Artificial Intelligence (AI) for Smart and Sustainable Urban Transportation

Sathyan Munirathinam, PhD, is a Senior Manager of Data & Analytics at ASML Corporation in USA. Pethuru R. Chelliah, PhD, is the Vice President of Reliance Jio Platforms Ltd. in Bangalore, India. Peter Augustine, PhD, is a Professor in the Department of Computer Science at CHRIST (Deemed to be University) in Bangalore, India. Beaulah Soundarabai, PhD, is an Associate Professor in the Department of Computer Science at CHRIST (Deemed to be University) in Bangalore, India.

• List of Contributors xxiAbout the Editors xxvii1 Recent Trends in Intelligent Transportation Systems 1Kathi Durgesh, Siddharth Garia, and Vishal Kumar Narnoli1.1 Introduction 11.2 Methodology 31.3 Results 91.4 Discussion 102 Artificial Intelligence and IoT Applications Transforming the Automotive Industry 15Raviprakash R Salagame2.1 Introduction 152.2 Automotive AI Applications and Urban Use Cases 232.3 Connected Vehicles 302.4 Electric Vehicles (EV) 322.5 Shared Mobility 352.6 Future Trends Toward Smart Transportation 352.7 Conclusion 363 Artificial Intelligence in Transportation Using Automated Grading, Adaptive Learning, and Predictive Maintenance to Increase Efficiency 41S. Cyciliya Pearline Christy, K. Merriliance, and Mary Immaculate Sheela Lourdusamy3.1 Introduction 413.2 Overview of Artificial Intelligence in Transportation 423.3 Automated Grading Systems in Transportation 443.4 Adaptive Learning in Traffic Management and Logistics 463.5 Predictive Maintenance in Transportation Systems 483.6 Ethical, Regulatory, and Security Considerations 503.7 Future Outlook and Emerging Technologies 513.8 Conclusion 534 Autonomous Vehicles and Smart Mobility 57P. Sudheer, S. Ashmad, M. Saravanan, and A. Immanuel4.1 Introduction 574.2 Challenges in Smart Mobility and Autonomous Vehicles 624.3 Case Studies and Practical Applications of Smart Mobility and Self-driving Cars 644.4 Policies, Ethics, and Governance in the Autonomous Vehicle Ecosystem 665 Artificial Intelligence (AI) for Smart and Sustainable Urban Transportation 73Ishika Gupta, Hriday Gupta, Siddharth Gupta, and Prerna Ajmani5.1 Introduction 735.2 Background 745.3 Enabling Technologies 785.4 Components 845.5 AI-driven Sustainable Solutions 925.6 Security and Privacy in AI and IoT for Smart Cities and Electric Vehicles 955.7 Case Studies and Real-world Implementations 1005.8 Challenges for 6G 1055.9 Future Directions 1065.10 Conclusion 1106 Smart Mobility: Integrating AI for Sustainable Urban Transportation Solutions 113A. Jothi Kumar6.1 Introduction 1136.2 AI in Traffic Management Systems 1146.3 Virtual Architecture for AI-based Traffic Management Systems 1176.4 AI Applications in Sustainable Urban Mobility 1196.5 Data-driven Mobility Solution 1216.6 Case Studies of AI Implementation in Smart Cities 1216.7 Moral and Political Views 1216.8 Future Trends and Innovations 1226.9 Conclusion 1237 Reinforcement Learning for Energy-efficient Urban Freight Transportation 125Nancy Jasmine Goldena and R. Rashia Subashree7.1 Introduction 1257.2 Fundamentals of RL 1267.3 RL Applications in Energy-efficient Urban Freight Transportation 1277.4 Integration of RL Applications with Smart Logistics and IoT 1317.5 Challenges and Limitations 1367.6 Future Directions 1377.7 Conclusion 1388 Advancements and Challenges in Autonomous Vehicles and Smart Mobility: The Role of AI in Transforming Transportation 141A. Jane, Dr. K. Merriliance, and Dr. Mary Immaculate Sheela Lourdusamy8.1 Introduction 1418.2 Advancements in Autonomous Vehicles and Smart Mobility 1448.3 Artificial Intelligence in Autonomous Vehicles 1458.4 Perception and Fusion of Sensors for AI-powered Automobiles 1488.5 Advantages of AI in Autonomous Vehicles 1508.6 Challenges in Autonomous Vehicles and Smart Mobility 1518.7 Future Directions and Conclusion 1529 Enhancing Urban Traffic Management with Multi-scale Hierarchical GANs 159Ashik Shah Jahangeer and P Shanmugavadivu9.1 A System Stuck in Time 1599.2 When GANs Hit the Road: The Gaps in Current AI Models 1629.3 Reimagining Intelligence: The Architecture of MSH-GAN 1649.4 The City in Layers: Micro and Macro-level Generators 1679.5 Listening to the City: Real-time IoT Data Integration 1699.6 Understanding the Why: Hierarchical Modeling and Contextual Awareness 1729.7 Thinking at the Edge: Decentralized Computation for Faster Response 1749.8 Measuring Intelligence: Evaluating the Performance of MSH-GAN 1769.9 From Control to Care: MSH-GAN and the Future of Smart Cities 1799.10 Looking Ahead: The Road Beyond MSH-GAN 18210 IoT and AI Integration in Traffic Management 187J. Steffi, K. Merriliance, and Mary Immaculate Sheela Lourdusamy10.1 Introduction 18710.2 Role of IoT in Traffic Management 18810.3 AI Applications in Traffic Optimization 19110.4 Smart Traffic Signals and AI-driven Control Systems 19310.5 Incident Detection and Emergency Response 19510.6 Public Transport Enhancement with IoT and AI 19610.7 Environmental and Sustainability Benefits 19810.8 Challenges and Future Trends 20011 Intelligent Urbanism: AI and Big Data-driven Approaches to Planning, Design, and Transportation 205R. Saradha11.1 Introduction 20511.2 Literature Background 20711.3 Methodology 21011.4 Results and Discussion 22111.5 Conclusion 22312 AI-driven Public Transport Solutions 231Shantanu Bindewari, Prakhar Consul, Hilal Ahmed Shah, Basab Nath, and Mansi Trivedi12.1 Introduction 23112.2 AI Applications in Transportation 23412.3 Introduction to AI in Automation and Ticketing 23712.4 AI for Safety and Security 24112.5 Dynamic Route Optimization Systems 24312.6 AI in Public vs. Private Transportation 24512.7 Challenges and Ethical Considerations 24912.8 Future Trends and Innovations 25012.9 Conclusion 25113 AI-driven Data Analytics for Smart Urban Transport: Innovations, Challenges, and Future Trends 255A. Jasmine Sugil, K. Merriliance, and Mary Immaculate Sheela Lourdusamy13.1 Introduction 25513.2 AI-powered Data Sources in Urban Transport 26013.3 AI Techniques for Urban Transport Analytics 26613.4 Key Applications of AI in Urban Transport 27013.5 Case Studies and Real-world Implementations 27213.6 Challenges and Ethical Considerations 27513.7 Future Trends in AI for Urban Transport 27713.8 Conclusion 28014 Transforming Smart Mobility: L4S and NaaS APIs for Real-time Traffic Management and Autonomous Transport 283L. Ameer Shohail14.1 Introduction 28314.2 Architectural Foundation for Real-time and Autonomous Mobility 28414.3 Current Directions in Programmable Transport Networks and LatencyControl 28814.4 System Design and Implementation Strategy for Real-time Mobility Control 29114.5 Results from Real-time Policy and Queue Enforcement 29314.6 Reflections on Programmable Responsiveness in Urban Mobility 29614.7 Conclusion 29815 AI-driven Public Transportation: Enhancing Efficiency, Sustainability, and User Experience 301M. Robinson Joel15.1 Introduction 30115.2 Existing AI Uses in Public Transportation 30415.3 Recognizing AI's Significance in Transportation 30515.4 AI Applications in Transportation: Exemplary Instances 30715.5 Traffic Management Systems Using AI 30915.6 Top AI Resources for Public Transportation 31015.7 AI Improve Public Transportation Efficiency 31815.8 Safety Benefits of AI in Public Transportation 31915.9 Flowchart for GPS-based Vehicle Tracking 32115.10 Build Your Own ESP32 GPS Tracker with Live Tracking 32415.11 Market Share of AI in Transportation by Different Elements 32615.12 Related Work 33115.13 Conclusion 33616 Cognitive AI for Adaptive and Resilient Urban Transportation: A Data-driven Approach to Sustainable Mobility 343Vishal Jain, Archan Mitra, and Sanchita Paul16.1 Introduction 34316.2 Conceptual Framework and Literature Review 34616.3 Methodology 35016.4 Integrated Cognitive AI Framework for Urban Transportation 35216.5 Data Analysis and Empirical Findings 35516.6 Discussion 35816.7 Conclusion 36117 Optimizing Urban Traffic with Graph Analytics: A Case Study of a Metropolitan Transportation Network 367S. Rakshika and Sudeepa Roy Dey17.1 Introduction 36717.2 Related Work 37017.3 Types of Routing Algorithm 37217.4 Work 37518 Urban Mobility Reimagined: AMRUT Interventions and the 2041 Outlook 387S. Thangapriya, Nancy Jasmine Goldena, T. S. Vasughi, M. Kannan, and Barath Ramesh18.1 Introduction 38718.2 Geospatial Mapping of Tirunelveli Using Advanced Technologies 38818.3 Identifying Research Gaps in Tirunelveli for Sustainable Regional Development 38918.4 Climate and Rainfall 39118.5 Precipitation 39218.6 Soil Type Analysis and Resource-efficient Agricultural Planning in Tirunelveli Region 39318.7 Geomorphology 39418.8 A Road map for Tirunelveli's Future Economy 39718.9 AI-based Urban Housing Analytics and Slum Rehabilitation Forecasting for Tirunelveli LPA 39818.10 Tirunelveli 2041 as a Sustainable Growth Use Case 39918.11 Conclusion 40319 GIS-based Analysis of Road Accidents: A Case Study on Hotspot Identification and Safety Improvement 407Kirti Goyal, Siddharth Garia, Anoop Bhardwaj, Amol Sharma, Sneha Das, and Animesh Nayak19.1 Introduction 40719.2 Methodology 40819.3 Results and Discussion 40819.4 Data Collection and Preparation 40819.5 Analysis 41219.6 Identifying Blackspots Using GIS 41419.7 Key Insights 41619.8 Conclusion 41719.9 Recommendations 41719.10 Way Forward 417References 418Index 421