A Hybrid CNN-LSTM Model for Traffic Accident Frequency Forecasting During the Tourist Season

Abstract

Population density in major tourist centers of the world increases significantly during the tourist season. Estimating the frequency of traffic accidents during the upcoming tourist season is of particular interest to many stakeholders, such as local governments. The objective of this study is to propose a hybrid deep learning model, based on convolutional neural network (CNN) and long short term memory (LSTM) models to predict the frequency of traffic accidents during the tourism season. The dataset used in the study includes daily frequencies of traffic accidents with fatalities and injuries that occurred in Antalya between January 2012 and December 2017. In the next phase of the study, seasonal autoregressive integrated moving average (SARIMA), Facebook prophet and deep learning methods including LSTM and the proposed Hybrid CNN-LSTM were tested to predict traffic accident frequencies in Antalya. The experimental results show that the root mean square error (RMSE) of the proposed model is less than 2480, 13266 and 186 compared to SARIMA, prophet and LSTM models, respectively. Also, the R-squared value of the proposed model is greater than 0.016, 0.103 and 0.001 compared to SARIMA, prophet and LSTM models, respectively. It is clear that the proposed hybrid CNN-LSTM model was more successful in predicting traffic accidents when compared to the other models.

Keywords

• convolutional neural network
• SARIMA model
• time series analysis
• tourism
• traffic accident forecasting