Real-Time Incident Clearance Time Prediction Using Traffic Data from Internet of Mobility Sensors

The time wasted in traffic congestion especially the non-recurring (unexpected) is priceless. Intelligent Transportation Systems (ITS) technologies aim at improving transportation safety and mobility, reduce environmental impacts, and enhance productivity by utilizing the current available infrastructure. Providing the drivers with an accurate clearance time will help them manage their trips efficiently. Existing studies attempted to predict the clearance time of highway incidents through statistical modeling by fitting the incident features to their models. Unfortunately, the incident features are not available at the beginning of the incident. In this study, we develop efficient prediction models by training millions of real-time traffic and weather data from Florida Department of Transportation District 4 (FDOT-D4), in conjunction with the crash data reported for the same period. Our models can predict the incident clearance time in real-time once the accident happens by using the real-time traffic data. In this paper, we mainly use Deep Leaning (DL) to build the clearance time prediction model. In addition, we build two other models, Distributed Random Forests (DRF)and Generalized Linear Model (GLM) then, we compare the results of all these models. Traffic Management Centers (TMCs) will use the predicted results to display them on Dynamic Message Signs (DMSs) and broadcast them on the Highway Advisory Radio (HAR). The models performances are validated with the actual clearance times provided by the incidents respondents. DL outperforms both DRF and GLM in predicting the clearance time, reflected in the lowest root mean squared error (RMSE) of 0.99 and the lowest mean absolute error (MAE) of 0.6.