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.
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Acknowledgement
This work was supported in part by the Ministry of Science and Technology of Taiwan, R.O.C., under Contracts MOST 105-2119-M-035-002 and MOST 105-2634-E-035-001. Also, We are grateful to the National Center for High-performance Computing in Taiwan for computer time and facilities.
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Lee, RK., Yang, YC., Chen, JH., Chen, YC. (2018). Freeway Travel Time Prediction by Using the GA-Based Hammerstein Recurrent Neural Network. In: Lin, JW., Pan, JS., Chu, SC., Chen, CM. (eds) Genetic and Evolutionary Computing. ICGEC 2017. Advances in Intelligent Systems and Computing, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-10-6487-6_2
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DOI: https://doi.org/10.1007/978-981-10-6487-6_2
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