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Prediction method of shared bicycle traffic based on Prophet-BiLSTM combined model

Published: 30 March 2023 Publication History

Abstract

Shared bicycles provide citizens with a flexible travel mode, which not only reduces the generation of greenhouse gases, but also brings convenience and benefits, and is increasingly popular in urban transportation. At present, the biggest problem in the management of shared bicycles is excessive delivery, and how to accurately predict the number of them has become the key to standardized management. In order to solve the problem of traffic prediction accuracy of shared bicycles, a combined prediction model based on Prophet and Bidirectional long-short term memory (BiLSTM) network is proposed. First, the data set is preprocessed, then a prediction model based on Prophet and BiLSTM is established, and the two methods are combined with different weights using the least squares method, and the combined model is used as a new model for prediction. Taking the data obtained in the UCI machine learning library as an example, the data of shared bicycle rental is analyzed. Compared with the single-item forecasting model and three typical time series forecasting models, its forecasting performance is the best.

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    ICIT '22: Proceedings of the 2022 10th International Conference on Information Technology: IoT and Smart City
    December 2022
    385 pages
    ISBN:9781450397438
    DOI:10.1145/3582197
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    Published: 30 March 2023

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    Author Tags

    1. bidirectional long short-term memory neural network
    2. least squares method
    3. prophet
    4. time series model
    5. traffic forecast

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    ICIT 2022
    ICIT 2022: IoT and Smart City
    December 23 - 25, 2022
    Shanghai, China

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