Putting intelligence back into traffic lights
Putting intelligence back into traffic lights
The first sets of traffic lights (in the 19th century) were controlled by a person stood beside them, however as technologies have developed, humans have increasingly been taken out of the control loop. Modern traffic lights are designed to monitor approaching vehicles automatically and this data then forms the input for algorithms to decide which direction should get a green light and for how long. Over time these algorithms have become more complex (to account for wider policy priorities such as pedestrian, cyclist and bus priority), but are limited by their inherent lack of flexibility in rapidly changing traffic conditions and high traffic flows, the very conditions where the adaptability of a human mind would be appropriate.
This paper therefore reports on a study where teams of four human participants were given control of the traffic lights at four close proximity road junctions in a computer simulation. With no prior information, discussions or explanation of what the precise objective function of a control strategy should be, individuals were allowed to make up their own mind as to how they wanted the junction to operate and the consequential impacts on vehicles. Teams chose variously to either work cooperatively from the outset or to essentially ignore each other until other team members performance began to have a direct impact. Conclusions are presented illustrating the range of control strategies that emerged and their resulting impacts on the overall performance of the simulated traffic network.
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Osowski, Christopher, John
50ff2ca5-f0d7-4bc7-aadb-5a53bc92dbe3
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0
Baker, James
a8a45768-5dc3-482f-98d2-d067d66a299c
14 September 2017
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Osowski, Christopher, John
50ff2ca5-f0d7-4bc7-aadb-5a53bc92dbe3
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0
Baker, James
a8a45768-5dc3-482f-98d2-d067d66a299c
Waterson, Ben, Osowski, Christopher, John, Wills, Gary and Baker, James
(2017)
Putting intelligence back into traffic lights.
59th Annual Conference of the Operational Research Society, , Loughborough, United Kingdom.
12 - 14 Sep 2017.
Record type:
Conference or Workshop Item
(Other)
Abstract
The first sets of traffic lights (in the 19th century) were controlled by a person stood beside them, however as technologies have developed, humans have increasingly been taken out of the control loop. Modern traffic lights are designed to monitor approaching vehicles automatically and this data then forms the input for algorithms to decide which direction should get a green light and for how long. Over time these algorithms have become more complex (to account for wider policy priorities such as pedestrian, cyclist and bus priority), but are limited by their inherent lack of flexibility in rapidly changing traffic conditions and high traffic flows, the very conditions where the adaptability of a human mind would be appropriate.
This paper therefore reports on a study where teams of four human participants were given control of the traffic lights at four close proximity road junctions in a computer simulation. With no prior information, discussions or explanation of what the precise objective function of a control strategy should be, individuals were allowed to make up their own mind as to how they wanted the junction to operate and the consequential impacts on vehicles. Teams chose variously to either work cooperatively from the outset or to essentially ignore each other until other team members performance began to have a direct impact. Conclusions are presented illustrating the range of control strategies that emerged and their resulting impacts on the overall performance of the simulated traffic network.
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Published date: 14 September 2017
Venue - Dates:
59th Annual Conference of the Operational Research Society, , Loughborough, United Kingdom, 2017-09-12 - 2017-09-14
Identifiers
Local EPrints ID: 420218
URI: http://eprints.soton.ac.uk/id/eprint/420218
PURE UUID: c49dd8eb-8eb9-44fe-8b63-dbd444053188
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Date deposited: 02 May 2018 16:30
Last modified: 02 Mar 2023 02:36
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Contributors
Author:
Christopher, John Osowski
Author:
Gary Wills
Author:
James Baker
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