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Smart Metro Station Systems : Data Science and Engineering

By: Chao Chen (Author) , Ch Central South University School of Traffic and Transportation Engineering and Vice-dean Hui (Author) , Yanfei Li (Author) , Ye Li (Author) , Zhu Duan (Author)

Extended Catalogue

Ksh 22,800.00

Format: Paperback or Softback

ISBN-10: 0323905889

ISBN-13: 9780323905886

Publisher: Elsevier - Health Sciences Division

Imprint: Elsevier - Health Sciences Division

Country of Manufacture: NL

Country of Publication: GB

Publication Date: Jan 24th, 2022

Publication Status: Active

Product extent: 316 Pages

Weight: 506.00 grams

Dimensions (height x width x thickness): 15.30 x 22.80 x 1.90 cms

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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.

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.


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