Spillover Detection for Urban Traffic Networks Using Signal Timing and Stop Line Detector Data
A spillover detection method for urban traffic networks is proposed using only signal timing and stop line detector data. The method uses measurements from a local detector, with data from downstream intersections used for validation. A new measure, termed a spillover index, is introduced that denotes the reduced traffic flow for each signal cycle caused by spillovers. The spillover index is compared with a predefined threshold to detect spillovers. The proposed method is tested and validated on a small simulated network and a busy arterial road in the city of Dublin, which is controlled by the SCATS adaptive traffic signal control system. The evolution of spillovers on an arterial road is studied using a Markov Chain process. Field data are used to determine the transition probabilities among all possible spillover states. A sensitivity analysis is performed to study the detection accuracy.
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Supplemental Notes:
- This paper was sponsored by TRB committee AHB25 Standing Committee on Traffic Signal Systems.
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Wang, Ren
- Nair, Rahul
- Pascale, Alessandra
- Work, Daniel
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Conference:
- Transportation Research Board 96th Annual Meeting
- Location: Washington DC, United States
- Date: 2017-1-8 to 2017-1-12
- Date: 2017
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 16p
- Monograph Title: TRB 96th Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Algorithms; Detection and identification systems; Markov chains; Traffic oversaturation; Traffic signal timing
- Identifier Terms: SCATS (Computer program)
- Uncontrolled Terms: Spillover; Stop lines
- Geographic Terms: Dublin (Ireland)
- Subject Areas: Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01628724
- Record Type: Publication
- Report/Paper Numbers: 17-04213
- Files: TRIS, TRB, ATRI
- Created Date: Mar 10 2017 9:21AM