Smart Freight Logistics explains how digital and data-driven tools are reshaping freight logistics from urban deliveries to regional and national supply chains.
Readers will gain comprehensive understanding of digital freight transformation through structured analysis of real-world ITS applications, case studies, and analytical frameworks. The book delivers practical insights by examining technology integration challenges, showcasing successful implementations across global logistics environments, and providing decision-making tools for optimizing freight operations. Key topics include last-mile delivery optimization, urban freight management, autonomous vehicles, blockchain for supply chain transparency, and sustainable logistics strategies. Through these elements, readers will develop skills to reduce operational costs, enhance supply chain visibility, improve resilience against disruptions, implement data-driven logistics management, and design more sustainable freight systems adaptable to rapidly changing market conditions.
This book serves postgraduate students in Transport Planning, Logistics, and Supply Chain Management programmes, along with researchers and industry professionals seeking evidence-based solutions for modern freight logistics challenges.
Table of Contents:
List of Figures xiii
List of Tables xiv
Preface xv
Acknowledgements xvii
List of Abbreviations xviii
SECTION 1
Foundations of ITS in Freight Logistics 1
1 The Role of Intelligent Transport Systems in
Modern Freight Supply Chains 3
1.1 Introduction 3
1.2 Evolution of Freight and Logistics Systems 4
1.3 Understanding ITS 5
1.4 ITS in Modern Supply Chains 8
1.5 Key Components and Technologies in ITS for Freight 10
1.5.1 Vehicle Telematics and Fleet Monitoring 10
1.5.2 GNSS, AVL, and Real-Time Tracking 12
1.5.3 V2I and V2V Communication Technologies 12
1.5.4 Digital Freight Platforms and Aggregators 12
1.5.5 Geofencing and Access Management Systems 12
1.5.6 Smart Parking and Loading Bay Management 13
1.5.7 Electronic Proof of Delivery 13
1.5.8 Urban Freight Control Centres 13
1.5.9 AI-Based Freight Decision Support Systems 13
1.5.10 Blockchain for Freight Security and Traceability 14
1.6 Integration of ITS with Urban Planning 14
1.7 Global Best Practices in Freight ITS Implementation 15
1.8 Challenges and Barriers to ITS Adoption 15
1.8.1 Infrastructure and Technological Gaps 16
1.8.2 High Capital and Operational Costs 16
1.8.3 Data Privacy, Security, and Ownership Issues 16
1.8.4 Institutional Fragmentation and Regulatory Delays 16
1.8.5 Behavioural and Skill Barriers 17
1.8.6 Limited Demonstration Projects and Evidence Base 17
1.9 Summary and Key Takeaways 17
References 18
2 ITS Solutions for Last-Mile Delivery and E-Commerce 22
2.1 Introduction 22
2.2 ITS Applications for Last-Mile Delivery 23
2.3 Digital Technologies Enabling E-Commerce Logistics 24
2.4 Comparative Approaches: E-Commerce vs
Q-Commerce ITS Needs 25
2.5 ITS-Integrated Emerging Delivery Models 26
2.5.1 Micro-Hubs 27
2.5.2 Drone Deliveries 27
2.5.3 Autonomous Delivery Robots 27
2.6 Case Studies of ITS in Last-Mile Freight 29
2.6.1 DHL’s Sensor-Driven Parcel Locker
Network, Germany 29
2.6.2 Yamato Transport’s Dynamic Route
Optimization, Japan 30
2.6.3 Flipkart’s AI-Powered Predictive Routing, India 30
2.6.4 Barcelona’s Micro-Distribution Centres
with ITS Integration, Spain 31
2.6.5 Autonomous Delivery Robots in Singapore’s
Business Districts 31
2.6.6 Comparative Insights 31
2.7 Challenges in Adoption and Governance 32
2.8 Future Outlook and Innovation Pathways 33
2.9 Summary and Key Takeaways 34
References 35
3 Urban Freight Management with ITS 37
3.1 Introduction 37
3.2 Architecture of Integrated Freight ITS 38
3.3 Data Flow and Interoperability Standards 40
3.3.1 Structure of Data Flow in Freight ITS 40
3.3.2 Role of Interoperability Frameworks 40
3.3.3 Security and Reliability in Data Exchange 40
3.4 Optimization Models in Freight ITS 41
3.4.1 Core Vehicle Routing Formulation 41
3.4.2 Time Windows and Service Times 43
3.4.3 Energy Aware or Range-Constrained Routing 44
3.4.4 Fleet Assignment as a Network Flow 44
3.4.5 Scheduling with Tardiness or Make Span Objectives 44
3.4.6 Demand Forecasting with ARIMA and
Count Models 44
3.4.7 Predictive Control with Reinforcement Learning 45
3.4.8 Multi-Objective Cost and Emission Optimization 45
3.5 Decision Support Systems for Freight Planning 45
3.5.1 Structure and Core Components 46
3.5.2 Multi-Criteria Decision-Making in Freight Planning 46
3.5.3 Integration with Predictive and Prescriptive Analytics 47
3.6 Integration with Urban Mobility and Smart City Systems 49
3.7 Case Studies of Integrated ITS Freight Solutions 50
3.8 Barriers to Integration and Optimization 53
3.9 Summary and Key Takeaways 54
References 54
SECTION 2
Emerging Technologies in Freight Logistics 59
4 Digital Twins in Freight and Supply Chains 61
4.1 Introduction 61
4.2 Concept and Evolution of Digital Twins 62
4.3 Framework for Digital Twin Implementation in Freight 63
4.3.1 Data Acquisition 63
4.3.2 Integration Platforms 63
4.3.3 Analytics Engines 63
4.3.4 Visualization Tools 64
4.4 Applications in Freight and Supply Chain Operations 64
4.5 Integration with Other ITS Technologies 66
4.6 Evidence and Models 67
4.6.1 Case-Based Evidence 67
4.6.2 Conceptual Predictive Freight Flow Framework 68
4.7 Implementation Challenges 69
4.8 Innovation and Future Trajectories 71
4.8.1 Data-Driven Predictive Freight Ecosystems 71
4.8.2 Autonomous and Semi-Autonomous Freight Operations 71
4.8.3 Integration of Green and Circular Logistics Principles 71
4.8.4 Hyperconnected Multi-Modal Freight Networks 72
4.8.5 Policy, Governance, and Institutional Transformation 73
4.8.6 Research and Development Priorities 73
4.9 Summary and Key Takeaways 73
References 74
5 Big Data Analytics in Freight ITS 76
5.1 Introduction 76
5.2 Data Sources in Freight ITS 77
5.3 Data Collection, Storage, and Processing Frameworks 81
5.3.1 Data Collection 81
5.3.2 Data Storage 81
5.3.3 Data Processing 82
5.4 Analytical Techniques in Freight ITS 83
5.4.1 Descriptive Analytics 83
5.4.2 Predictive Analytics 84
5.4.3 Prescriptive Analytics 84
5.4.4 Comparative Perspective 85
5.5 Big Data–Driven DSS 85
5.6 Applications in Urban Freight and Last-Mile Delivery 87
5.6.1 Real-Time Traffic Prediction 87
5.6.2 Dynamic Load Balancing and Fleet Reallocation 87
5.6.3 Delivery Time Window Optimization 88
5.6.4 Sustainable and Low-Emission Freight Routing 88
5.6.5 On-Demand Delivery Orchestration 88
5.6.6 Predictive Maintenance for Urban Fleets 88
5.6.7 Warehouse and Micro-Fulfilment Optimization 89
5.6.8 Crowdshipping and Platform-Based Logistics 89
5.7 Case Studies 89
5.7.1 Case 1: India Big Data in E-Commerce
Fleet Analytics 90
5.7.2 Case 2: Port of Rotterdam Digital Twin 90
5.8 Challenges and Risks 91
5.8.1 Data Privacy and Ownership Concerns 91
5.8.2 Interoperability Issues in Heterogeneous ITS Systems 91
5.8.3 Infrastructure, Skills, and Cost Barriers 92
5.8.4 Cybersecurity Risks 92
5.8.5 Organizational Resistance and Change Management 92
5.9 Summary and Key Takeaways 92
References 93
6 Blockchain Applications in Freight ITS 97
6.1 Introduction 97
6.2 Fundamentals of Blockchain Technology for Logistics 99
6.3 Blockchain Applications in Freight ITS 100
6.3.1 Secure Data Exchange 100
6.3.2 End-to-End Visibility and Traceability 100
6.3.3 Automated Freight Payments and Settlements 100
6.3.4 Digital Identity for Vehicles, Shipments, and Assets 101
6.4 Integration with ITS 101
6.4.1 Blockchain and IoT Integration 101
6.4.2 Interoperability with Big Data Analytics 103
6.4.3 Blockchain in ITS-Enabled Policy Mechanisms 103
6.4.4 Towards a Unified Freight ITS Ecosystem 103
6.5 Case Studies 104
6.5.1 Case 1: India – TradeLens at Major Ports 104
6.5.2 Case 2: Germany – DHL’s Pharmaceutical Cold Chain Pilot 105
6.6 Challenges in Adoption 105
6.6.1 Technical Challenges 105
6.6.2 Regulatory Challenges 106
6.6.3 Organizational Challenges 107
6.7 Future Pathways 107
6.7.1 Blockchain and AI for Predictive Freight 107
6.7.2 Blockchain and IoT for Automated Compliance 108
6.7.3 Decentralized Freight Marketplaces 108
6.7.4 Policy and Sustainability Dimensions 108
6.8 Summary and Key Takeaways 109
References 110
7 Autonomous Freight Systems and Logistics Automation 113
7.1 Introduction 113
7.2 Evolution of Automation in Freight Logistics 114
7.3 Core Technologies Behind Freight Automation 116
7.3.1 LIDAR 116
7.3.2 Radar and Ultrasonic Sensors 116
7.3.3 Computer Vision 116
7.3.4 AI/ML 116
7.3.5 V2X Communication 117
7.3.6 Digital Twins and Simulation 17
7.4 AFVs 117
7.4.1 Long-Haul Autonomous Trucks 119
7.4.2 Middle-Mile and Urban Delivery Vans 119
7.4.3 Last-Mile Delivery Robots and Drones 119
7.4.4 Regulatory and Safety Considerations 120
7.5 Integration with ITS 120
7.5.1 Real-Time Traffic Coordination 120
7.5.2 Freight Signal Priority 120
7.5.3 Truck Platooning and Cooperative Driving 120
7.5.4 Connection to Logistics Platforms 121
7.5.5 Data Sharing and Cybersecurity 121
7.6 Robotics in Freight Terminals and Warehouses 121
7.6.1 AGVs and Autonomous Mobile Robots 121
7.6.2 Drones for Inventory and Delivery 121
7.6.3 Cobots 123
7.6.4 Automated Storage and Retrieval Systems 123
7.6.5 Autonomous Forklifts, Robotic Arms,
and Palletizing Robots 123
7.6.6 Robotic Sortation Systems and Conveyors 123
7.6.7 Swarm Robotics and Digital Twins 123
7.6.8 Vision-Based Picking Robots 124
7.6.9 Exoskeletons (Wearable Robotics) 124
7.7 Case Studies of Freight Automation 124
7.7.1 U.S. Autonomous Trucking Pilots 124
7.7.2 Warehouse Robotics in Europe and Asia 125
7.8 Challenges and Risks 125
7.8.1 Technical Limitations and Infrastructure Readiness 126
7.8.2 Cybersecurity and Data Vulnerabilities 126
7.8.3 Legal and Ethical Considerations 126
7.8.4 Workforce Transition and Socio-Economic Impacts 126
7.8.5 Interoperability and Standardization Issues 127
7.9 Summary and Key Takeaways 127
References 128
SECTION 3
Sustainability and Future Directions 131
8 Digital Transformation in Sustainable Urban Freight 133
8.1 Introduction 133
8.2 Digital Transformation Tools for Sustainable Freight 134
8.2.1 IoT and Real-Time Monitoring 135
8.2.2 AI and Predictive Analytics 135
8.2.3 Information and Communication Technology Platforms 135
8.2.4 Digital Twins and Simulation Models 135
8.2.5 Electrification and Smart Routing Integration 136
8.2.6 Governance Through Digital Data 136
8.3 UCCs and Micro-Hubs 137
8.4 Low Emission and Zero Emission Zones 137
8.5 Electric and Alternative Fuel Freight Vehicles 141
8.5.1 Electric Freight Vehicles 141
8.5.2 Hydrogen Fuel Cell Trucks 141
8.5.3 Cargo Bikes and Non-Motorized Alternatives 142
8.5.4 Barriers to Adoptionv142
8.5.5 Policy and Market Enablers 143
8.6 Integration with Smart City and ITS Frameworks 143
8.6.1 UFCCs 143
8.6.2 Digital Twins for Freight Planning 143
8.6.3 ITS-Enabled Curbside and Network Management 145
8.6.4 Integration with Broader Smart City Platforms 145
8.6.5 Policy and Institutional Dimensions 145
8.7 Case Studies: Sustainable Freight in Practice 145
8.7.1 London’s Low Emission Freight Zone (LEZ and ULEZ) 146
8.7.2 Paris’ UCCs 146
8.7.3 New York City’s Off-Hour Delivery Programme 147
8.7.4 Tokyo’s Electric and Hybrid Freight Fleet Initiatives 147
8.7.5 Delhi’s Electric Freight Policy Implementation 147
8.8 Challenges, Barriers, and Enablers of Sustainable Urban Freight 147
8.8.1 Technical and Infrastructure Challenges 147
8.8.2 Financial and Market Barriers 148
8.8.3 Institutional and Governance Constraints 148
8.8.4 Operational and Behavioural Challenges 148
8.8.5 Social and Equity Dimensions 149
8.8.6 Role of Technology and Digital Enablers 149
8.9 Summary and Key Takeaways 149
References 150
9 Economic and Financial Dimensions of ITS in Freight Logistics 155
9.1 Introduction 155
9.2 Conceptual Underpinnings of Economic Impacts 156
9.3 Dimensions of Economic Impacts 157
9.3.1 Cost Reduction and Operational Efficiency 158
9.3.2 Productivity Gains and Service Reliability 159
9.3.3 Investment and Return on Technology 160
9.3.4 Market Competitiveness and Innovation 160
9.4 Regional and Sectoral Perspectives 161
9.5 Case Studies 165
9.5.1 Case 1: DHL Smart Logistics (Europe) 165
9.5.2 Case 2: UPS ORION Routing (United States) 166
9.5.3 E-commerce and FASTag (India) 167
9.5.4 Comparative Synthesis 168
9.6 Challenges in Realizing Economic Benefits 169
9.6.1 High Capital Expenditure 169
9.6.2 Uneven Returns and Scale Effects 169
9.6.3 Digital Divide and Data Gaps 169
9.6.4 Short-Term Disruptions and Transition Costs 169
9.6.5 Risks of Over-Automation and Sunk Costs 170
9.7 Summary and Key Takeaways 170
References 171
10 Pathways for ITS Adoption in Developing Economies 174
10.1 Introduction 174
10.2 Barriers in Developing Economies 175
10.2.1 Infrastructure Gaps 175
10.2.2 Fragmented Governance and Policy Misalignment 175
10.2.3 Financial Constraints and Limited Investment 176
10.2.4 Institutional Weaknesses and Capacity Deficits 176
10.2.5 Regulatory Gaps and Informal Logistics Dominance 176
10.2.6 Social and Cultural Constraints 176
10.3 Enablers of Adoption 177
10.3.1 Digital Transformation and Technology Diffusion 177
10.3.2 Low-Cost Innovations and Local Adaptations 177
10.3.3 Policy Enablers and Open Standards 179
10.3.4 Financing Pathways: Public Private Partnerships,
Value Capture, and Risk Sharing 180
10.3.5 Capacity and Skills as Enablers 180
10.3.6 Global Best Practices and Knowledge Transfer 180
10.3.7 Synthesis of Enablers 181
10.4 Institutional and Governance Pathways 181
10.4.1 Integrated Institutional Structures 181
10.4.2 Regulatory Governance and Enforcement 182
10.4.3 Financing Reforms and Sustainability 182
10.4.4 PPP Governance 182
10.4.5 Institutional Capacity and Change Management 182
10.4.6 Feedback Loops and Adaptive Governance 183
10.5 Regional Case Studies 183
10.5.1 India: FASTag and EV Freight 183
10.5.2 Africa: Smart Pilots and Corridors 184
10.5.3 Latin America: Consolidation and Regulation 185
10.5.4 Comparative Synthesis 185
10.6 Summary and Key Takeaways 185
References 186
Index 189
About the Author :
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.
