Forecasting vehicular traffic flow using MLP and LSTM

This work presents an analysis of Artificial Neural Network (ANN) based forecasting models for vehicular traffic flow. The forecasting was performed for three periods of time: 1 week, 1 month and 1 year. The vehicular traffic flow data analysed were collected from Interstate 87, a highway located within the U.S. state of New York. Temporal and climate information, such as weekday, time of day, precipitation, visibility and temperature, were provided to the ANNs. Two different architectures were implemented to forecast vehicular traffic flow: Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM). The performance was evaluated using the Root Mean Square Error (RMSE) and comparing the predicted flow with the real data in the evaluated period. The MLP resulted in RMSE of 113.96, 185.98 and 201.19, and MAP of 9.56%, 15.49% and 16.95% for a forecasting interval of 1 week, 1 month and 1 year, respectively. Similarly, the LSTM network resulted in RMSE of 122.20, 179.55 and 203.63 and MAPE of 14.66%, 17.89% and 24.10% for the same time periods. Additionally, the two developed ANNs were evaluated to perform a forecasting vehicular traffic flow for another highway, the Interstate 88. The results from the two ANNs are similar, the MLP network resulted in MAPE of 17.15%, 25.65% and 19.76% for a forecasting interval of 1 week, 1 month and 1 year, respectively. On the other hand, the LSTM network presented RMSE of 19.14%, 31.87% and 22.97%. The results obtained come close to the similar work results when considered shorter prediction periods. The results for longer prediction time could not be compared to the literature due to the lack of work in this area, being a novelty of the current work. Moreover, the models implemented in this paper were precise and stable and can be used as strategy information to help road and cities planning. In addition, results have shown that the model can be replicated for other roads in the same region or either from other states or countries.