Smart Metro Station Systems
Data Science and Engineering
Published on 24. January 2022
Book
Paperback/Softback
316 pages
978-0-323-90588-6 (ISBN)
Description
Smart Metro Station Systems: Data Science and Engineering introduces key technologies in data science and engineering for smart metro station systems. The book consists of three main parts, focusing on the environment, people and energy. Each chapter includes practical applications, along with information on metro traffic flow monitoring and passenger guidance, methods for behavior analysis and trajectory projection, clustering and anomaly detection in crowd hotspots, monitoring and prediction for station humidity, monitoring and spatial prediction for air pollutants, time series feature extraction and analysis of metro load, characteristic and correlation analysis of metro load, and prediction and intelligent ventilation control.
This volume offers a key reference on the emerging area of smart metro stations and will be useful to those working on smart railways, data science, engineering, artificial intelligence and aligned fields.
This volume offers a key reference on the emerging area of smart metro stations and will be useful to those working on smart railways, data science, engineering, artificial intelligence and aligned fields.
More details
Language
English
Place of publication
Philadelphia
United States
Target group
Professional and scholarly
Illustrations
270 illustrations (20 in full color); Illustrations
Dimensions
Height: 229 mm
Width: 152 mm
Weight
450 gr
ISBN-13
978-0-323-90588-6 (9780323905886)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
01/2022
Elsevier
€109.00
Available for download
Persons
Hui Liu is a Professor of Robotics and Artificial Intelligence, at Central South University, China, as well as a Vice Dean of the Faculty of Traffic and Transportation Engineering, and Director of the Institute of Artificial Intelligence and Robotics (IAIR). He received a Ph.D. in Automation Engineering, and his habilitation, from Rostock University in Germany. He has published more than 70 papers, and holds over 50 patents. Chao Chen researches Data Science and Artificial Intelligence, at Central South University, China. He has published over twenty papers, and has applications pending for eight national patents in the area of smart metro. Prof. Yanfei Li is Associate Professor, Shennong Chair Professor, Director of Smart Agriculture, College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha, China. Prof. Li published 45 SCI/EI papers, including 27 JCR Q1 journal papers, 2 ESI 1% highly-cited papers. She authorized 10 national invention patents as the first inventor and published one monograph in China communications press. She leaded several important research projects, including China National Key Research and Development Project, Human Natural Science Foundation Project, etc. She won the second prize of the Natural Science Award of Ministry of Education in 2017, the first prize of the Hunan New Energy Science and Technology Progress Award in 2020, the Logistics Technology Innovation Award of China Federation of Logistics and Purchasing in 2021 and the Transportation and Logistics Innovation Award of China Highway and Transportation Society in 2020, etc. Prof. Li is an expert in the smart agriculture and transportation systems. Zhu Duan researches Smart Railway and Engineering, at Central South University, China. He has published over twenty papers, as well as holding 10 national patents on railway engineering. Dr. Ye Li is a Scientist, Leader of Junior Research Group of Smart Robotics, School of Traffic and Transportation Engineering, Central South University, Changsha, China. Dr. Li published 16 journal and conference papers and authorized 4 China invention patents. He leaded and participated in several important research projects, including Operational management decision support tool for bridges subjected to extreme wind funded by Highway England, Short-term multi-step prediction and spatial correlation of PM2.5 concentrations for smart metro station funded by NSFC of China. Dr. Li is a young expert in the field of smart metro station.
Author
Professor, Director of Institute of Artificial Intelligence and Robotics (IAIR), and Vice-dean, School of Traffic and Transportation Engineering, Central South University, Changsha, China
Scientist, Leader of Junior Research Group of Smart Environment, School of Traffic and Transportation Engineering, Central South University, Changsha, China
Associate Professor, Director of Smart Agriculture, College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha, China
Scientist, Leader of Junior Research Group of Smart Transportation, School of Traffic and Transportation Engineering, Central South University, Changsha, China
Scientist, Leader of Junior Research Group of Smart Robotics, School of Traffic and Transportation Engineering, Central South University, Changsha, China
Content
1. Introduction
Part I Human and smart metro station systems
2. Metro traffic flow monitoring and passenger guidance
3. Individual behavior analysis and trajectory prediction
4. Clustering and anomaly detection of crowd hotspot regions
Part II Environment and smart metro station systems
5. Monitoring and deterministic prediction of station humidity
6. Monitoring and probabilistic prediction of station temperature
7. Monitoring and spatial prediction of multi-dimensional air pollutants
Part III Energy and smart metro station systems
8. Time series feature extraction and analysis of metro load
9. Characteristic and correlation analysis of metro load
10. Metro load prediction and intelligent ventilation control
Part I Human and smart metro station systems
2. Metro traffic flow monitoring and passenger guidance
3. Individual behavior analysis and trajectory prediction
4. Clustering and anomaly detection of crowd hotspot regions
Part II Environment and smart metro station systems
5. Monitoring and deterministic prediction of station humidity
6. Monitoring and probabilistic prediction of station temperature
7. Monitoring and spatial prediction of multi-dimensional air pollutants
Part III Energy and smart metro station systems
8. Time series feature extraction and analysis of metro load
9. Characteristic and correlation analysis of metro load
10. Metro load prediction and intelligent ventilation control