Self-Learning Algorithm and Signal State Prognosis at Traffic Lights for V2I Applications
V2I applications are increasingly getting into the focus of traffic optimization. Especially in urban areas the traffic lights affect the traffic flow substantially. An exchange of information on remaining green- or redtimes of the signal groups and an information about the optimal speed for a green wave promise large effects in terms of Level of Service [1]. The prognosis of switching times for non-fixed-time traffic lights is not trivial, even more so under adaptive control. This paper presents a self-learning algorithm for signal plans in combination with a prognosis algorithm for switching times of traffic lights at fixed-time and also of adaptive signalization. It shows areas of application and ways of visualization. Most of the results are based on the Austrian research project SHARE.
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Availability:
- Find a library where document is available. Order URL: http://www.its-jp.org/english/congress_e/
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Supplemental Notes:
- Abstract used with permission of ITS Japan. Paper No. 4081. Alternate title: Self-Learning Algorithm and Signal State Prognosis at Traffic Lights for V2I-Applications.
- Corporate Authors: Tokyo, Japan
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Authors:
- Otto, Thomas
- Weichenmeier, Florian
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Conference:
- 20th ITS World Congress
- Location: Tokyo , Japan
- Date: 2013-10-14 to 2013-10-18
- Publication Date: 2013
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References;
- Pagination: 11p
- Monograph Title: 20th ITS World Congress, Tokyo 2013. Proceedings
Subject/Index Terms
- TRT Terms: Adaptive control; Algorithms; Markov chains; Public transit; Signalized intersections; Traffic signal control systems; Traffic signal cycle; Vehicle to infrastructure communications
- Subject Areas: Highways; Operations and Traffic Management; Public Transportation; I73: Traffic Control;
Filing Info
- Accession Number: 01535479
- Record Type: Publication
- ISBN: 9784990493981
- Files: TRIS
- Created Date: Aug 27 2014 10:47AM