stationary states in the transportation literature. This paper starts with a comprehensive introduction of stationary states and presents an automatic near-stationary state identification method. In this method, the authors first apply a pruned exact linear time (PELT) changepoint detection algorithm to split flow and occupancy sequences into multiple candidate intervals, in each of which averages of flows/occupancies stay relatively constant over time. After deriving characteristics of the detected candidate intervals, the authors present two identification criteria based on Cassidy’s method to extract near-stationary states from the candidates. The authors provide two ways of representing near-stationary states and apply near-stationary data on the fundamental diagram calibration. The results demonstrate that the identified near-stationary states can yield a nearly scatter-free flow-occupancy relation, and further lead to a well-defined triangular fundamental diagram.
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Supplemental Notes:
- This paper was sponsored by TRB committee AHB45 Standing Committee on Traffic Flow Theory and Characteristics. Alternate title: Automatic Near-Stationary Traffic State Identification Pased on PELT Changepoint Detection
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Yan, Qinglong
- Sun, Zhe
- Gan, Qijian
- Jin, Wenlong
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0000-0002-5413-8377
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Conference:
- Transportation Research Board 95th Annual Meeting
- Location: Washington DC, United States
- Date: 2016-1-10 to 2016-1-14
- Date: 2016
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 20p
- Monograph Title: TRB 95th Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Algorithms; Methodology; Traffic density; Traffic flow; Traffic speed
- Uncontrolled Terms: Fundamental diagram
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01592758
- Record Type: Publication
- Report/Paper Numbers: 16-2847
- Files: TRIS, TRB, ATRI
- Created Date: Mar 4 2016 5:05PM