A random forest incident detection algorithm that incorporates contexts

Evans, Jonny, Rhys Alexander, Waterson, Ben and Hamilton, Andrew (2019) A random forest incident detection algorithm that incorporates contexts. 15th World Conference on Transport Research, , Mumbai, India. 26 - 31 May 2019. 14 pp .

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 is based on a previously developed random forest traffic forecasting algorithm, RoadCast, which uses contextual data to create forecasts of traffic conditions that could be expected if no incident occurred. RCID compares these forecasts with real-time conditions, and raises alerts when there is a sufficient difference. RCID was evaluated on loop detector flow data and city council incident logs from Southampton, U.K. Comparisons were made with and without context, and to a state of the art algorithm, RAID. RCID was found to outperform RAID in terms of detection rate and false alert rate. RCID was also found to have 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.

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Contributors

Author: Ben Waterson

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