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

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), 107-118. (doi:10.1016/j.engappai.2011.09.008).


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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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.engappai.2011.09.008
ISSNs: 0952-1976 (print)
Related URLs:
Subjects: H Social Sciences > HE Transportation and Communications
T Technology > TA Engineering (General). Civil engineering (General)
Divisions : Faculty of Engineering and the Environment > Civil, Maritime and Environmental Engineering and Science > Transportation Research Group
ePrint ID: 207831
Accepted Date and Publication Date:
February 2012Published
28 September 2011In press
Date Deposited: 12 Jan 2012 11:01
Last Modified: 31 Mar 2016 13:48
Improved Traffic Signal Control through Individual Vehicle Position Data
Funded by: EPSRC (EP/E045960/1)
Led by: Benedict Waterson
1 May 2008 to 31 August 2010

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