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Comparison of signalized junction control strategies using individual vehicle position data

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
Box, S. and Waterson, B. J. (2010) Comparison of signalized junction control strategies using individual vehicle position data At 5th IMA Conference on Mathematics in Transport, United Kingdom. 12 - 14 Apr 2010.

Box, S. and Waterson, B. J. (2010) Comparison of signalized junction control strategies using individual vehicle position data At 5th IMA Conference on Mathematics in Transport, United Kingdom. 12 - 14 Apr 2010.

Record type: Conference or Workshop Item (Paper)

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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More information

Published date: April 2010
Venue - Dates: 5th IMA Conference on Mathematics in Transport, United Kingdom, 2010-04-12 - 2010-04-14

Identifiers

Local EPrints ID: 74658
URI: http://eprints.soton.ac.uk/id/eprint/74658
PURE UUID: f64f668a-f2c8-4928-b007-bfaa2e95c5b5
ORCID for B. J. Waterson: ORCID iD orcid.org/0000-0001-9817-7119

Catalogue record

Date deposited: 11 Mar 2010
Last modified: 18 Jul 2017 23:45

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