Travel Time Forecasting on a Freeway Corridor: a Dynamic Information Fusion Model based on the Machine Learning Approach

The metropolitan areas suffer more traffic, the change in travel time is very complex as it can be influenced by various factors, many of which are also unpredictable. Random forest was applied in the travel time prediction application to overcome the overfitting problem. Furthermore, the attention mechanism is implemented by developing the neural network to capture the inner relationship within the traffic data. The proposed long short memory neural network with attention mechanism method achieves its superior capability for TTP longer than 15 minutes (30 min to 60 min), overcoming the performance issue through long temporal dependency and memory blocks. To validate the accuracy and reliability of proposed models, the proposed approaches are tested using a freeway corridor in Charlotte, North Carolina, using the probe vehicle-based traffic data. Detailed information about the input variables and data preprocessing was presented. The results indicate that all proposed TTP models predicting in 15 minutes show better prediction performance over the other time horizons. A comparison with other prediction methods validates that the proposed hybrid LSTM and RF method can achieve a better prediction performance in accuracy and efficiency, proving its deployment is one of the successful solutions to critical, real-world transportation challenges.