Urban Air Mobility

Urban Air Mobility
Intelligent, Safe and Sustainable Systems for Future Transportation

River Publishers Series in Transport Technology

Urban Air Mobility
Intelligent, Safe and Sustainable Systems for Future Transportation Forthcoming

Editors:
Vishnu Kumar Kaliappan, KPR Institute of Engineering and Technology, Tamil Nadu, India
Mohana Sundaram Kuppusamy, KPR Institute of Engineering and Technology, Tamil Nadu, India
Dugki Min, Konkuk University, Seoul, South Korea

ISBN: 9788770226783 e-ISBN: 9788770226776

Available: October 2024


This book is a resource for engineers and researchers to develop intelligent, safe, and sustainable systems for urban air mobility. In recent years, the growth of the world’s urban population has increased tremendously, and it is predicted that by 2040, 70% of the world population will be living in an urban setting. Existing ground transportation will be unable to cope with such an expansion, especially as congestion and over-crowding becomes more common. An answer may be found with the advent of recent technologies such as urban air mobility, which may play a vital role in providing solutions for public transportation.
The impact of modelling, analysis and application of intelligent algorithms is very much at the core of the design and implementation of Urban Air Mobility. The various chapters are configured to address the challenges in modelling, analysis, navigation, traffic control, battery efficiency, safety and security in terms of Artificial intelligence techniques.

Urban Air Mobility, DLN, DQN, Control systems, Unmanned Aerial Vehicle, Deep Learning models, PID, FOPID, Navigation control law, Traffic control law, power control system, energy efficient batteries, security mechanisms.

Chapter1: Introduction to Future Transportation
In this chapter a brief introduction about the existing transportation systems available are discussed in first half and the importance of moving towards future transportation with respect to sustainable ecosystem is discussed at second half. 

 Chapter 2: Modelling and Analysis of urban transportation systems
This chapter brings the insight into the modelling aspects in designing of control systems like PID, FOPID, aesthetics in the system. 
Chapter 3: System Dynamic Model of Urban Transportation System
In this chapter the system dynamic, population sub-model, economic sub-model modeling for urban transportation system is discussed in detail.
Chapter 4: Deep Learning Methods for High-Level Control Using Object Tracking
In this chapter the object detection algorithms using deep learning techniques for urban air mobility to track the objects are deeply discussed.
Chapter 5: Deep Learning approaches for Urban Air Mobility
In this chapter the application of Deep Learning approaches such as Reinforcement Learning Network, Convolution Neural Network, Deep Queuing Network, adopted for design of control algorithms. 
Chapter 6: Automated Electric Vertical Takeoff and Landing Decision Making
This chapter provides an analysis of how reinforcement learning could extend its applicability for the Electric vertical takeoff and landing for the effective aerial mobility system for the drone taxis towards detection, loading and shipping of passengers to the respective destinations. 
Chapter 7: Urban Aerial Mobility Concepts Modelling and Challenges
The chapter discusses the significance of UAM, its ideology, modelling methods and challenges of implementation.
Chapter 8: Navigation and traffic control systems for Urban Air Mobility
This chapter briefs about navigation and traffics control law like group coordination, Navigation, obstacle avoidance, fault identification, etc.  
Chapter 9: Challenges in charging of Batteries for Urban Air Mobility
In this chapter the various challenges faced using batteries for fast charging and durable operation is discussed. 
Chapter 10: Safety and Security challenges in implementing Urban Air Mobility
This chapter provides the safety aspects for the users and security challenges like network vulnerabilities, compromise the onboard systems and other catastrophic consequences for the passengers.