利用扩展卡尔曼滤波和长短期记忆网络进行数据驱动数值模拟,预测高速公路交通流量

IF 1.5 4区 工程技术 Q3 MECHANICS
Chung- Yu Shih, Chia-Ming Chang, Bo-Fan Wu, Chia-Hui Chang, Feng-Nan Hwang
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引用次数: 0

摘要

为交通流量预测开发准确可靠的计算工具一直是交通工程和规划领域活跃的研究课题。现有的预测工具一般分为参数法、非参数法和基于 PDE 的方法。其中,机器学习方法(如长短期记忆(LSTM)网络)属于非参数方法。本研究提出了利用 LSTM 预测高速公路交通流量的数据同化技术。所提出的方法是在扩展卡尔曼滤波器(EKF)算法框架下开发的,该算法由两个关键部分组成:分析和预测步骤。数值模拟器是预测工具的内核部分,我们使用显式(EX)戈杜诺夫方案来离散化 Lighthill-Whitham- Richards 模型,其中 MacNicholas 公式被用作速度和密度之间的基本关系。EKF 从两个角度结合了 LSTM 预测。在实际应用中,上游或下游边界点的未来数据不可用。因此,可以利用 LSTM 生成的预测值来设置边界条件。此外,EKF 的两个阶段将 LSTM 预测值(称为伪观测值)与观测数据同化,并将数值模拟获得的背景值与观测数据(只要有)依次同化。这一同化过程旨在为后续预测获得更好的初始条件,从而提高预测精度。基于台湾雪山隧道高速公路历史交通数据的数值结果表明,我们的方法可以有效减少观测误差,并优于三种基准方法:EX、EKF 和 LSTM。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven numerical simulation with extended Kalman filtering and long short-term memory networks for highway traffic flow prediction
Developing an accurate and reliable computational tool for traffic flow prediction has always been an active research topic in transportation engineering and planning. The available predictive tools generally fall into parametric, nonparametric, and PDE-based approaches. In particular, the machine learning methods, such as the long short-term memory (LSTM) networks, belong to the nonparametric methods. This study proposes the data assimilation technique with LSTM for predicting highway traffic flows. The proposed method is developed under the framework of the extended Kalman filter (EKF) algorithm, which consists of two key components: the analysis and prediction steps. As the numerical simulator, a kernel component of the predictive tool, we use an explicit (EX) Godunov's scheme to discretize the Lighthill-Whitham- Richards model, where the MacNicholas formulation is used as the fundamental relation between the velocity and density. EKF combines LSTM prediction from two perspectives. In practical scenarios, future data at the upstream or downstream boundary points are unavailable. Therefore, the predicted values generated by LSTM are employed to set boundary conditions. Furthermore, two stages in EKF assimilate the LSTM predicted values, known as pseudo-observations, and the observed data in order with background values obtained through numerical simulation and observed data whenever available. This assimilation process aims to obtain a better initial condition for subsequent predictions, resulting in improved accuracy. Based on traffic data for the historical Hsuehshan Tunnel highway traffic data in Taiwan, the numerical results demonstrate that our method can effectively reduce the observation error and outperforms three baselines: EX, EKF, and LSTM.
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来源期刊
Journal of Mechanics
Journal of Mechanics 物理-力学
CiteScore
3.20
自引率
11.80%
发文量
20
审稿时长
6 months
期刊介绍: The objective of the Journal of Mechanics is to provide an international forum to foster exchange of ideas among mechanics communities in different parts of world. The Journal of Mechanics publishes original research in all fields of theoretical and applied mechanics. The Journal especially welcomes papers that are related to recent technological advances. The contributions, which may be analytical, experimental or numerical, should be of significance to the progress of mechanics. Papers which are merely illustrations of established principles and procedures will generally not be accepted. Reports that are of technical interest are published as short articles. Review articles are published only by invitation.
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