
Smart Metro Station Systems
Data Science and Engineering
- 1st Edition - January 4, 2022
- Imprint: Elsevier
- Authors: Hui Liu, Chao Chen, Yanfei Li, Zhu Duan, Ye Li
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 5 8 8 - 6
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 7 1 2 - 5
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… Read more
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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.
- Presents relevant core technologies of data science and engineering in smart metro station systems
- Describes systems based on holographic perception, terminal platform control and highly-autonomous operation
- Gives a large number of practical case studies and experimental designs
- Introduces state-of-the-art machine learning and data mining methods for smart metro station systems
- Offers a comprehensive, up-to-date research solution for the emerging area of smart metro stations
Chapter 1 Introduction
1.1 Overview of data science and engineering
1.2 Framework of smart metro station systems
1.3 Environment for smart metro station systems
1.4 Human for smart metro station systems
1.5 Energy for smart metro station systems
1.6 Scope of this book
1.7 References
Part I Human and smart metro station systems
Chapter 2 Metro traffic flow monitoring and passenger guidance
2.1 Introduction
2.2 Description of metro traffic flow data
2.3 Prediction of metro traffic flow based on Elman neural network
2.4 Prediction of metro traffic flow based on deep echo state network
2.5 Passenger guidance strategy based on prediction results
2.6 Conclusions
2.7 References
Chapter 3 Individual behavior analysis and trajectory prediction
3.1 Introduction
3.2 Description of individual GPS data
3.3 Preprocessing of individual GPS data
3.4 Prediction of GPS trajectory based on optimized extreme learning machine
3.5 Prediction of GPS trajectory based on optimized support vector machine
3.6 Analysis of individual behavior based on prediction results
3.7 Conclusions
3.8 References
Chapter 4 Clustering and anomaly detection of crowd hotspot regions
4.1 Introduction
4.2 Description of crowd GPS data
4.3 Clustering of crowd hotspot regions based on K-means
4.4 Clustering of crowd hotspot regions based on DBSCAN
4.5 Anomaly detection of crowd hotspot regions based on Markov chain
4.6 Conclusions
4.7 References
Part II Environment and smart metro station systems
Chapter 5 Monitoring and deterministic prediction of station humidity
5.1 Introduction
5.2 Description of station humidity data
5.3 Deterministic prediction of station humidity based on optimization ensemble
5.4 Deterministic prediction of station humidity based on stacking ensemble
5.5 Evaluation of deterministic prediction results
5.6 Conclusions
5.7 References
Chapter 6 Monitoring and probabilistic prediction of station temperature
6.1 Introduction
6.2 Description of station temperature data
6.3 Interval prediction of station temperature based on quantile regression
6.4 Interval prediction of station temperature based on kernel density estimation
6.5 Evaluation of probabilistic prediction results
6.6 Conclusions
6.7 References
Chapter 7 Monitoring and spatial prediction of multi-dimensional air pollutants
7.1 Introduction
7.2 Description of multi-dimensional air pollutants data
7.3 Dimensionality reduction of multi-dimensional air pollutants data
7.4 Spatial prediction of air pollutants based on long short-term memory
7.5 Evaluation of spatial prediction results
7.6 Conclusions
7.7 References
Part III Energy and smart metro station systems
Chapter 8 Time series feature extraction and analysis of metro load
8.1 Introduction
8.2 Description of metro load data
8.3 Feature extraction of metro load based on statistical methods
8.4 Feature extraction of metro load based on transformation
8.5 Feature extraction of metro load based on model
8.6 Conclusions
8.7 References
Chapter 9 Characteristic and correlation analysis of metro load
9.1 Introduction
9.2 Description of metro load and related data
9.3 Characteristic analysis of metro load data
9.4 Correlation analysis of metro load and temperature
9.5 Correlation analysis of metro load and passenger flow
9.6 Comprehensive correlation ranking of metro load and related data
9.7 Conclusions
9.8 References
Chapter 10 Metro load prediction and intelligent ventilation control
10.1 Introduction
10.2 Description of short-term and long-term metro load data
10.3 Short-term prediction of metro load data based on ANFIS model
10.4 Long-term prediction of metro load data based on SARIMA model
10.5 Performance evaluation of prediction results
10.6 Intelligent ventilation control based on prediction results
10.7 Conclusions
10.8 References
- Edition: 1
- Published: January 4, 2022
- Imprint: Elsevier
- Language: English
HL
Hui Liu
CC
Chao Chen
YL
Yanfei Li
ZD
Zhu Duan
YL