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智慧地铁车站系统：数据科学与工程（英文版）
作者：刘辉
出版社：中南大学出版社
出版时间：2022-03-01
ISBN：9787548747864
定价：¥168.00
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内容简介
地铁作为重大民生工程，因其运量大、快速准时、绿色低碳等优点，已成为国内外解决大城市交通问题的重要选择。随着“以人为本、绿色节能”等理念越发深入人心，下一代地铁系统尤其是地铁车站系统将面临来自多方面的全新挑战。例如，在乘客服务方面，将更加关注乘客出行的安全性、有序性和便捷性，提供更加人性化的服务；在车站环境方面，希望进一步提高舒适性，改善车站运行和服务性能；在能源管理方面，地铁系统需要更加适应节能减排战略，加强能源节约与资源优化。人工智能、大数据为代表的新一代信息技术将不断推动上述面临挑战的解决进程，让地铁车站系统越来越“智慧”，加快完成地铁车站系统的未来信息化技术变革。本书入选“十四五”时期国家重点出版物出版专项规划项目。本书以未来憧憬的“智慧地铁车站系统”为目标，对智慧地铁车站系统的人类、环境和能源3个关键层面开展大数据融合和应用，能应用于智慧地铁车站系统的客流引导、污染预警、运量预测、能效提升等多个最急需的场景。本书提供了完整的实际应用案例。本书不仅可作为人工智能、大数据、轨道交通等领域工程技术与科学研究人员的参考书，也能作为研究生、本科生和留学生等学生的学习用书。
作者简介
刘辉，现任中南大学二级教授、博导、交通院副院长。主要研究方向为轨道交通与人工智能。获中德双博士学位（交通运输工程/自动化工程）、德国教授文凭。入选国家万人计划青年拔尖人才、全球2%顶尖科学家榜单、爱思唯尔中国高被引学者。获国家科技进步奖一等奖（排15）、教育部自然科学奖二等奖（排1）、中国交通运输协会科技进步奖一等奖（排1）等；获施普林格-自然“中国新发展奖”、中国智能交通协会科技领军人才奖、中国交通运输协会首届青年奖、湖南省青年科技奖、宝钢优秀教师奖等。
目录
Chapter 1 Exordium
1.1 Overview of data science and engineering
1.2 Framework of smart metro station systems
1.3 Human and smart metro station systems
1.4 Environment and smart metro station systems
1.5 Energy and smart metro station systems
1.6 Scope of this book
References
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
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
References
Chapter 4 Clustering and anomaly detection of crowd hotspot regions
4.1 Introduction
4.2 Description of crowd GPS data
4.3 Preprocessing of crowd GPS data
4.4 Clustering of crowd hotspot regions based on K-means
4.5 Clustering of crowd hotspot regions based on DBSCAN
4.6 Anomaly detection of crowd hotspot regions based on Markov chain
4.7 Conclusions
References
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
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
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
References
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 transform methods
8.5 Feature extraction of metro load based on model
8.6 Conclusions
References
Chapter 9 Characteristic and correlation analysis of metro load
9.1 Introduction
9.2 The theoretical basis of correlation analysis
9.3 Description of metro load data
9.4 Correlation analysis of metro load and environment data
9.5 Correlation analysis of metro load and operation data
9.6 Comprehensive correlation ranking of metro load and related data
9.7 Conclusions
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
References
1.1 Overview of data science and engineering
1.2 Framework of smart metro station systems
1.3 Human and smart metro station systems
1.4 Environment and smart metro station systems
1.5 Energy and smart metro station systems
1.6 Scope of this book
References
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
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
References
Chapter 4 Clustering and anomaly detection of crowd hotspot regions
4.1 Introduction
4.2 Description of crowd GPS data
4.3 Preprocessing of crowd GPS data
4.4 Clustering of crowd hotspot regions based on K-means
4.5 Clustering of crowd hotspot regions based on DBSCAN
4.6 Anomaly detection of crowd hotspot regions based on Markov chain
4.7 Conclusions
References
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
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
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
References
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 transform methods
8.5 Feature extraction of metro load based on model
8.6 Conclusions
References
Chapter 9 Characteristic and correlation analysis of metro load
9.1 Introduction
9.2 The theoretical basis of correlation analysis
9.3 Description of metro load data
9.4 Correlation analysis of metro load and environment data
9.5 Correlation analysis of metro load and operation data
9.6 Comprehensive correlation ranking of metro load and related data
9.7 Conclusions
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
References
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