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.

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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.