Abstract
With respect to the prediction of short-term unoccupied parking space of parking guidance and information system (PGIS),a prediction method using genetic algorithm combined with recurrent neural network (RNN) is proposed. First, set the parameters of the RNN population, including the search space of the neural network’s hidden layers, neuron number, and neuron type. Then by setting the parameters of the genetic algorithm to drive and control the RNN training process, and using the RMSE value of the prediction result as the fitness function of the genetic algorithm to perform the individual evaluation index of the RNN. Finally, the RMSE values of the predicted results of all RNN individuals on the experimental dataset are compared through two different scenarios of prediction examples to obtain the best prediction model. The results of experiments show that this method has excellent prediction accuracy and wide applicability for the prediction of short-term and parking spaces in parking guidance information systems.
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Acknowledgment
This research is supported by the National Natural Science Foundation of China (61672259, 61602203), Key Projects of Jilin Province Science and Technology Development Plan (20180201064SF), and Outstanding Young Talent Foundation of Jilin Province (20170520064JH, 20180520020JH).
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Qiu, J., Tian, J., Chen, H., Lu, X. (2018). Prediction Method of Parking Space Based on Genetic Algorithm and RNN. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11164. Springer, Cham. https://doi.org/10.1007/978-3-030-00776-8_79
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DOI: https://doi.org/10.1007/978-3-030-00776-8_79
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