A Novel Short-term Post-accident Traffic Prediction Model

Traffic forecasting at appropriate times is vital for a variety of urban traffic control applications. There are a plethora of factors that can severely affect the performance of such forecasting models, especially those unpredictable events that cannot be learned through previous time steps in the model. One of the important factors that can change the traffic flow pattern abruptly, is accident occurrence. This issue affects more when the severity of the accident is high which would lead to a complete anomaly behavior in the traffic flow pattern. In this paper, we detected the approximate time that the accidents happen through anomaly detection around the accident record and then pulled out the ones that change the traffic pattern suddenly. To predict the traffic flow after a severe accident, we propose a hybrid CNN-LSTM model that takes spatio-temporal matrices as discrete time sequences for each accident. Furthermore, we leverage distances between neighboring nodes and accident location, coupled with static features that are pertained to the accidents. Moreover, we evaluate our model on PeMs dataset which is enhanced with the static features of the accidents from Moosavi's accident data set [1] [2]. Despite recent trials that are not able to forecast the results when an anomaly event happens, our results demonstrate that our proposed model can learn traffic patterns after the accidents and predict the traffic flow more accurately compared to current models.