The development of smart cities is one of the most important challenges over the next few decades. Governments and companies are leveraging billions of dollars in public and private funds for smart cities. Next generation smart cities are heavily dependent on distributed smart sensing systems and devices to monitor the urban infrastructure. The smart sensor networks serve as autonomous intelligent nodes to measure a variety of physical or environmental parameters. They should react in time, establish automated control, and collect information for intelligent decision-making. In this context, one of the major tasks is to develop advanced frameworks for the interpretation of the huge amount of information provided by the emerging testing and monitoring systems.
Data Analytics for Smart Cities brings together some of the most exciting new developments in the area of integrating advanced data analytics systems into smart cities along with complementary technological paradigms such as cloud computing and Internet of Things (IoT). The book serves as a reference for researchers and engineers in domains of advanced computation, optimization, and data mining for smart civil infrastructure condition assessment, dynamic visualization, intelligent transportation systems (ITS), cyber-physical systems, and smart construction technologies. The chapters are presented in a hands-on manner to facilitate researchers in tackling applications.
Arguably, data analytics technologies play a key role in tackling the challenge of creating smart cities. Data analytics applications involve collecting, integrating, and preparing time- and space-dependent data produced by sensors, complex engineered systems, and physical assets, followed by developing and testing analytical models to verify the accuracy of results. This book covers this multidisciplinary field and examines multiple paradigms such as machine learning, pattern recognition, statistics, intelligent databases, knowledge acquisition, data visualization, high performance computing, and expert systems. The book explores new territory by discussing the cutting-edge concept of Big Data analytics for interpreting massive amounts of data in smart city applications.
Table of Contents:
Preface
Editors
Contributors
1 Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment
Amir H. Alavi and William G. Buttlar
2 Global Satellite Observations for Smart Cities
Zhong Liu, Menglin S. Jin, Jacqueline Liu, Angela Li, William Teng, Bruce Vollmer, and David Meyer
3 Advancing Smart and Resilient Cities with Big Spatial Disaster Data: Challenges, Progress, and Opportunities
Xuan Hu and Jie Gong
4 Smart City Portrayal: Dynamic Visualization Applied to the Analysis of Underground Metro
Evgheni Polisciuc and Penousal Machado
5 Smart Bike-Sharing Systems for Smart Cities
Hesham A. Rakha, Mohammed Elhenawy, Huthaifa I. Ashqar, Mohammed H. Almannaa, and Ahmed Ghanem
6 Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal Processing
Abdollah Malekjafarian, Eugene J. OBrien, and Fatemeh Golpayegani
7 Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart Cities
Arash Jahangiri, Sahar Ghanipoor Machiani, and Vahid Balali
8 Exploratory Analysis of Run-Off-Road Crash Patterns
Mohammad Jalayer, Huaguo Zhou, and Subasish Das
9 Predicting Traffic Safety Risk Factors Using an Ensemble Classifier
Nasim Arbabzadeh, Mohammad Jalayer, and Mohsen Jafari
10 Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public Houses
Clyde Zhengdao Li, Bo Yu, Cheng Fan, and Jingke Hong
Index.
