LAUSR

A precise knowledge about future traffic will eventu- ally open a new era in traffic management. Research has focused on the still unresolved problem of predicting travel time (TT). However, practitioners favor the level of service (LOS) as a meaningful metric that avoids the continuous fluctuations and link-specificity of TT. Evolving from TT to LOS opens a new research line in the field, moving the underlying mathematical problem from regression to classification. This study proposes a short-term LOS classifier to fulfill this requirement. Given that traffic conditions are mostly free-flow throughout the day, LOS classes are unbalanced. Therefore, we based our predictor on a Random Undersampling Boost algorithm (RUSBoost), especially suited to overcome this issue. We trained and validated this LOS predictor with 12 months of arrival travel time data, captured by a Bluetooth network with 6 links, in real operation on the SE-30 highway (Seville, Spain). This classifier achieved an average recall of 82.8 % for prediction horizons up to 15 minutes, reaching 92.5 % predicting congestion. We reached this performance by exploiting two facts that we empirically demonstrated: (i) information from every link (even those in the opposite direction) contributes to increase the accuracy of the prediction; and (ii) traffic presents different behavior depending on the day of the week, which we used to segment the data and construct specific classifiers. These promising results show the potential of the proposed LOS predictor, providing a new perspective into traffic forecast and the subsequent traffic management that yields with what practitioners demand.