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.

中文翻译：

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使用基于上下文的随机森林算法预测道路交通状况

摘要 由于能够提前数小时、数天甚至数月准确预测道路交通状况，旅行者和网络管理员都可以采取主动措施来减少拥堵，节省时间、金钱和排放。本研究评估了先前开发的随机森林算法 RoadCast，该算法旨在完成此任务。RoadCast 结合使用机器学习的上下文来更准确地预测上下文，例如公共假期、体育赛事和学期日期。本文评估了 RoadCast 作为用于智能交通系统应用的交通预测算法的潜力。使用许多不同的预测范围和不同数量的训练数据进行测试，并建议实施程序。