Predicting ambient traffic of a vehicle from road abrasion measurements using random forest

The development and application of Intelligent Transportation Systems (ITSs) leads to a growing demand of traffic data. Floating Car Observers (FCOs) contribute by providing information about ambient traffic of vehicles while driving. We present an approach to implement an FCO that uses particulate matter sensors for obtaining road abrasion from cars driving ahead of a test vehicle. Using Random Forest (RF), we predict presence and absence of ambient traffic in the vicinity of test vehicle with particulate matter readings (PM01, PM2.5, PM10) as predictor variables. Results show that RF reaches prediction accuracy ranging from 86 to 99 percent for different train/test split options when analysing individual trajectories as well as 88 to 91 percent accuracy when analysing all trajectories combined. We face limitations mainly when merging single trajectories, due to different initial ambient particulate matter values. We conclude that presence and absence of ambient traffic are predictable using Random Forest with road abrasion values as predictor variables. Further, rainfall events (that may cause wash-off effects on roads) do not significantly change the accuracy of our classification. Optimisation of the model and the need of testing more diverse weather and road conditions remain open tasks for future research.