Smart Metro Station Systems

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