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Explores the future of transportation and provides a comprehensive guide to leveraging cutting-edge digital technologies and AI-powered platforms for creating smart, energy-efficient, and sustainable urban transportation systems.
As urbanization accelerates globally, transportation has become a major contributor to environmental degradation and climate change. Rising greenhouse gas (GHG) emissions—including carbon dioxide (CO2), methane, ozone, nitrous oxide, and chlorofluorocarbons—pose a serious threat to air quality and environmental sustainability. To counteract these challenges, nations advocate smart, eco-friendly urban mobility solutions. This book presents the latest advancements and transformative trends in urban transportation, emphasizing emerging digital technologies that foster sustainability. The integration of artificial intelligence, 5G and 6G, cybersecurity, the Internet of Things, blockchain, edge computing, and cloud-native infrastructures enhances intelligent and energy-efficient transportation systems. Experts and environmental advocates champion innovative software platforms and solutions essential for modernizing mobility. This book examines the foundational technologies driving this transformation and explores AI-powered platforms and management solutions shaping the future of urban transportation, making it an essential resource for beginners and seasoned professionals alike.
Uncovers the innovative features of artificial intelligence in urban transportation, illustrating how integrated platforms enhance operational efficiency and sustainability at both macro and micro levels;
Delves into the most common AI techniques and algorithms used in modern urban mobility systems;
Focuses on how the evolution of AI paradigms supports real-time decision-making, transforming urban transportation planning and management;
Examines the integration of trust management and advanced cybersecurity measures within AI-powered transportation systems;
Provides a collection of case studies and detailed analyses of AI-based integrated platforms, offering theoretical perspectives and practical examples of technological advancements and their challenges.
Contents
Preface xix
Part 1: Artificial Intelligence in Solving Urban Planning and Designing Challenges 1
1 Illustrating the Sustainability, Challenges, and Concerns of Urban Mobility and Smart Cities 3
Nilesh Bhosle, Amandeep Kaur, Raman Kumar, Yashwant Singh Bisht and Laith H. Alzubaidi
1.1 Introduction 4
1.1.1 Characteristics of a Smart City 5
1.2 Smart City 6
1.2.1 An Overview of Smart Cities 6
1.2.2 Role of Digitalisation in Smart Cities 6
1.2.3 Infrastructural Impacts of Digitalisation in Smart Cities 9
1.3 Smart Mobility in Smart Cities 10
1.4 Analysis of Security Threats 13
1.4.1 Mobility Trends in Smart Cities in the Future 14
1.5 Issues and Opportunities Related to Smart Cities 15
1.5.1 Challenges for Smart Cities 15
1.5.2 Trends and Opportunities for the Future 17
1.6 Conclusions 17
References 18
2 Accentuating Climate Change Adaptation and Vulnerability (CCAV) Challenges 23
Adil Abbas Alwan, Amandeep Kaur, Nilesh Bhosle, Sanjeev Kumar Shah and Mohemmed Hussien
2.1 Introduction 24
2.1.1 Adapting to Climate Change Vulnerabilities 25
2.2 Related Work 26
2.2.1 Spatial Violence 26
2.2.2 Response to Climate Change 27
2.3 Key Challenges in Climate Change Adaptation and Vulnerability (CCAV) 28
2.3.1 Technical Challenges 28
2.3.2 Financial Constraints 28
2.3.3 Social and Cultural Barriers 28
2.3.4 Institutional and Governance Challenges 29
2.3.5 Multi-Level Governance (MLG) of Climate Change 29
2.4 Case Studies Highlighting Vulnerability and Adaptation Challenges 30
2.4.1 Small Island Developing States (SIDS) 30
2.4.2 Rural Farming Communities in Sub-Saharan Africa 30
2.4.3 Urban Slums in South Asia 31
2.5 Strategic Frameworks for Addressing CCAV Challenges 31
2.5.1 Through Community-Based Approaches 31
2.5.2 Mobilising Climate Finance and Reducing Funding Barriers 31
2.5.3 Strengthening Institutional Capacity and Governance Frameworks 32
2.5.4 Innovating and Leveraging Technology 32
2.5.5 Insufficient Funding and Resources 32
2.5.6 Data Gaps and Uncertainty 32
2.5.7 Insufficient Localised Solutions 33
2.5.8 Institutional and Policy Challenges 33
2.5.9 Social and Economic Inequities 33
2.5.10 Awareness and Engagement of the Public Lacking 33
2.5.11 Using Fossil Fuels as a Source of Energy 33
2.5.12 Limitations 34
2.5.13 Maintaining a Balance Between Short-Term and Long-Term Needs 34
2.5.14 Adaptation Challenges Based on Ecosystems 34
2.5.15 Efforts to Monitor and Evaluate Adaptation 34
2.5.16 Global Coordination and Climate Justice 34
2.6 Conclusion 35
References 35
3 Delineating the Solution Approaches for Sustainable Urban Mobility 39
Adil Abbas Alwan, Amandeep Kaur, Nilesh Bhosle, Rajesh Singh and Mohammed Al-Farouni
3.1 Introduction 40
3.2 Related Work 42
3.3 Materials and Methods 44
3.3.1 Travel Demand Generation 45
3.3.2 Traffic Simulation Process 46
3.4 Results Analysis and Discussion 47
3.4.1 Amsterdam 47
3.4.2 Helsinki 49
3.5 Conclusion 51
References 51
4 About the Growing Power of Artificial Intelligence (AI) and Blockchain for Fleet Management and Sustainable Societies 55
Jayant Jagtap, Raman Kumar, Kunal Gagneja, Anita Gehlot and M. Muhsen Hassan
4.1 Introduction 56
4.1.1 Artificial Intelligence and Blockchain 57
4.1.2 Sustainable Smart City Society 59
4.2 Literature Survey and Contribution 60
4.2.1 Privacy and Security Concerns 60
4.3 Blockchain to Support Smart Cities' Operations 62
4.4 Blockchain Benefits 63
4.5 Types of Blockchain Networks 64
4.6 Blockchain Suitability 65
4.7 Conclusion 66
References 67
5 Testifying the Criticality of the Internet of Things (IoT), 5G and AI: A Perfect Combination for Battery Management 71
Preeti Rani, Raman Kumar, Amrita Singh, Jayant Jagtap and Muntather Almusawi
5.1 Introduction 72
5.1.1 Energy Management Strategy Description 74
5.2 Literature Review 74
5.2.1 Managing an EV Battery Pack 75
5.2.2 IoT in Battery Management 75
5.2.3 Wireless BMS Incentive Program 75
5.2.4 5G as a Catalyst for Rapid Data Transmission 76
5.2.5 AI and Predictive Analytics in Battery Optimization 76
5.2.6 Synergy of IoT, 5G, and AI in Battery Management 76
5.3 The Internet of Things (IoT) in Battery Management 77
5.3.1 Real-Time Monitoring and Predictive Maintenance 77
5.3.2 Data Collection and Data-Driven Insights 77
5.4 5G Connectivity: Enabling High-Speed, Low-Latency Data Exchange 77
5.4.1 Enhancing Real-Time Decision Making 78
5.4.2 Scalability of IoT Networks 78
5.5 Artificial Intelligence (AI): The Brain Behind Smart Battery Management 78
5.6 BMS's Goals and Challenges 81
5.6.1 Optimal Charging 82
5.6.2 Fast Characterization 83
5.7 Conclusion 83
References 84
6 Using Local Knowledge and Sustainable Transport for Greener Mobility 89
Jayant Jagtap, Amrita Singh, Sandeep Singh, Shivani Pant and Haider Mohammed Abbas
6.1 Introduction 90
6.2 Related Work 92
6.3 Greening Mobility Necessities 94
6.3.1 Green Transport Standards 94
6.4 Principles of the Sustainable Mobility Paradigm 97
6.5 Conclusion 100
References 100
Part 2: Green Revolution in IoV 105
7 Expounding the Importance of Explainable AI for Greener Transportations 107
Abhilasha Jadhav, Amrita Singh, Adil Abbas Alwan, Ruby Pant and Haider Alabdeli
7.1 Introduction 108
7.2 Related Work 110
7.2.1 Why Explainable AI is Needed? 111
7.2.2 Evaluation of Explainable-AI (XAI) Frameworks and Results 113
7.3 The Need for Explainable AI in Transportation 115
7.4 AI's Potential for Transforming Smart Cities and its Limitations 116
7.5 Explainable AI Supports Greener Transportation 117
7.5.1 Optimizing Traffic Flow and Reducing Emissions 117
7.5.2 Managing and Reducing Fleet Emissions 117
7.5.3 Enhancing Predictive Maintenance 118
7.5.4 Supporting Autonomous Vehicles and Green Routing 118
7.5.5 Facilitating Transparent Data Sharing 118
7.6 Benefits of Explainable AI in Greener Transportation 118
7.7 Challenges of Implementing Explainable AI in Greener Transportation 119
7.8 Conclusion 120
References 120
8 Demystifying the Aspects of Edge Computing and Edge AI for Real-Time Insights 127
Abhilasha Jadhav, Heena Madan, Mohammed Y. Al-khuzaie, Ruby Pant, Nidhi Singh and Hassan M. Al-Jawahry
8.1 Introduction 128
8.1.1 Importance of Real-Time Processing in AI 129
8.1.2 A Paradigm for Edge Computing 131
8.1.3 Mobile Edge Computing (MEC) 132
8.1.3.1 Understanding Edge Computing 132
8.1.3.2 The Architecture of Edge Computing 132
8.1.4 Advantages of Edge Computing 133
8.2 Edge AI 134
8.2.1 Decision-Making in Real-Time: Why it's Important 135
8.2.2 Purpose and Scope of the Paper 135
8.3 Application of Edge AI in a Variety of Industries 137
8.3.1 Manufacturing 137
8.4 Edge AI Challenges and Limitations 138
8.4.1 Challenges in Technology 138
8.5 Future Directions and Trends 140
8.5.1 Federated Learning on the Edge 140
8.5.2 5G and Edge Synergy 140
8.5.3 TinyML for Edge AI 140
8.5.4 Integration with Blockchain for Security 140
8.6 Conclusion 140
References 141
9 Elucidating the Strategic Significance of Smart Grids Towards Sustainable Cities 145
Abhilasha Jadhav, Heena Madan, Mohammed Y. Al-khuzaie, Sanjeev Kumar Shah and Mohammed I. Habelalmateen
9.1 Introduction 146
9.2 Related Work 149
9.3 Smart Grids as a Catalyst for Sustainability in Urban Environments 154
9.4 Smart Grid Technologies: Enabling Real-Time Decision Making 155
9.5 Challenges in Implementing Smart Grids for Sustainable Cities 156
9.6 Case Studies: Smart Grid Implementation in Sustainable Cities 156
9.7 Conclusion 157
References 157
10 Describing the Needs for Connected Electric Vehicles for Better Air Quality 161
Shivakrishna Dasi, Jasgurpreet Singh Chohan, Saroj Kumar Gupta, Rajesh Singh and Myasar Mundher Adnan
10.1 Introduction 162
10.2 Related Work 164
10.2.1 Battery Electric Vehicles 165
10.3 Performance Aspects of CAEVs 166
10.3.1 Autonomous Vehicles 167
10.3.2 Connected Vehicles 168
10.3.3 Electric Vehicles 169
10.4 The Impact of Air Quality on Environmental Justice (EJ) 170
10.4.1 Data Collection and Setup of Air Quality Modeling Systems 170
10.5 CAV Taxonomy Based on Performance 170
10.5.1 Connected and Autonomous Electric Vehicles (CAEVs) 171
10.5.2 The Quality of Experience Framework for CAEVs 172
10.6 Conclusion 173
References 173
11 Distilling the Convergence of AI and EVs Towards Self-Driving EVs 177
Shivakrishna Dasi, Heena Madan, Mohammed Y. Al-khuzaie, Anita Gehlot and Ramy Riad Al-Fatlawy
11.1 Introduction 178
11.1.1 The State of Electric Vehicles (EVs) Today 181
11.2 Related Work 181
11.2.1 AI as the Backbone of Self-Driving Technology 182
11.2.2 Machine Learning and Computer Vision 182
11.2.3 Deep Reinforcement Learning 182
11.3 The Convergence of AI and EVs: Key Enablers for Self-Driving EVs 184
11.3.1 Technical Challenges in the Path Towards Self-Driving EVs 185
11.4 The Impact of Self-Driving EVs on Society and the Environment 186
11.5 The Impact of Self-Driving Vehicles on the Environment 189
11.6 Conclusion 190
References 191
12 Explaining the Distinct Functionalities of Battery Management Systems (BMS) 197
Hawraa Ali Sabah, Shivakrishna Dasi, Jaspreet Kaur, Devendra Singh and Ahmad Radee Alawadi
12.1 Introduction 198
12.2 Battery Management System (BMS) 199
12.3 An Overview of Components and Topologies 202
12.3.1 Software Architecture 203
12.3.2 Functionalities 204
12.4 Battery Models 205
12.4.1 Thermal Modeling 205
12.4.2 Electrical Modeling 207
12.5 Monitoring the Stack 208
12.5.1 Batteries for Grid Storage 209
12.5.2 A Modeling Approach to Lithium-Ion Batteries 209
12.5.3 Advanced Model-Based BMSs 210
12.6 State of Charge Estimation 210
12.6.1 The Need for BMS in Smart Grids and EVs 211
12.6.2 Challenges of BMS and Possible Solutions 211
12.7 Conclusion 211
References 212
13 Detailing How AI Empowers Battery Management Systems 215
Umesh Chandra Garjola, Ashish Singh, Hawraa Ali Sabah, Jaspreet Kaur and Zaid Alsalami
13.1 Introduction 216
13.2 Systems for Managing Batteries 218
13.2.1 Structure of Elements and Arrangements 219
13.2.2 Structure of Battery-Management System 221
13.2.3 System Functions to Manage Batteries 221
13.2.4 Impacts of Battery-Management Systems 222
13.2.5 A Study of How AI Can Be Applied to Smart Grids and Renewable Energy 222
13.3 Traditionally, BMS Has Faced Many Challenges 224
13.4 AI in Business Management Systems 225
13.4.1 Calculation of State of Charge (SoC) and State of Health (SoH) 225
13.4.2 Balancing and Controlling the Temperature of Cells 225
13.4.3 Predicting and Diagnosing Faults 225
13.4.4 Optimizing Energy Efficiency and Extending the Range 226
13.5 BMS Powered by Artificial Intelligence 226
13.5.1 Machine Learning (ML) and Deep Learning (DL) 226
13.5.2 Reinforcement Learning (RL) 226
13.5.3 An Algorithm for Detecting Anomalies 227
13.5.4 Digital Twins 227
13.6 BMS with AI Enhancements: Benefits 227
13.6.1 BMS Integration with AI Offers Numerous Benefits 227
13.6.2 Future Trends and Challenges 227
13.6.3 Future Prospects 228
13.7 Conclusion 228
References 228
Part 3: Infrastructure Optimization in EV 233
14 Insisting for Electric Vehicle (EV) Charging Infrastructure Management Systems 235
Jasgurpreet Singh Chohan, Ashish Singh, Jaspreet Kaur, Ruby Pant and Kassem AL-Attabi
14.1 Introduction 236
14.2 The Need for EV Charging Infrastructure Management Systems 238
14.2.1 User Demand for Convenience 239
14.2.2 Utility and Energy Load Management 239
14.2.3 Integration with Renewable Energy Sources 239
14.3 Overview of the Charging Infrastructure for Electric Vehicles 239
14.3.1 Equipment Specifications for Electric Vehicles 239
14.3.2 Standards for Interoperable EV Charging 241
14.4 Model Overview 242
14.4.1 Vehicle Fleet 243
14.4.2 Deployment of Electric Vehicle Charging Infrastructure 244
14.5 Hotspot-Based EVCS 246
14.6 Key Features of EV Charging Infrastructure Management Systems 246
14.6.1 Smart Charging and Load Balancing 246
14.6.2 Data Collection and Predictive Maintenance 247
14.6.3 Dynamic Pricing and User Management 247
14.6.4 Integration with Mobile Applications 247
14.6.5 Grid Interaction and Energy Storage 247
14.6.6 Scalability and Flexibility 247
14.7 Challenges in Implementing EV Charging Infrastructure Management Systems 248
14.7.1 High Initial Investment Costs 248
14.7.2 Data Security and Privacy 248
14.7.3 Interoperability and Standardization 248
14.7.4 Grid Reliability and Capacity 248
14.8 Future Directions and Innovations in EV Charging Infrastructure Management 248
14.8.1 AI and Machine Learning for Predictive Optimization 249
14.8.2 Blockchain for Secure Transactions 249
14.8.3 Ultra-Fast and Wireless Charging 249
14.9 Conclusion 249
References 250
15 Illuminating the AIs Role in Shaping Up EV Charging Infrastructures 253
Jatinder Kumar, Ashish Singh, Hawraa Ali Sabah, Yashwant Singh Bisht and Laith H. Jasim
15.1 Electro Mobility Charging Systems 254
15.2 Literature Review 256
15.3 Electric Vehicle Charging Infrastructure 257
15.3.1 Infrastructural Types of Charging 258
15.4 Optimizing the Charging Infrastructure Using Artificial Intelligence 259
15.4.1 Predicting Charging Demand with Data Analytics 260
15.4.2 Managing Dynamic Charges with AI 260
15.4.3 Planned Infrastructure Optimization Algorithms 261
15.5 Charging Intelligent Infrastructures 262
15.5.1 The Challenges of Developing EV Charging Infrastructure 262
15.5.2 Predicting Demand and Selecting Sites with AI 262
15.5.3 Managing and Balancing Loads in Real Time 263
15.5.4 The Integration of Renewable Energy Sources with AI 263
15.5.5 Infrastructural Challenges and Considerations in AI-Driven Charging 264
15.5.6 The Future of AI in EV Charging Infrastructure 264
15.6 Conclusion 265
References 265
16 Deciphering Smart Grid Integration and Energy Management 269
Jatinder Kumar, Protyay Dey, Jasgurpreet Singh Chohan, Sanjeev Kumar Shah and Laith Jasim
16.1 Introduction 270
16.1.1 Smart Grid Systems 271
16.1.2 Energy Management System 272
16.1.3 System for Managing Transmission Energy 273
16.2 A Smart Grid EMS Based on Communication Technologies 275
16.2.1 Gprs 275
16.2.2 WiMAX (IEEE 802.16) 276
16.2.3 Bluetooth (IEEE 802.15) 276
16.2.4 Power Line Communication (PLC) 276
16.3 Smart Grids: An Overview 277
16.3.1 An Overview of Smart Grid Components 277
16.3.2 Goals of a Smart Grid 278
16.4 Integrating Smart Grids with Existing Infrastructure 278
16.4.1 Upgrading Infrastructure 278
16.4.2 Synchronizing with Renewable Sources 279
16.4.3 Digitalizing the Grid 279
16.4.4 Cybersecurity Measures 279
16.5 Energy Management in the Smart Grid 279
16.5.1 Demand Response 279
16.5.2 Distributed Energy Resources Management (derm) 280
16.5.3 Energy Storage Solutions 280
16.5.4 Rates and Pricing for Real-Time Usage 280
16.5.5 Electric Vehicle (EV) Integration 280
16.6 Managing Energy and Integrating Smart Grids 280
16.7 Conclusion 281
References 282
17 Decoding the Aspects of Intelligent Traffic Management 287
Preeti Rani, Jatinder Kumar, Sandeep Singh, Protyay Dey and Laith H. Jasim
17.1 Introduction 288
17.2 Related Work 290
17.3 Proposed Methodology 292
17.3.1 Design Objectives 292
17.3.2 Method and Materials 293
17.4 ITS Applications in Various Transport Sectors 295
17.4.1 Transportation Industry 296
17.4.2 Low CE of Urban Transportation 296
17.4.3 Road Traffic Transportation Infrastructure 296
17.5 Result and Discussion 297
17.6 Conclusion 298
References 299
18 Exploring the Impact of Computer Vision in Smart Transportation 301
Umesh Chandra Garjola, Sandeep Singh, Kamaljeet Kaur, Protyay Dey and Laith Hussein
18.1 Introduction 302
18.1.1 Surveillance Systems Along Roadsides: An Overview 302
18.2 Related Work 303
18.2.1 Computer Vision Functions 303
18.3 Proposed Methodology 308
18.3.1 ACF Object Detection System 309
18.3.2 Point Tracker Algorithm 309
18.3.3 Intelligent Transportation Systems: Computer Vision Applications 309
18.3.4 Intelligent Transportation Systems and Machine Learning (ML) 310
18.3.4.1 Machine Learning: The Evolution 311
18.3.4.2 Challenges 313
18.4 Result and Discussion 314
18.5 Conclusion 317
References 317
19 Exposing the Importance of Connected Lighting for Urban Sustainability 323
Zainab. R. Abdulsada, Kamaljeet Kaur, Sapna Singh, Devendra Singh and Mohammed H. Al-Farouni
19.1 Introduction 324
19.2 Sustainability 326
19.3 Transdisciplinary Framework for Urban Lighting Research: Actors, Framework, and Four Steps 327
19.4 Understanding Connected Lighting Systems 330
19.5 Energy Efficiency and Reduced Carbon Emissions 330
19.6 Conclusion 332
References 333
20 Responsible and Green AI for Environment Sustainability 337
Kunal Gagneja, Sapna Singh, Zainab. R. Abdulsada, Shivani Pant and Rami Riad Hussien
20.1 Introduction 338
20.2 AI and the Environment 339
20.2.1 Green-by AI 340
20.2.2 Green-in AI 342
20.3 Principles of Responsible AI 344
20.4 Sustainable AI for Human and Planetary Flourishing 345
20.5 AI for Environmental Sustainability 348
20.5.1 Climate Prediction and Disaster Management 348
20.5.2 Precision Agriculture 348
20.5.3 Wildlife Conservation and Biodiversity 348
20.5.4 Renewable Energy Optimisation 348
20.6 Challenges and Future Directions 349
20.7 Conclusion 349
References 349
21 Integrating AI into Mobility as a Service (MaaS): The Future of Urban Transportation 355
Kunal Gagneja, Zainab. R. Abdulsada, Sapna Singh, Ruby Pant and Ramy Al-Fatlawy
21.1 Introduction 356
21.1.1 The MaaS Concept 356
21.2 Mobility in Rural Areas is a Problem 359
21.3 Transportation Systems and Artificial Intelligence: A Critical Review 360
21.3.1 Artificial Intelligence-Assisted Smart Cities 360
21.3.2 AI Applications Currently in Use 363
21.3.3 Identifying Research Gaps 365
21.3.4 Sustainability Implications of Apps 365
21.3.5 The Impact of Urban Development on the Environment 366
21.4 Encounters 367
21.4.1 Challenges in Knowledge 367
21.5 Expected Early Adopter and Users 368
21.6 Conclusion 371
References 371
Index 375
