Comparison of signalized junction control strategies using individual vehicle position data
Comparison of signalized junction control strategies using individual vehicle position data
This paper is concerned with the development of control strategies for urban signalized junction that can make use of individual vehicle position data from localization probes on board the vehicles. Strategy development involves simulating the behaviour of vehicles as they negotiate junctions controlled by prototype strategies and evaluating performance. Two strategies are discussed in this paper, a simple auctioning agent strategy and an extended auctioning agent strategy where a machine learning approach is used to enable agents to be trained by a human expert to improve performance. The performance of these two strategies are compared with each other and with the MOVA algorithm in simulated tests. The results show that auctioning agents using individual vehicle position data can out perform MOVA, but that this performance can be improved further still by using learning auctioning agents trained by a human expert.
Box, S.
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Waterson, B.J.
60a59616-54f7-4c31-920d-975583953286
April 2010
Box, S.
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Waterson, B.J.
60a59616-54f7-4c31-920d-975583953286
Box, S. and Waterson, B.J.
(2010)
Comparison of signalized junction control strategies using individual vehicle position data.
5th IMA Conference on Mathematics in Transport, , London, United Kingdom.
12 - 14 Apr 2010.
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Conference or Workshop Item
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Abstract
This paper is concerned with the development of control strategies for urban signalized junction that can make use of individual vehicle position data from localization probes on board the vehicles. Strategy development involves simulating the behaviour of vehicles as they negotiate junctions controlled by prototype strategies and evaluating performance. Two strategies are discussed in this paper, a simple auctioning agent strategy and an extended auctioning agent strategy where a machine learning approach is used to enable agents to be trained by a human expert to improve performance. The performance of these two strategies are compared with each other and with the MOVA algorithm in simulated tests. The results show that auctioning agents using individual vehicle position data can out perform MOVA, but that this performance can be improved further still by using learning auctioning agents trained by a human expert.
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BoxWatIMA2010.pdf
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Published date: April 2010
Venue - Dates:
5th IMA Conference on Mathematics in Transport, , London, United Kingdom, 2010-04-12 - 2010-04-14
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Local EPrints ID: 74658
URI: http://eprints.soton.ac.uk/id/eprint/74658
PURE UUID: f64f668a-f2c8-4928-b007-bfaa2e95c5b5
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Date deposited: 11 Mar 2010
Last modified: 14 Mar 2024 02:41
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S. Box
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