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An automated signalized junction controller that learns strategies from a human expert

Record type: Article

An automated signalized junction control system that can learn strategies from a human expert has been developed. This system applies machine learning techniques based on logistic regression and neural networks to affect a classification of state space using evidence data generated when a human expert controls a simulated junction. The state space is constructed from a series of bids from agents, which monitor regions of the road network. This builds on earlier work which has developed the High Bid auctioning agent system to control signalized junctions using localization probe data. For reference the performance of the machine learning signal control strategies are compared to that of High Bid and the MOVA system, which uses inductive loop detectors. Performance is evaluated using simulation experiments on two networks. One is an isolated T-junction and the other is a two junction network modelled on the High Road area of Southampton, UK. The experimental results indicate that machine learning junction control strategies trained by a human expert can outperform High Bid and MOVA both in terms of minimizing average delay and maximizing equitability; where the variance of the distribution over journey times is taken as a quantitative measure of equitability. Further experimental tests indicate that the machine learning control strategies are robust to variation in the positioning accuracy of localization probes and to the fraction of vehicles equipped with probes.

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Citation

Box, Simon and Waterson, Ben (2012) An automated signalized junction controller that learns strategies from a human expert Engineering Applications of Artificial Intelligence, 25, (1), pp. 107-118. (doi:10.1016/j.engappai.2011.09.008).

More information

Accepted/In Press date: 28 September 2011
Published date: February 2012
Organisations: Transportation Group

Identifiers

Local EPrints ID: 207831
URI: http://eprints.soton.ac.uk/id/eprint/207831
ISSN: 0952-1976
PURE UUID: 1fed891d-5e0d-43e9-9011-a72929bac8c6
ORCID for Ben Waterson: ORCID iD orcid.org/0000-0001-9817-7119

Catalogue record

Date deposited: 12 Jan 2012 11:01
Last modified: 18 Jul 2017 10:49

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