Ish Kumar, PhD, is a transport planning researcher specializing in urban freight logistics, last-mile delivery systems, and Intelligent Transport Systems (ITS). A former Assistant Professor in the Department of Transport Planning at the School of Planning and Architecture (SPA), Delhi, he has published in Scopus-indexed journals, book chapters, and conference proceedings. His work focuses on freight demand modelling, routing and network design, multi-criteria decision analysis, blockchain-enabled transparency, and sustainability and emissions assessment. He has received Best Paper awards at CTSEM 2025 and RATE 2022 and was selected for the VREF Urban Logistics Summer School (University of Antwerp). Kumar serves as a reviewer for leading journals and for major conferences including TRB, WCTR, CTRG, CTSEM, and AIIT TIS Roma. Vinay Maitri, PhD, is a former Professor and Dean at the School of Planning and Architecture, Delhi. With more than three decades in academia and practice, he has helped shape postgraduate curricula in Transport Economics and ITS, supervised numerous theses, and advised on national and city transport initiatives, including World Bank–supported studies. His publications include several books and research papers on transport systems management, freight operations, Geographic Information Systems (GIS) applications, and policy analysis, and he has presented at international forums such as TRB (Washington, D.C.). Maitri has served on professional committees and standards bodies, bringing analytical rigour and practitioner experience to the governance and delivery of urban mobility and logistics.

• List of Figures xiiiList of Tables xivPreface xvAcknowledgements xviiList of Abbreviations xviiiSECTION 1Foundations of ITS in Freight Logistics 11 The Role of Intelligent Transport Systems inModern Freight Supply Chains 31.1 Introduction 31.2 Evolution of Freight and Logistics Systems 41.3 Understanding ITS 51.4 ITS in Modern Supply Chains 81.5 Key Components and Technologies in ITS for Freight 101.5.1 Vehicle Telematics and Fleet Monitoring 101.5.2 GNSS, AVL, and Real-Time Tracking 121.5.3 V2I and V2V Communication Technologies 121.5.4 Digital Freight Platforms and Aggregators 121.5.5 Geofencing and Access Management Systems 121.5.6 Smart Parking and Loading Bay Management 131.5.7 Electronic Proof of Delivery 131.5.8 Urban Freight Control Centres 131.5.9 AI-Based Freight Decision Support Systems 131.5.10 Blockchain for Freight Security and Traceability 141.6 Integration of ITS with Urban Planning 141.7 Global Best Practices in Freight ITS Implementation 151.8 Challenges and Barriers to ITS Adoption 151.8.1 Infrastructure and Technological Gaps 161.8.2 High Capital and Operational Costs 161.8.3 Data Privacy, Security, and Ownership Issues 161.8.4 Institutional Fragmentation and Regulatory Delays 161.8.5 Behavioural and Skill Barriers 171.8.6 Limited Demonstration Projects and Evidence Base 171.9 Summary and Key Takeaways 17References 182 ITS Solutions for Last-Mile Delivery and E-Commerce 222.1 Introduction 222.2 ITS Applications for Last-Mile Delivery 232.3 Digital Technologies Enabling E-Commerce Logistics 242.4 Comparative Approaches: E-Commerce vsQ-Commerce ITS Needs 252.5 ITS-Integrated Emerging Delivery Models 262.5.1 Micro-Hubs 272.5.2 Drone Deliveries 272.5.3 Autonomous Delivery Robots 272.6 Case Studies of ITS in Last-Mile Freight 292.6.1 DHL’s Sensor-Driven Parcel LockerNetwork, Germany 292.6.2 Yamato Transport’s Dynamic RouteOptimization, Japan 302.6.3 Flipkart’s AI-Powered Predictive Routing, India 302.6.4 Barcelona’s Micro-Distribution Centreswith ITS Integration, Spain 312.6.5 Autonomous Delivery Robots in Singapore’sBusiness Districts 312.6.6 Comparative Insights 312.7 Challenges in Adoption and Governance 322.8 Future Outlook and Innovation Pathways 332.9 Summary and Key Takeaways 34References 353 Urban Freight Management with ITS 373.1 Introduction 373.2 Architecture of Integrated Freight ITS 383.3 Data Flow and Interoperability Standards 403.3.1 Structure of Data Flow in Freight ITS 403.3.2 Role of Interoperability Frameworks 403.3.3 Security and Reliability in Data Exchange 403.4 Optimization Models in Freight ITS 413.4.1 Core Vehicle Routing Formulation 413.4.2 Time Windows and Service Times 433.4.3 Energy Aware or Range-Constrained Routing 443.4.4 Fleet Assignment as a Network Flow 443.4.5 Scheduling with Tardiness or Make Span Objectives 443.4.6 Demand Forecasting with ARIMA andCount Models 443.4.7 Predictive Control with Reinforcement Learning 453.4.8 Multi-Objective Cost and Emission Optimization 453.5 Decision Support Systems for Freight Planning 453.5.1 Structure and Core Components 463.5.2 Multi-Criteria Decision-Making in Freight Planning 463.5.3 Integration with Predictive and Prescriptive Analytics 473.6 Integration with Urban Mobility and Smart City Systems 493.7 Case Studies of Integrated ITS Freight Solutions 503.8 Barriers to Integration and Optimization 533.9 Summary and Key Takeaways 54References 54SECTION 2Emerging Technologies in Freight Logistics 594 Digital Twins in Freight and Supply Chains 614.1 Introduction 614.2 Concept and Evolution of Digital Twins 624.3 Framework for Digital Twin Implementation in Freight 634.3.1 Data Acquisition 634.3.2 Integration Platforms 634.3.3 Analytics Engines 634.3.4 Visualization Tools 644.4 Applications in Freight and Supply Chain Operations 644.5 Integration with Other ITS Technologies 664.6 Evidence and Models 674.6.1 Case-Based Evidence 674.6.2 Conceptual Predictive Freight Flow Framework 684.7 Implementation Challenges 694.8 Innovation and Future Trajectories 714.8.1 Data-Driven Predictive Freight Ecosystems 714.8.2 Autonomous and Semi-Autonomous Freight Operations 714.8.3 Integration of Green and Circular Logistics Principles 714.8.4 Hyperconnected Multi-Modal Freight Networks 724.8.5 Policy, Governance, and Institutional Transformation 734.8.6 Research and Development Priorities 734.9 Summary and Key Takeaways 73References 745 Big Data Analytics in Freight ITS 765.1 Introduction 765.2 Data Sources in Freight ITS 775.3 Data Collection, Storage, and Processing Frameworks 815.3.1 Data Collection 815.3.2 Data Storage 815.3.3 Data Processing 825.4 Analytical Techniques in Freight ITS 835.4.1 Descriptive Analytics 835.4.2 Predictive Analytics 845.4.3 Prescriptive Analytics 845.4.4 Comparative Perspective 855.5 Big Data–Driven DSS 855.6 Applications in Urban Freight and Last-Mile Delivery 875.6.1 Real-Time Traffic Prediction 875.6.2 Dynamic Load Balancing and Fleet Reallocation 875.6.3 Delivery Time Window Optimization 885.6.4 Sustainable and Low-Emission Freight Routing 885.6.5 On-Demand Delivery Orchestration 885.6.6 Predictive Maintenance for Urban Fleets 885.6.7 Warehouse and Micro-Fulfilment Optimization 895.6.8 Crowdshipping and Platform-Based Logistics 895.7 Case Studies 895.7.1 Case 1: India Big Data in E-CommerceFleet Analytics 905.7.2 Case 2: Port of Rotterdam Digital Twin 905.8 Challenges and Risks 915.8.1 Data Privacy and Ownership Concerns 915.8.2 Interoperability Issues in Heterogeneous ITS Systems 915.8.3 Infrastructure, Skills, and Cost Barriers 925.8.4 Cybersecurity Risks 925.8.5 Organizational Resistance and Change Management 925.9 Summary and Key Takeaways 92References 936 Blockchain Applications in Freight ITS 976.1 Introduction 976.2 Fundamentals of Blockchain Technology for Logistics 996.3 Blockchain Applications in Freight ITS 1006.3.1 Secure Data Exchange 1006.3.2 End-to-End Visibility and Traceability 1006.3.3 Automated Freight Payments and Settlements 1006.3.4 Digital Identity for Vehicles, Shipments, and Assets 1016.4 Integration with ITS 1016.4.1 Blockchain and IoT Integration 1016.4.2 Interoperability with Big Data Analytics 1036.4.3 Blockchain in ITS-Enabled Policy Mechanisms 1036.4.4 Towards a Unified Freight ITS Ecosystem 1036.5 Case Studies 1046.5.1 Case 1: India – TradeLens at Major Ports 1046.5.2 Case 2: Germany – DHL’s Pharmaceutical Cold Chain Pilot 1056.6 Challenges in Adoption 1056.6.1 Technical Challenges 1056.6.2 Regulatory Challenges 1066.6.3 Organizational Challenges 1076.7 Future Pathways 1076.7.1 Blockchain and AI for Predictive Freight 1076.7.2 Blockchain and IoT for Automated Compliance 1086.7.3 Decentralized Freight Marketplaces 1086.7.4 Policy and Sustainability Dimensions 1086.8 Summary and Key Takeaways 109References 1107 Autonomous Freight Systems and Logistics Automation 1137.1 Introduction 1137.2 Evolution of Automation in Freight Logistics 1147.3 Core Technologies Behind Freight Automation 1167.3.1 LIDAR 1167.3.2 Radar and Ultrasonic Sensors 1167.3.3 Computer Vision 1167.3.4 AI/ML 1167.3.5 V2X Communication 1177.3.6 Digital Twins and Simulation 177.4 AFVs 1177.4.1 Long-Haul Autonomous Trucks 1197.4.2 Middle-Mile and Urban Delivery Vans 1197.4.3 Last-Mile Delivery Robots and Drones 1197.4.4 Regulatory and Safety Considerations 1207.5 Integration with ITS 1207.5.1 Real-Time Traffic Coordination 1207.5.2 Freight Signal Priority 1207.5.3 Truck Platooning and Cooperative Driving 1207.5.4 Connection to Logistics Platforms 1217.5.5 Data Sharing and Cybersecurity 1217.6 Robotics in Freight Terminals and Warehouses 1217.6.1 AGVs and Autonomous Mobile Robots 1217.6.2 Drones for Inventory and Delivery 1217.6.3 Cobots 1237.6.4 Automated Storage and Retrieval Systems 1237.6.5 Autonomous Forklifts, Robotic Arms,and Palletizing Robots 1237.6.6 Robotic Sortation Systems and Conveyors 1237.6.7 Swarm Robotics and Digital Twins 1237.6.8 Vision-Based Picking Robots 1247.6.9 Exoskeletons (Wearable Robotics) 1247.7 Case Studies of Freight Automation 1247.7.1 U.S. Autonomous Trucking Pilots 1247.7.2 Warehouse Robotics in Europe and Asia 1257.8 Challenges and Risks 1257.8.1 Technical Limitations and Infrastructure Readiness 1267.8.2 Cybersecurity and Data Vulnerabilities 1267.8.3 Legal and Ethical Considerations 1267.8.4 Workforce Transition and Socio-Economic Impacts 1267.8.5 Interoperability and Standardization Issues 1277.9 Summary and Key Takeaways 127References 128SECTION 3Sustainability and Future Directions 1318 Digital Transformation in Sustainable Urban Freight 1338.1 Introduction 1338.2 Digital Transformation Tools for Sustainable Freight 1348.2.1 IoT and Real-Time Monitoring 1358.2.2 AI and Predictive Analytics 1358.2.3 Information and Communication Technology Platforms 1358.2.4 Digital Twins and Simulation Models 1358.2.5 Electrification and Smart Routing Integration 1368.2.6 Governance Through Digital Data 1368.3 UCCs and Micro-Hubs 1378.4 Low Emission and Zero Emission Zones 1378.5 Electric and Alternative Fuel Freight Vehicles 1418.5.1 Electric Freight Vehicles 1418.5.2 Hydrogen Fuel Cell Trucks 1418.5.3 Cargo Bikes and Non-Motorized Alternatives 1428.5.4 Barriers to Adoptionv1428.5.5 Policy and Market Enablers 1438.6 Integration with Smart City and ITS Frameworks 1438.6.1 UFCCs 1438.6.2 Digital Twins for Freight Planning 1438.6.3 ITS-Enabled Curbside and Network Management 1458.6.4 Integration with Broader Smart City Platforms 1458.6.5 Policy and Institutional Dimensions 1458.7 Case Studies: Sustainable Freight in Practice 1458.7.1 London’s Low Emission Freight Zone (LEZ and ULEZ) 1468.7.2 Paris’ UCCs 1468.7.3 New York City’s Off-Hour Delivery Programme 1478.7.4 Tokyo’s Electric and Hybrid Freight Fleet Initiatives 1478.7.5 Delhi’s Electric Freight Policy Implementation 1478.8 Challenges, Barriers, and Enablers of Sustainable Urban Freight 1478.8.1 Technical and Infrastructure Challenges 1478.8.2 Financial and Market Barriers 1488.8.3 Institutional and Governance Constraints 1488.8.4 Operational and Behavioural Challenges 1488.8.5 Social and Equity Dimensions 1498.8.6 Role of Technology and Digital Enablers 1498.9 Summary and Key Takeaways 149References 1509 Economic and Financial Dimensions of ITS in Freight Logistics 1559.1 Introduction 1559.2 Conceptual Underpinnings of Economic Impacts 1569.3 Dimensions of Economic Impacts 1579.3.1 Cost Reduction and Operational Efficiency 1589.3.2 Productivity Gains and Service Reliability 1599.3.3 Investment and Return on Technology 1609.3.4 Market Competitiveness and Innovation 1609.4 Regional and Sectoral Perspectives 1619.5 Case Studies 1659.5.1 Case 1: DHL Smart Logistics (Europe) 1659.5.2 Case 2: UPS ORION Routing (United States) 1669.5.3 E-commerce and FASTag (India) 1679.5.4 Comparative Synthesis 1689.6 Challenges in Realizing Economic Benefits 1699.6.1 High Capital Expenditure 1699.6.2 Uneven Returns and Scale Effects 1699.6.3 Digital Divide and Data Gaps 1699.6.4 Short-Term Disruptions and Transition Costs 1699.6.5 Risks of Over-Automation and Sunk Costs 1709.7 Summary and Key Takeaways 170References 17110 Pathways for ITS Adoption in Developing Economies 17410.1 Introduction 17410.2 Barriers in Developing Economies 17510.2.1 Infrastructure Gaps 17510.2.2 Fragmented Governance and Policy Misalignment 17510.2.3 Financial Constraints and Limited Investment 17610.2.4 Institutional Weaknesses and Capacity Deficits 17610.2.5 Regulatory Gaps and Informal Logistics Dominance 17610.2.6 Social and Cultural Constraints 17610.3 Enablers of Adoption 17710.3.1 Digital Transformation and Technology Diffusion 17710.3.2 Low-Cost Innovations and Local Adaptations 17710.3.3 Policy Enablers and Open Standards 17910.3.4 Financing Pathways: Public Private Partnerships,Value Capture, and Risk Sharing 18010.3.5 Capacity and Skills as Enablers 18010.3.6 Global Best Practices and Knowledge Transfer 18010.3.7 Synthesis of Enablers 18110.4 Institutional and Governance Pathways 18110.4.1 Integrated Institutional Structures 18110.4.2 Regulatory Governance and Enforcement 18210.4.3 Financing Reforms and Sustainability 18210.4.4 PPP Governance 18210.4.5 Institutional Capacity and Change Management 18210.4.6 Feedback Loops and Adaptive Governance 18310.5 Regional Case Studies 18310.5.1 India: FASTag and EV Freight 18310.5.2 Africa: Smart Pilots and Corridors 18410.5.3 Latin America: Consolidation and Regulation 18510.5.4 Comparative Synthesis 18510.6 Summary and Key Takeaways 185References 186Index 189

Ish Kumar, PhD, is a transport planning researcher specializing in urban freight logistics, last-mile delivery systems, and Intelligent Transport Systems (ITS). A former Assistant Professor in the Department of Transport Planning at the School of Planning and Architecture (SPA), Delhi, he has published in Scopus-indexed journals, book chapters, and conference proceedings. His work focuses on freight demand modelling, routing and network design, multi-criteria decision analysis, blockchain-enabled transparency, and sustainability and emissions assessment. He has received Best Paper awards at CTSEM 2025 and RATE 2022 and was selected for the VREF Urban Logistics Summer School (University of Antwerp). Kumar serves as a reviewer for leading journals and for major conferences including TRB, WCTR, CTRG, CTSEM, and AIIT TIS Roma. Vinay Maitri, PhD, is a former Professor and Dean at the School of Planning and Architecture, Delhi. With more than three decades in academia and practice, he has helped shape postgraduate curricula in Transport Economics and ITS, supervised numerous theses, and advised on national and city transport initiatives, including World Bank–supported studies. His publications include several books and research papers on transport systems management, freight operations, Geographic Information Systems (GIS) applications, and policy analysis, and he has presented at international forums such as TRB (Washington, D.C.). Maitri has served on professional committees and standards bodies, bringing analytical rigour and practitioner experience to the governance and delivery of urban mobility and logistics.

• List of Figures xiiiList of Tables xivPreface xvAcknowledgements xviiList of Abbreviations xviiiSECTION 1Foundations of ITS in Freight Logistics 11 The Role of Intelligent Transport Systems inModern Freight Supply Chains 31.1 Introduction 31.2 Evolution of Freight and Logistics Systems 41.3 Understanding ITS 51.4 ITS in Modern Supply Chains 81.5 Key Components and Technologies in ITS for Freight 101.5.1 Vehicle Telematics and Fleet Monitoring 101.5.2 GNSS, AVL, and Real-Time Tracking 121.5.3 V2I and V2V Communication Technologies 121.5.4 Digital Freight Platforms and Aggregators 121.5.5 Geofencing and Access Management Systems 121.5.6 Smart Parking and Loading Bay Management 131.5.7 Electronic Proof of Delivery 131.5.8 Urban Freight Control Centres 131.5.9 AI-Based Freight Decision Support Systems 131.5.10 Blockchain for Freight Security and Traceability 141.6 Integration of ITS with Urban Planning 141.7 Global Best Practices in Freight ITS Implementation 151.8 Challenges and Barriers to ITS Adoption 151.8.1 Infrastructure and Technological Gaps 161.8.2 High Capital and Operational Costs 161.8.3 Data Privacy, Security, and Ownership Issues 161.8.4 Institutional Fragmentation and Regulatory Delays 161.8.5 Behavioural and Skill Barriers 171.8.6 Limited Demonstration Projects and Evidence Base 171.9 Summary and Key Takeaways 17References 182 ITS Solutions for Last-Mile Delivery and E-Commerce 222.1 Introduction 222.2 ITS Applications for Last-Mile Delivery 232.3 Digital Technologies Enabling E-Commerce Logistics 242.4 Comparative Approaches: E-Commerce vsQ-Commerce ITS Needs 252.5 ITS-Integrated Emerging Delivery Models 262.5.1 Micro-Hubs 272.5.2 Drone Deliveries 272.5.3 Autonomous Delivery Robots 272.6 Case Studies of ITS in Last-Mile Freight 292.6.1 DHL’s Sensor-Driven Parcel LockerNetwork, Germany 292.6.2 Yamato Transport’s Dynamic RouteOptimization, Japan 302.6.3 Flipkart’s AI-Powered Predictive Routing, India 302.6.4 Barcelona’s Micro-Distribution Centreswith ITS Integration, Spain 312.6.5 Autonomous Delivery Robots in Singapore’sBusiness Districts 312.6.6 Comparative Insights 312.7 Challenges in Adoption and Governance 322.8 Future Outlook and Innovation Pathways 332.9 Summary and Key Takeaways 34References 353 Urban Freight Management with ITS 373.1 Introduction 373.2 Architecture of Integrated Freight ITS 383.3 Data Flow and Interoperability Standards 403.3.1 Structure of Data Flow in Freight ITS 403.3.2 Role of Interoperability Frameworks 403.3.3 Security and Reliability in Data Exchange 403.4 Optimization Models in Freight ITS 413.4.1 Core Vehicle Routing Formulation 413.4.2 Time Windows and Service Times 433.4.3 Energy Aware or Range-Constrained Routing 443.4.4 Fleet Assignment as a Network Flow 443.4.5 Scheduling with Tardiness or Make Span Objectives 443.4.6 Demand Forecasting with ARIMA andCount Models 443.4.7 Predictive Control with Reinforcement Learning 453.4.8 Multi-Objective Cost and Emission Optimization 453.5 Decision Support Systems for Freight Planning 453.5.1 Structure and Core Components 463.5.2 Multi-Criteria Decision-Making in Freight Planning 463.5.3 Integration with Predictive and Prescriptive Analytics 473.6 Integration with Urban Mobility and Smart City Systems 493.7 Case Studies of Integrated ITS Freight Solutions 503.8 Barriers to Integration and Optimization 533.9 Summary and Key Takeaways 54References 54SECTION 2Emerging Technologies in Freight Logistics 594 Digital Twins in Freight and Supply Chains 614.1 Introduction 614.2 Concept and Evolution of Digital Twins 624.3 Framework for Digital Twin Implementation in Freight 634.3.1 Data Acquisition 634.3.2 Integration Platforms 634.3.3 Analytics Engines 634.3.4 Visualization Tools 644.4 Applications in Freight and Supply Chain Operations 644.5 Integration with Other ITS Technologies 664.6 Evidence and Models 674.6.1 Case-Based Evidence 674.6.2 Conceptual Predictive Freight Flow Framework 684.7 Implementation Challenges 694.8 Innovation and Future Trajectories 714.8.1 Data-Driven Predictive Freight Ecosystems 714.8.2 Autonomous and Semi-Autonomous Freight Operations 714.8.3 Integration of Green and Circular Logistics Principles 714.8.4 Hyperconnected Multi-Modal Freight Networks 724.8.5 Policy, Governance, and Institutional Transformation 734.8.6 Research and Development Priorities 734.9 Summary and Key Takeaways 73References 745 Big Data Analytics in Freight ITS 765.1 Introduction 765.2 Data Sources in Freight ITS 775.3 Data Collection, Storage, and Processing Frameworks 815.3.1 Data Collection 815.3.2 Data Storage 815.3.3 Data Processing 825.4 Analytical Techniques in Freight ITS 835.4.1 Descriptive Analytics 835.4.2 Predictive Analytics 845.4.3 Prescriptive Analytics 845.4.4 Comparative Perspective 855.5 Big Data–Driven DSS 855.6 Applications in Urban Freight and Last-Mile Delivery 875.6.1 Real-Time Traffic Prediction 875.6.2 Dynamic Load Balancing and Fleet Reallocation 875.6.3 Delivery Time Window Optimization 885.6.4 Sustainable and Low-Emission Freight Routing 885.6.5 On-Demand Delivery Orchestration 885.6.6 Predictive Maintenance for Urban Fleets 885.6.7 Warehouse and Micro-Fulfilment Optimization 895.6.8 Crowdshipping and Platform-Based Logistics 895.7 Case Studies 895.7.1 Case 1: India Big Data in E-CommerceFleet Analytics 905.7.2 Case 2: Port of Rotterdam Digital Twin 905.8 Challenges and Risks 915.8.1 Data Privacy and Ownership Concerns 915.8.2 Interoperability Issues in Heterogeneous ITS Systems 915.8.3 Infrastructure, Skills, and Cost Barriers 925.8.4 Cybersecurity Risks 925.8.5 Organizational Resistance and Change Management 925.9 Summary and Key Takeaways 92References 936 Blockchain Applications in Freight ITS 976.1 Introduction 976.2 Fundamentals of Blockchain Technology for Logistics 996.3 Blockchain Applications in Freight ITS 1006.3.1 Secure Data Exchange 1006.3.2 End-to-End Visibility and Traceability 1006.3.3 Automated Freight Payments and Settlements 1006.3.4 Digital Identity for Vehicles, Shipments, and Assets 1016.4 Integration with ITS 1016.4.1 Blockchain and IoT Integration 1016.4.2 Interoperability with Big Data Analytics 1036.4.3 Blockchain in ITS-Enabled Policy Mechanisms 1036.4.4 Towards a Unified Freight ITS Ecosystem 1036.5 Case Studies 1046.5.1 Case 1: India – TradeLens at Major Ports 1046.5.2 Case 2: Germany – DHL’s Pharmaceutical Cold Chain Pilot 1056.6 Challenges in Adoption 1056.6.1 Technical Challenges 1056.6.2 Regulatory Challenges 1066.6.3 Organizational Challenges 1076.7 Future Pathways 1076.7.1 Blockchain and AI for Predictive Freight 1076.7.2 Blockchain and IoT for Automated Compliance 1086.7.3 Decentralized Freight Marketplaces 1086.7.4 Policy and Sustainability Dimensions 1086.8 Summary and Key Takeaways 109References 1107 Autonomous Freight Systems and Logistics Automation 1137.1 Introduction 1137.2 Evolution of Automation in Freight Logistics 1147.3 Core Technologies Behind Freight Automation 1167.3.1 LIDAR 1167.3.2 Radar and Ultrasonic Sensors 1167.3.3 Computer Vision 1167.3.4 AI/ML 1167.3.5 V2X Communication 1177.3.6 Digital Twins and Simulation 177.4 AFVs 1177.4.1 Long-Haul Autonomous Trucks 1197.4.2 Middle-Mile and Urban Delivery Vans 1197.4.3 Last-Mile Delivery Robots and Drones 1197.4.4 Regulatory and Safety Considerations 1207.5 Integration with ITS 1207.5.1 Real-Time Traffic Coordination 1207.5.2 Freight Signal Priority 1207.5.3 Truck Platooning and Cooperative Driving 1207.5.4 Connection to Logistics Platforms 1217.5.5 Data Sharing and Cybersecurity 1217.6 Robotics in Freight Terminals and Warehouses 1217.6.1 AGVs and Autonomous Mobile Robots 1217.6.2 Drones for Inventory and Delivery 1217.6.3 Cobots 1237.6.4 Automated Storage and Retrieval Systems 1237.6.5 Autonomous Forklifts, Robotic Arms,and Palletizing Robots 1237.6.6 Robotic Sortation Systems and Conveyors 1237.6.7 Swarm Robotics and Digital Twins 1237.6.8 Vision-Based Picking Robots 1247.6.9 Exoskeletons (Wearable Robotics) 1247.7 Case Studies of Freight Automation 1247.7.1 U.S. Autonomous Trucking Pilots 1247.7.2 Warehouse Robotics in Europe and Asia 1257.8 Challenges and Risks 1257.8.1 Technical Limitations and Infrastructure Readiness 1267.8.2 Cybersecurity and Data Vulnerabilities 1267.8.3 Legal and Ethical Considerations 1267.8.4 Workforce Transition and Socio-Economic Impacts 1267.8.5 Interoperability and Standardization Issues 1277.9 Summary and Key Takeaways 127References 128SECTION 3Sustainability and Future Directions 1318 Digital Transformation in Sustainable Urban Freight 1338.1 Introduction 1338.2 Digital Transformation Tools for Sustainable Freight 1348.2.1 IoT and Real-Time Monitoring 1358.2.2 AI and Predictive Analytics 1358.2.3 Information and Communication Technology Platforms 1358.2.4 Digital Twins and Simulation Models 1358.2.5 Electrification and Smart Routing Integration 1368.2.6 Governance Through Digital Data 1368.3 UCCs and Micro-Hubs 1378.4 Low Emission and Zero Emission Zones 1378.5 Electric and Alternative Fuel Freight Vehicles 1418.5.1 Electric Freight Vehicles 1418.5.2 Hydrogen Fuel Cell Trucks 1418.5.3 Cargo Bikes and Non-Motorized Alternatives 1428.5.4 Barriers to Adoptionv1428.5.5 Policy and Market Enablers 1438.6 Integration with Smart City and ITS Frameworks 1438.6.1 UFCCs 1438.6.2 Digital Twins for Freight Planning 1438.6.3 ITS-Enabled Curbside and Network Management 1458.6.4 Integration with Broader Smart City Platforms 1458.6.5 Policy and Institutional Dimensions 1458.7 Case Studies: Sustainable Freight in Practice 1458.7.1 London’s Low Emission Freight Zone (LEZ and ULEZ) 1468.7.2 Paris’ UCCs 1468.7.3 New York City’s Off-Hour Delivery Programme 1478.7.4 Tokyo’s Electric and Hybrid Freight Fleet Initiatives 1478.7.5 Delhi’s Electric Freight Policy Implementation 1478.8 Challenges, Barriers, and Enablers of Sustainable Urban Freight 1478.8.1 Technical and Infrastructure Challenges 1478.8.2 Financial and Market Barriers 1488.8.3 Institutional and Governance Constraints 1488.8.4 Operational and Behavioural Challenges 1488.8.5 Social and Equity Dimensions 1498.8.6 Role of Technology and Digital Enablers 1498.9 Summary and Key Takeaways 149References 1509 Economic and Financial Dimensions of ITS in Freight Logistics 1559.1 Introduction 1559.2 Conceptual Underpinnings of Economic Impacts 1569.3 Dimensions of Economic Impacts 1579.3.1 Cost Reduction and Operational Efficiency 1589.3.2 Productivity Gains and Service Reliability 1599.3.3 Investment and Return on Technology 1609.3.4 Market Competitiveness and Innovation 1609.4 Regional and Sectoral Perspectives 1619.5 Case Studies 1659.5.1 Case 1: DHL Smart Logistics (Europe) 1659.5.2 Case 2: UPS ORION Routing (United States) 1669.5.3 E-commerce and FASTag (India) 1679.5.4 Comparative Synthesis 1689.6 Challenges in Realizing Economic Benefits 1699.6.1 High Capital Expenditure 1699.6.2 Uneven Returns and Scale Effects 1699.6.3 Digital Divide and Data Gaps 1699.6.4 Short-Term Disruptions and Transition Costs 1699.6.5 Risks of Over-Automation and Sunk Costs 1709.7 Summary and Key Takeaways 170References 17110 Pathways for ITS Adoption in Developing Economies 17410.1 Introduction 17410.2 Barriers in Developing Economies 17510.2.1 Infrastructure Gaps 17510.2.2 Fragmented Governance and Policy Misalignment 17510.2.3 Financial Constraints and Limited Investment 17610.2.4 Institutional Weaknesses and Capacity Deficits 17610.2.5 Regulatory Gaps and Informal Logistics Dominance 17610.2.6 Social and Cultural Constraints 17610.3 Enablers of Adoption 17710.3.1 Digital Transformation and Technology Diffusion 17710.3.2 Low-Cost Innovations and Local Adaptations 17710.3.3 Policy Enablers and Open Standards 17910.3.4 Financing Pathways: Public Private Partnerships,Value Capture, and Risk Sharing 18010.3.5 Capacity and Skills as Enablers 18010.3.6 Global Best Practices and Knowledge Transfer 18010.3.7 Synthesis of Enablers 18110.4 Institutional and Governance Pathways 18110.4.1 Integrated Institutional Structures 18110.4.2 Regulatory Governance and Enforcement 18210.4.3 Financing Reforms and Sustainability 18210.4.4 PPP Governance 18210.4.5 Institutional Capacity and Change Management 18210.4.6 Feedback Loops and Adaptive Governance 18310.5 Regional Case Studies 18310.5.1 India: FASTag and EV Freight 18310.5.2 Africa: Smart Pilots and Corridors 18410.5.3 Latin America: Consolidation and Regulation 18510.5.4 Comparative Synthesis 18510.6 Summary and Key Takeaways 185References 186Index 189

Ish Kumar, PhD, is a transport planning researcher specializing in urban freight logistics, last-mile delivery systems, and Intelligent Transport Systems (ITS). A former Assistant Professor in the Department of Transport Planning at the School of Planning and Architecture (SPA), Delhi, he has published in Scopus-indexed journals, book chapters, and conference proceedings. His work focuses on freight demand modelling, routing and network design, multi-criteria decision analysis, blockchain-enabled transparency, and sustainability and emissions assessment. He has received Best Paper awards at CTSEM 2025 and RATE 2022 and was selected for the VREF Urban Logistics Summer School (University of Antwerp). Kumar serves as a reviewer for leading journals and for major conferences including TRB, WCTR, CTRG, CTSEM, and AIIT TIS Roma. Vinay Maitri, PhD, is a former Professor and Dean at the School of Planning and Architecture, Delhi. With more than three decades in academia and practice, he has helped shape postgraduate curricula in Transport Economics and ITS, supervised numerous theses, and advised on national and city transport initiatives, including World Bank–supported studies. His publications include several books and research papers on transport systems management, freight operations, Geographic Information Systems (GIS) applications, and policy analysis, and he has presented at international forums such as TRB (Washington, D.C.). Maitri has served on professional committees and standards bodies, bringing analytical rigour and practitioner experience to the governance and delivery of urban mobility and logistics.

• List of Figures xiiiList of Tables xivPreface xvAcknowledgements xviiList of Abbreviations xviiiSECTION 1Foundations of ITS in Freight Logistics 11 The Role of Intelligent Transport Systems inModern Freight Supply Chains 31.1 Introduction 31.2 Evolution of Freight and Logistics Systems 41.3 Understanding ITS 51.4 ITS in Modern Supply Chains 81.5 Key Components and Technologies in ITS for Freight 101.5.1 Vehicle Telematics and Fleet Monitoring 101.5.2 GNSS, AVL, and Real-Time Tracking 121.5.3 V2I and V2V Communication Technologies 121.5.4 Digital Freight Platforms and Aggregators 121.5.5 Geofencing and Access Management Systems 121.5.6 Smart Parking and Loading Bay Management 131.5.7 Electronic Proof of Delivery 131.5.8 Urban Freight Control Centres 131.5.9 AI-Based Freight Decision Support Systems 131.5.10 Blockchain for Freight Security and Traceability 141.6 Integration of ITS with Urban Planning 141.7 Global Best Practices in Freight ITS Implementation 151.8 Challenges and Barriers to ITS Adoption 151.8.1 Infrastructure and Technological Gaps 161.8.2 High Capital and Operational Costs 161.8.3 Data Privacy, Security, and Ownership Issues 161.8.4 Institutional Fragmentation and Regulatory Delays 161.8.5 Behavioural and Skill Barriers 171.8.6 Limited Demonstration Projects and Evidence Base 171.9 Summary and Key Takeaways 17References 182 ITS Solutions for Last-Mile Delivery and E-Commerce 222.1 Introduction 222.2 ITS Applications for Last-Mile Delivery 232.3 Digital Technologies Enabling E-Commerce Logistics 242.4 Comparative Approaches: E-Commerce vsQ-Commerce ITS Needs 252.5 ITS-Integrated Emerging Delivery Models 262.5.1 Micro-Hubs 272.5.2 Drone Deliveries 272.5.3 Autonomous Delivery Robots 272.6 Case Studies of ITS in Last-Mile Freight 292.6.1 DHL’s Sensor-Driven Parcel LockerNetwork, Germany 292.6.2 Yamato Transport’s Dynamic RouteOptimization, Japan 302.6.3 Flipkart’s AI-Powered Predictive Routing, India 302.6.4 Barcelona’s Micro-Distribution Centreswith ITS Integration, Spain 312.6.5 Autonomous Delivery Robots in Singapore’sBusiness Districts 312.6.6 Comparative Insights 312.7 Challenges in Adoption and Governance 322.8 Future Outlook and Innovation Pathways 332.9 Summary and Key Takeaways 34References 353 Urban Freight Management with ITS 373.1 Introduction 373.2 Architecture of Integrated Freight ITS 383.3 Data Flow and Interoperability Standards 403.3.1 Structure of Data Flow in Freight ITS 403.3.2 Role of Interoperability Frameworks 403.3.3 Security and Reliability in Data Exchange 403.4 Optimization Models in Freight ITS 413.4.1 Core Vehicle Routing Formulation 413.4.2 Time Windows and Service Times 433.4.3 Energy Aware or Range-Constrained Routing 443.4.4 Fleet Assignment as a Network Flow 443.4.5 Scheduling with Tardiness or Make Span Objectives 443.4.6 Demand Forecasting with ARIMA andCount Models 443.4.7 Predictive Control with Reinforcement Learning 453.4.8 Multi-Objective Cost and Emission Optimization 453.5 Decision Support Systems for Freight Planning 453.5.1 Structure and Core Components 463.5.2 Multi-Criteria Decision-Making in Freight Planning 463.5.3 Integration with Predictive and Prescriptive Analytics 473.6 Integration with Urban Mobility and Smart City Systems 493.7 Case Studies of Integrated ITS Freight Solutions 503.8 Barriers to Integration and Optimization 533.9 Summary and Key Takeaways 54References 54SECTION 2Emerging Technologies in Freight Logistics 594 Digital Twins in Freight and Supply Chains 614.1 Introduction 614.2 Concept and Evolution of Digital Twins 624.3 Framework for Digital Twin Implementation in Freight 634.3.1 Data Acquisition 634.3.2 Integration Platforms 634.3.3 Analytics Engines 634.3.4 Visualization Tools 644.4 Applications in Freight and Supply Chain Operations 644.5 Integration with Other ITS Technologies 664.6 Evidence and Models 674.6.1 Case-Based Evidence 674.6.2 Conceptual Predictive Freight Flow Framework 684.7 Implementation Challenges 694.8 Innovation and Future Trajectories 714.8.1 Data-Driven Predictive Freight Ecosystems 714.8.2 Autonomous and Semi-Autonomous Freight Operations 714.8.3 Integration of Green and Circular Logistics Principles 714.8.4 Hyperconnected Multi-Modal Freight Networks 724.8.5 Policy, Governance, and Institutional Transformation 734.8.6 Research and Development Priorities 734.9 Summary and Key Takeaways 73References 745 Big Data Analytics in Freight ITS 765.1 Introduction 765.2 Data Sources in Freight ITS 775.3 Data Collection, Storage, and Processing Frameworks 815.3.1 Data Collection 815.3.2 Data Storage 815.3.3 Data Processing 825.4 Analytical Techniques in Freight ITS 835.4.1 Descriptive Analytics 835.4.2 Predictive Analytics 845.4.3 Prescriptive Analytics 845.4.4 Comparative Perspective 855.5 Big Data–Driven DSS 855.6 Applications in Urban Freight and Last-Mile Delivery 875.6.1 Real-Time Traffic Prediction 875.6.2 Dynamic Load Balancing and Fleet Reallocation 875.6.3 Delivery Time Window Optimization 885.6.4 Sustainable and Low-Emission Freight Routing 885.6.5 On-Demand Delivery Orchestration 885.6.6 Predictive Maintenance for Urban Fleets 885.6.7 Warehouse and Micro-Fulfilment Optimization 895.6.8 Crowdshipping and Platform-Based Logistics 895.7 Case Studies 895.7.1 Case 1: India Big Data in E-CommerceFleet Analytics 905.7.2 Case 2: Port of Rotterdam Digital Twin 905.8 Challenges and Risks 915.8.1 Data Privacy and Ownership Concerns 915.8.2 Interoperability Issues in Heterogeneous ITS Systems 915.8.3 Infrastructure, Skills, and Cost Barriers 925.8.4 Cybersecurity Risks 925.8.5 Organizational Resistance and Change Management 925.9 Summary and Key Takeaways 92References 936 Blockchain Applications in Freight ITS 976.1 Introduction 976.2 Fundamentals of Blockchain Technology for Logistics 996.3 Blockchain Applications in Freight ITS 1006.3.1 Secure Data Exchange 1006.3.2 End-to-End Visibility and Traceability 1006.3.3 Automated Freight Payments and Settlements 1006.3.4 Digital Identity for Vehicles, Shipments, and Assets 1016.4 Integration with ITS 1016.4.1 Blockchain and IoT Integration 1016.4.2 Interoperability with Big Data Analytics 1036.4.3 Blockchain in ITS-Enabled Policy Mechanisms 1036.4.4 Towards a Unified Freight ITS Ecosystem 1036.5 Case Studies 1046.5.1 Case 1: India – TradeLens at Major Ports 1046.5.2 Case 2: Germany – DHL’s Pharmaceutical Cold Chain Pilot 1056.6 Challenges in Adoption 1056.6.1 Technical Challenges 1056.6.2 Regulatory Challenges 1066.6.3 Organizational Challenges 1076.7 Future Pathways 1076.7.1 Blockchain and AI for Predictive Freight 1076.7.2 Blockchain and IoT for Automated Compliance 1086.7.3 Decentralized Freight Marketplaces 1086.7.4 Policy and Sustainability Dimensions 1086.8 Summary and Key Takeaways 109References 1107 Autonomous Freight Systems and Logistics Automation 1137.1 Introduction 1137.2 Evolution of Automation in Freight Logistics 1147.3 Core Technologies Behind Freight Automation 1167.3.1 LIDAR 1167.3.2 Radar and Ultrasonic Sensors 1167.3.3 Computer Vision 1167.3.4 AI/ML 1167.3.5 V2X Communication 1177.3.6 Digital Twins and Simulation 177.4 AFVs 1177.4.1 Long-Haul Autonomous Trucks 1197.4.2 Middle-Mile and Urban Delivery Vans 1197.4.3 Last-Mile Delivery Robots and Drones 1197.4.4 Regulatory and Safety Considerations 1207.5 Integration with ITS 1207.5.1 Real-Time Traffic Coordination 1207.5.2 Freight Signal Priority 1207.5.3 Truck Platooning and Cooperative Driving 1207.5.4 Connection to Logistics Platforms 1217.5.5 Data Sharing and Cybersecurity 1217.6 Robotics in Freight Terminals and Warehouses 1217.6.1 AGVs and Autonomous Mobile Robots 1217.6.2 Drones for Inventory and Delivery 1217.6.3 Cobots 1237.6.4 Automated Storage and Retrieval Systems 1237.6.5 Autonomous Forklifts, Robotic Arms,and Palletizing Robots 1237.6.6 Robotic Sortation Systems and Conveyors 1237.6.7 Swarm Robotics and Digital Twins 1237.6.8 Vision-Based Picking Robots 1247.6.9 Exoskeletons (Wearable Robotics) 1247.7 Case Studies of Freight Automation 1247.7.1 U.S. Autonomous Trucking Pilots 1247.7.2 Warehouse Robotics in Europe and Asia 1257.8 Challenges and Risks 1257.8.1 Technical Limitations and Infrastructure Readiness 1267.8.2 Cybersecurity and Data Vulnerabilities 1267.8.3 Legal and Ethical Considerations 1267.8.4 Workforce Transition and Socio-Economic Impacts 1267.8.5 Interoperability and Standardization Issues 1277.9 Summary and Key Takeaways 127References 128SECTION 3Sustainability and Future Directions 1318 Digital Transformation in Sustainable Urban Freight 1338.1 Introduction 1338.2 Digital Transformation Tools for Sustainable Freight 1348.2.1 IoT and Real-Time Monitoring 1358.2.2 AI and Predictive Analytics 1358.2.3 Information and Communication Technology Platforms 1358.2.4 Digital Twins and Simulation Models 1358.2.5 Electrification and Smart Routing Integration 1368.2.6 Governance Through Digital Data 1368.3 UCCs and Micro-Hubs 1378.4 Low Emission and Zero Emission Zones 1378.5 Electric and Alternative Fuel Freight Vehicles 1418.5.1 Electric Freight Vehicles 1418.5.2 Hydrogen Fuel Cell Trucks 1418.5.3 Cargo Bikes and Non-Motorized Alternatives 1428.5.4 Barriers to Adoptionv1428.5.5 Policy and Market Enablers 1438.6 Integration with Smart City and ITS Frameworks 1438.6.1 UFCCs 1438.6.2 Digital Twins for Freight Planning 1438.6.3 ITS-Enabled Curbside and Network Management 1458.6.4 Integration with Broader Smart City Platforms 1458.6.5 Policy and Institutional Dimensions 1458.7 Case Studies: Sustainable Freight in Practice 1458.7.1 London’s Low Emission Freight Zone (LEZ and ULEZ) 1468.7.2 Paris’ UCCs 1468.7.3 New York City’s Off-Hour Delivery Programme 1478.7.4 Tokyo’s Electric and Hybrid Freight Fleet Initiatives 1478.7.5 Delhi’s Electric Freight Policy Implementation 1478.8 Challenges, Barriers, and Enablers of Sustainable Urban Freight 1478.8.1 Technical and Infrastructure Challenges 1478.8.2 Financial and Market Barriers 1488.8.3 Institutional and Governance Constraints 1488.8.4 Operational and Behavioural Challenges 1488.8.5 Social and Equity Dimensions 1498.8.6 Role of Technology and Digital Enablers 1498.9 Summary and Key Takeaways 149References 1509 Economic and Financial Dimensions of ITS in Freight Logistics 1559.1 Introduction 1559.2 Conceptual Underpinnings of Economic Impacts 1569.3 Dimensions of Economic Impacts 1579.3.1 Cost Reduction and Operational Efficiency 1589.3.2 Productivity Gains and Service Reliability 1599.3.3 Investment and Return on Technology 1609.3.4 Market Competitiveness and Innovation 1609.4 Regional and Sectoral Perspectives 1619.5 Case Studies 1659.5.1 Case 1: DHL Smart Logistics (Europe) 1659.5.2 Case 2: UPS ORION Routing (United States) 1669.5.3 E-commerce and FASTag (India) 1679.5.4 Comparative Synthesis 1689.6 Challenges in Realizing Economic Benefits 1699.6.1 High Capital Expenditure 1699.6.2 Uneven Returns and Scale Effects 1699.6.3 Digital Divide and Data Gaps 1699.6.4 Short-Term Disruptions and Transition Costs 1699.6.5 Risks of Over-Automation and Sunk Costs 1709.7 Summary and Key Takeaways 170References 17110 Pathways for ITS Adoption in Developing Economies 17410.1 Introduction 17410.2 Barriers in Developing Economies 17510.2.1 Infrastructure Gaps 17510.2.2 Fragmented Governance and Policy Misalignment 17510.2.3 Financial Constraints and Limited Investment 17610.2.4 Institutional Weaknesses and Capacity Deficits 17610.2.5 Regulatory Gaps and Informal Logistics Dominance 17610.2.6 Social and Cultural Constraints 17610.3 Enablers of Adoption 17710.3.1 Digital Transformation and Technology Diffusion 17710.3.2 Low-Cost Innovations and Local Adaptations 17710.3.3 Policy Enablers and Open Standards 17910.3.4 Financing Pathways: Public Private Partnerships,Value Capture, and Risk Sharing 18010.3.5 Capacity and Skills as Enablers 18010.3.6 Global Best Practices and Knowledge Transfer 18010.3.7 Synthesis of Enablers 18110.4 Institutional and Governance Pathways 18110.4.1 Integrated Institutional Structures 18110.4.2 Regulatory Governance and Enforcement 18210.4.3 Financing Reforms and Sustainability 18210.4.4 PPP Governance 18210.4.5 Institutional Capacity and Change Management 18210.4.6 Feedback Loops and Adaptive Governance 18310.5 Regional Case Studies 18310.5.1 India: FASTag and EV Freight 18310.5.2 Africa: Smart Pilots and Corridors 18410.5.3 Latin America: Consolidation and Regulation 18510.5.4 Comparative Synthesis 18510.6 Summary and Key Takeaways 185References 186Index 189