A random forest incident detection algorithm that incorporates contexts
A random forest incident detection algorithm that incorporates contexts
A major problem faced by state of the art incident detection algorithms is their high false alert rates, which are caused in part by failing to differentiate incidents from contexts. Contexts are referred to as external factors that could be expected to influence traffic conditions, such as sporting events, public holidays and weather conditions. This paper presents RoadCast Incident Detection (RCID), an algorithm that aims to make this differentiation by gaining a better understanding of conditions that could be expected during contexts’ disruption. RCID was found to outperform RAID in terms of detection rate and false alert rate, and had a 25% lower false alert rate when incorporating contextual data. This improvement suggests that if RCID were to be implemented in a Traffic Management Centre, operators would be distracted by far fewer false alerts from contexts than is currently the case with state of the art algorithms, and so could detect incidents more effectively.
Big data, Context, Machine learning, Random forest, Traffic flow prediction
230–242
Evans, Jonny
fa85f2a7-dd18-4b43-9b40-d0f40bb7363d
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Hamilton, Andrew
f26d95d5-1e75-4310-b54b-88e46911ea44
May 2020
Evans, Jonny
fa85f2a7-dd18-4b43-9b40-d0f40bb7363d
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Hamilton, Andrew
f26d95d5-1e75-4310-b54b-88e46911ea44
Evans, Jonny, Waterson, Ben and Hamilton, Andrew
(2020)
A random forest incident detection algorithm that incorporates contexts.
International Journal of Intelligent Transportation Systems Research, 18 (2), .
(doi:10.1007/s13177-019-00194-1).
Abstract
A major problem faced by state of the art incident detection algorithms is their high false alert rates, which are caused in part by failing to differentiate incidents from contexts. Contexts are referred to as external factors that could be expected to influence traffic conditions, such as sporting events, public holidays and weather conditions. This paper presents RoadCast Incident Detection (RCID), an algorithm that aims to make this differentiation by gaining a better understanding of conditions that could be expected during contexts’ disruption. RCID was found to outperform RAID in terms of detection rate and false alert rate, and had a 25% lower false alert rate when incorporating contextual data. This improvement suggests that if RCID were to be implemented in a Traffic Management Centre, operators would be distracted by far fewer false alerts from contexts than is currently the case with state of the art algorithms, and so could detect incidents more effectively.
Text
Evans2019_Article_ARandomForestIncidentDetection
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More information
Accepted/In Press date: 6 July 2019
e-pub ahead of print date: 17 August 2019
Published date: May 2020
Venue - Dates:
15th World Conference on Transport Research, , Mumbai, India, 2019-05-26 - 2019-05-31
Keywords:
Big data, Context, Machine learning, Random forest, Traffic flow prediction
Identifiers
Local EPrints ID: 433700
URI: http://eprints.soton.ac.uk/id/eprint/433700
PURE UUID: 44f1a02a-d4f3-492b-978b-8552036ce09f
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Date deposited: 30 Aug 2019 16:30
Last modified: 17 Mar 2024 02:46
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Author:
Jonny Evans
Author:
Andrew Hamilton
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