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基于CNN和LSTM的空间时间表征和补偿方法,用于住宅旅行数据

Adi Alhudhaif¹, Kemal Polat²

¹Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.

PeerJ. Computer science

|June 10, 2024

概括

这项研究引入了一种使用卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 网络的新方法,以改善使用有限居民旅行数据的交通模拟. 该方法准确地模拟了时空特征,大大减少了约50%的模拟错误.

科学领域:

运输科学运输科学
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:

交通模拟通常需要大量的旅行行为数据,由于隐私问题和数据量,收集这些数据具有挑战性.
现有的方法很难用有限的数据集准确地建模居民的旅行模式.

研究的目的:

开发一种方法,使用有限的居民旅行数据进行更准确的交通模拟.
利用深度学习来分析和补偿旅行行为中的时空特征.

主要方法:

一个混合模型,将卷积神经网络 (CNN) 结合起来,用于空间特征提取和长短期记忆 (LSTM) 网络进行时间序列分析.
模拟时空旅行模式,以实现现实的交通模拟.

主要成果:

拟议的CNN-LSTM方法与调制和统计估计方法相比,表现优越.
实验结果显示,该模型对平均行驶距离的输出更接近现实数据.
该策略显著减少了模型偏差,基本错误率大约减少了50%.

结论:

CNN-LSTM方法有效地模拟了时空旅行数据,使得在有限的输入中实现更准确的交通模拟.
这种方法为克服交通模拟中的数据采集挑战提供了可行的解决方案.
这项研究强调了深度学习在提高运输建模现实性和准确性的潜力.

关键词:

在美国,CNN是CNN.分类分类分类分类.数据分析数据分析这是LSTM的LSTM.分析交通数据,分析交通数据.

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