Abstract Freeway travel time prediction has become a focus of research in recent years. However, we must understand that most conventional methods are very instinctive. They rely on the small amount of real-time data from the day of travel to look for historical data with similar characteristics and then use the similar data to make predictions. This approach is only applicable for a single day and cannot be used to predict the travel time on a day in the future (such as looking up the travel time for the coming Sunday on a Monday). This study therefore developed a Hammerstein recurrent neural network based on genetic algorithms that learns the freeway travel time for different dates. The trained model can then be used to predict freeway travel time for a future date. The experiment results demonstrated the validity of the proposed approach.
Freeway Travel Time Prediction by Using the GA-Based Hammerstein Recurrent Neural Network
2017-10-18
8 pages
Article/Chapter (Book)
Electronic Resource
English
Transportation Research Record | 2002
|Travel-Time Prediction for Freeway Corridors
British Library Conference Proceedings | 1999
|British Library Conference Proceedings | 2002
|Travel-Time Prediction for Freeway Corridors
Online Contents | 1999
|Travel-Time Prediction for Freeway Corridors
Transportation Research Record | 1999
|