A Multi-Agent Case-Based Traffic Control Scenario Evaluation System
Reference
B. De Schutter,
S.P. Hoogendoorn,
H. Schuurman, and
S. Stramigioli,
"A Multi-Agent Case-Based Traffic Control Scenario Evaluation System,"
Proceedings of the IEEE 6th International Conference
on Intelligent Transportation Systems (ITSC'03), Shanghai,
China, pp. 678-683, Oct. 2003.
Abstract
Traffic operators in traffic control centers have various measures at
their disposition to control the traffic flows on motorways and on
urban roads such as ramp metering, variable speed limits, dynamic
route guidance, opening of shoulder lanes, etc. When having to
determine which of these control measures have to be applied and where
they have to be applied for a given traffic situation, the traffic
operator should be able to predict the effect of a control scenario in
order to be able to select the best scenario. As on-line, real-time
simulation of a large number of possible scenarios is usually not
tractable for even relatively small motorway networks, a fast method
to predict the effects of control measures on-line is a key
requirement for effectively applying traffic control. In this paper we
develop a multi-agent case-based approach to assist traffic operators
in evaluating or predicting the effects of control measures. The
proposed approach is much faster than straightforward traffic
simulation so that it can be used for on-line and real-time evaluation
of a large number of different control scenarios. In addition, it is
scalable so that it can also be used for large networks.
Downloads
- Corresponding technical report:
pdf
file
(361 KB)
Bibtex entry
@inproceedings{DeSHoo:02-018,
author={B. {D}e Schutter and S.P. Hoogendoorn and H. Schuurman and S.
Stramigioli},
title={A Multi-Agent Case-Based Traffic Control Scenario Evaluation System},
booktitle={Proceedings of the IEEE 6th International Conference on Intelligent
Transportation Systems (ITSC'03)},
address={Shanghai, China},
pages={678--683},
month=oct,
year={2003}
}
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Last update: February 21, 2026.