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Forecasting road traffic conditions using a context based random forest algorithm

Forecasting road traffic conditions using a context based random forest algorithm
Forecasting road traffic conditions using a context based random forest algorithm
With the ability to accurately forecast road traffic conditions several hours, days and even months ahead of time, both travellers and network managers can take pro-active measures to minimize congestion, saving time, money and emissions. This study evaluates a previously developed random forest algorithm, RoadCast, which was designed to achieve this task. RoadCast incorporates contexts using machine learning to forecast more accurately, contexts such as public holidays, sporting events and school term dates. This study aims to evaluate the potential of RoadCast as a traffic forecasting algorithm for use in Intelligent Transport Systems applications. Tests are undertaken using a number of different forecast horizons and varying amounts of training data, and an implementation procedure is recommended.
1029-0354
554-572
Evans, Jonny
fa85f2a7-dd18-4b43-9b40-d0f40bb7363d
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Hamilton, Andrew
12ead9ac-0af5-4773-a657-906b4d89772b
Evans, Jonny
fa85f2a7-dd18-4b43-9b40-d0f40bb7363d
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Hamilton, Andrew
12ead9ac-0af5-4773-a657-906b4d89772b

Evans, Jonny, Waterson, Ben and Hamilton, Andrew (2019) Forecasting road traffic conditions using a context based random forest algorithm. Transportation Planning and Technology, 42 (6), 554-572. (doi:10.1080/03081060.2019.1622250).

Record type: Article

Abstract

With the ability to accurately forecast road traffic conditions several hours, days and even months ahead of time, both travellers and network managers can take pro-active measures to minimize congestion, saving time, money and emissions. This study evaluates a previously developed random forest algorithm, RoadCast, which was designed to achieve this task. RoadCast incorporates contexts using machine learning to forecast more accurately, contexts such as public holidays, sporting events and school term dates. This study aims to evaluate the potential of RoadCast as a traffic forecasting algorithm for use in Intelligent Transport Systems applications. Tests are undertaken using a number of different forecast horizons and varying amounts of training data, and an implementation procedure is recommended.

Text
201# [10#-A] Roadcast (TPT) - Accepted Manuscript
Restricted to Repository staff only until 27 September 2020.
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More information

Accepted/In Press date: 27 March 2019
e-pub ahead of print date: 11 June 2019
Published date: 21 June 2019

Identifiers

Local EPrints ID: 431040
URI: https://eprints.soton.ac.uk/id/eprint/431040
ISSN: 1029-0354
PURE UUID: 12d1de39-1a4f-4ac7-96d6-cbfed4d5ece2
ORCID for Ben Waterson: ORCID iD orcid.org/0000-0001-9817-7119

Catalogue record

Date deposited: 22 May 2019 16:30
Last modified: 31 Jul 2019 00:48

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