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混合式停车位预测模型:集成ARIMA,长短期内存 (LSTM) 和反向传播神经网络 (BPNN) 来实现智慧城市发展

Anchal Dahiya¹, Pooja Mittal¹, Yogesh Kumar Sharma²

¹Department of Computer Science & Applications, MDU, Rohtak, Haryana, India.

PeerJ. Computer science

|February 3, 2025

概括

结合ARIMA和LSTM与BPNN的新混合型号显著提高了智能城市的停车位预测准确度. 这种方法减少了错误,为城市交通和停车挑战提供了可扩展的解决方案.

科学领域:

智慧城市技术智慧城市技术
人工智能的人工智能
数据科学数据科学数据科学

背景情况:

城市停车场短缺和交通拥堵是智能城市面临的主要挑战.
准确的停车位预测对于高效的城市流动性至关重要.

研究的目的:

提出一种新的,可扩展的混合模型,用于准确预测停车位.
通过整合多个AI模型来提高预测准确性.

主要方法:

一个双相混合模型,集成线性数据的自动回归集成移动平均 (ARIMA) 和非线性数据的长短期内存 (LSTM).
逆向传播神经网络 (BPNN) 在第二阶段用于最大限度地减少预测错误.
利用墨尔本和哈佛的物联网数据集,进行预处理以确保数据质量.

主要成果:

在墨尔本数据集上实现了低误差指标:MSE为0.32,MAE为0.48,RMSE为0.56.
在哈佛数据集上,与类似的低误差指标 (MSE:0.31,MAE:0.47,RMSE:0.56) 验证的概括性.
在两个数据集的预测准确性方面,超越了现有的模型.

结论:

拟议的混合模型为停车位预测提供了强大而准确的解决方案.
这项技术可以为可持续,经济的智慧城市做出重大贡献.
改进的停车预测提高了城市生活质量,减少了交通拥堵.

关键词:

在阿里马,阿里马就是阿里马.深度学习是一种深度学习.这就是为什么物联网物联网物联网.这是LSTM的LSTM.停车位停车位的空间.智慧城市是智慧城市.

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