Citation

Evans J, Waterson B, Hamilton A. Transp. Plann. Tech. 2019; 42(6): 554-572.

Copyright

(Copyright © 2019, Informa - Taylor and Francis Group)

DOI

10.1080/03081060.2019.1622250

PMID

unavailable

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 minimise 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 paper evaluates the potential of RoadCast as a traffic forecasting algorithm for use in Intelligent Transport System applications. Tests are undertaken using a number of different forecast horizons and varying amounts of training data, and an implementation procedure is recommended.

Language: en

Keywords

context; loop detector; machine learning; random forest; RoadCast; Traffic forecasting