{"title":"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","authors":"Chung- Yu Shih, Chia-Ming Chang, Bo-Fan Wu, Chia-Hui Chang, Feng-Nan Hwang","doi":"10.1093/jom/ufad046","DOIUrl":null,"url":null,"abstract":"\n 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.","PeriodicalId":50136,"journal":{"name":"Journal of Mechanics","volume":"113 12","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-driven numerical simulation with extended Kalman filtering and long short-term memory networks for highway traffic flow prediction\",\"authors\":\"Chung- Yu Shih, Chia-Ming Chang, Bo-Fan Wu, Chia-Hui Chang, Feng-Nan Hwang\",\"doi\":\"10.1093/jom/ufad046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n 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.\",\"PeriodicalId\":50136,\"journal\":{\"name\":\"Journal of Mechanics\",\"volume\":\"113 12\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2023-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Mechanics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1093/jom/ufad046\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mechanics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1093/jom/ufad046","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MECHANICS","Score":null,"Total":0}
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.
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.