Eiichi Taniguchi, Kyoto University, Japan. Russell G. Thompson, The University of Melbourne, Australia.

• Preface xvChapter 1. Urban Logistics Spaces: What Models, What Uses and What Role for Public Authorities? 1Danièle PATIER and Florence TOILIER1.1. Introduction 11.2. Literature review 21.3. ULS typology  . 41.3.1. The Urban Logistics Zone (ULZ) or freight village 41.3.2. The Urban Distribution Center (UDC) 61.3.3. Vehicle Reception Points (VRP) 91.3.4. Goods Reception Points (GRP) 121.3.5. The Urban Logistics Box (ULB) 131.3.6. Mobile Urban Logistics Spaces (mULS) 151.4. Recommendations 181.5. Conclusion 191.6. Bibliography 20Chapter 2. Dynamic Management of Urban Last-Mile Deliveries 23Tomislav LETNIK, Matej MENCINGER and Stane BOZICNIK2.1. Introduction 232.2. Review of urban freight loading bay problems and solutions 252.3. Information system for dynamic management of urban last-mile deliveries 262.4. Algorithm for dynamic management of urban freight deliveries 292.5. Application of the model to a real case 322.6. Conclusions 332.7. Bibliography 34Chapter 3. Stakeholders’ Roles for Business Modeling in a City Logistics Ecosystem: Towards a Conceptual Model 39Giovanni ZENEZINI, J.H.R. VAN DUIN, Lorant TAVASSZY and Alberto DE MARCO3.1. Introduction 393.2. Research background 413.2.1. Business model concept 413.2.2. Business ecosystem 423.2.3. Role-based networks and ecosystems 433.3. The CL business model framework: roles, business entities and value exchanges 433.4. City logistics concepts and role assignment 483.4.1. Parcel lockers installation: MyPUP 483.4.2. Urban consolidation centers 513.4.3. Business model implications 543.5. Conclusions 553.6. Bibliography 56Chapter 4. Establishing a Robust Urban Logistics Network at FEMSA through Stochastic Multi-Echelon Location Routing 59André SNOECK, Matthias WINKENBACH and Esteban E. MASCARINO4.1. Introduction 594.2. Strategic distribution network design 624.2.1. Distribution network 634.2.2. Network cost 634.2.3. Distribution cost 644.2.4. Optimization model 654.3. Solution scheme 674.3.1. Scenario generation and selection 674.3.2. Design generation 684.3.3. Design evaluation 684.4. Case study 684.4.1. Data and parameters 694.4.2. Analysis results 704.5. Results 714.5.1. Design generation 714.5.2. Design evaluation 724.5.3. Sensitivity to cost of lost sales 734.6. Conclusion 754.7. Bibliography 75Chapter 5. An Evaluation Model of Operational and Cost Impacts of Off-Hours Deliveries in the City of São Paulo, Brazil 79Cláudio B. CUNHA and Hugo T.Y. YOSHIZAKI5.1. Introduction 795.2. Literature review 815.3. Proposed approach 845.4. Scenario generation 875.5. Results 905.6. Concluding remarks 945.7. Bibliography 94Chapter 6. Application of the Bi-Level Location-Routing Problem for Post-Disaster Waste Collection 97Cheng CHENG, Russell G. THOMPSON, Alysson M. COSTA and Xiang HUANG6.1. Introduction 976.2. Model formulation 996.3. Solution algorithm 1046.3.1. Genetic Algorithms 1046.3.2. Greedy Algorithm 1056.3.3. Simulated Annealing 1066.4. Case study 1066.4.1. Case study area 1066.5. Result analysis 1096.5.1. Models comparison 1096.5.2. Sensitivity analysis 1116.6. Conclusion 1136.7. Bibliography 114Chapter 7. Next-Generation Commodity Flow Survey: A Pilot in Singapore 117Lynette CHEAH, Fang ZHAO, Monique STINSON, Fangping LU, Jing DING-MASTERA, Vittorio MARZANO, and Moshe BEN-AKIVA7.1. Introduction 1177.2. Integrated commodity flow survey 1197.2.1. Overview 1197.3. Key survey features 1217.3.1. Sampling related supply network entities 1217.3.2. Multiple survey instruments leveraging sensing technologies 1217.3.3. A unified web-based survey platform 1227.4. Pilot survey implementation 1237.4.1. Sample design and recruitment 1247.4.2. Shipment and vehicle tracking methods 1257.4.3. Pilot survey experience and lessons learnt 1267.4.4. Preliminary data analysis 1277.5. Conclusion 1297.6. Acknowledgements 1297.7. Bibliography 130Chapter 8. City Logistics and Clustering: Impacts of Using HDI and Taxes 131Rodrigo Barros CASTRO, Daniel MERCH N, Orlando Fontes LIMA JR and Matthias WINKENBACH8.1. Introduction 1318.2. Methodology 1338.2.1. Principal component analysis 1358.2.2. K-means clustering 1358.3. Results 1358.4. Conclusion 1408.5. Bibliography 140Chapter 9. Developing a Multi-Dimensional Poly-Parametric Typology for City Logistics 143Paulus ADITJANDRA and Thomas ZUNDER9.1. Introduction 1439.2. Literature review 1449.3. Methodology 1459.4. Evaluation and analysis 1469.4.1. Inventory of all EU projects 1469.4.2. Inventory of typologies 1479.4.3. Land use typologies 1489.4.4. Measure typologies 1499.4.5. Urban freight markets 1519.4.6. Traffic flow typology 1529.4.7. Impacts 1539.4.8. Gaps 1539.5. Validation and enhancement of the inventory 1549.6. Proposed typology 1559.6.1. Approach 1559.6.2. Dimension: Why? 1579.6.3. Dimension: Where? 1579.6.4. Dimension: Who? 1589.6.5. Dimension: What? 1589.6.6. Dimension: How? 1599.7. Reflections 1599.8. Conclusion 1609.9. Acknowledgements 1609.10. Bibliography 160Chapter 10. Multi-agent Simulation with Reinforcement Learning for Evaluating a Combination of City Logistics Policy Measures 165Eiichi TANIGUCHI, Ali Gul QURESHI and Kyosuke KONDA10.1. Introduction 16510.2. Literature review 16610.3. Models 16610.4. Case studies in Osaka and Motomachi 16810.4.1. Settings 16810.4.2. Results 17010.5. Conclusion 17510.6. Bibliography 176Chapter 11. Decision Support System for an Urban Distribution Center Using Agent-based Modeling: A Case Study of Yogyakarta Special Region Province, Indonesia 179Bertha Maya SOPHA, Anna Maria Sri ASIH, Hanif Arkan NURDIANSYAH and Rahma MAULIDA11.1. Introduction 17911.2. Theoretical background 18211.2.1. Urban distribution center 18211.2.2. Decision support system of city logistics 18311.3. The proposed decision support system 18411.3.1. System characterization 18411.3.2. The logical architecture 18511.3.3. Agent-based modeling (ABM) 18711.3.4. Model verification and validation 19011.4. Example of application: the case of Yogyakarta Special Region 19111.5. Conclusion 19211.6. Acknowledgements 19311.7. Bibliography 194Chapter 12. Evaluating the Relocation of an Urban Container Terminal 197Johan W. JOUBERT12.1. Introduction 19712.2. Methodology 19912.2.1. MATSim 19912.2.2. Initial demand 20012.2.3. Alternative scenarios 20112.3. Results 20112.3.1. Directly affected vehicles 20212.3.2. Extended effects 20512.4. Conclusion 20812.5. Acknowledgements 20912.6. Bibliography 209Chapter 13. Multi-Agent Simulation Using Adaptive Dynamic Programing for Evaluating Urban Consolidation Centers 211Nailah FIRDAUSIYAH, Eiichi TANIGUCHI and Ali Gul QURESHI13.1. Introduction 21113.2. Literature review 21213.2.1. Evaluation models for city logistics measures 21213.2.2. ADP for evaluating city logistics measures 21313.3. Models 21413.3.1. Freight carrier’s MAS-ADP model 21513.3.2. Freight carrier’s MAS Q-learning model 21713.3.3. Vehicle routing problem with soft time windows (VRPSSTW) 21813.4. Case study 22013.5. Results and discussions 22113.5.1. Case 0 (base case) 22213.5.2. Case 1 22313.6. Conclusion and future work 22613.7. Bibliography 226Chapter 14. Use Patterns and Preferences for Charging Infrastructure for Battery Electric Vehicles in Commercial Fleets in the Hamburg Metropolitan Region 229Christian BLUSCH, Heike FLÄMIG and Sören Christian TRÜMPER14.1. Introduction 22914.2. State of the art/context of study 23014.3. Research goal and approach 23114.4. Method of data collection 23214.5. Results and discussion 23214.6. Conclusions 23714.7. Acknowledgements 23814.8. Bibliography 238Chapter 15. The Potential of Light Electric Vehicles for Specific Freight Flows: Insights from the Netherlands 241Susanne BALM, Ewoud MOOLENBURGH, Nilesh ANAND andWalther PLOOS VAN AMSTEL15.1. Introduction 24115.2. Definition of LEFV 24315.3. State of the art 24415.4. Methodology 24615.5. Potential of LEFV for different freight flows 24715.5.1. Selection of freight flows 24715.5.2. Description of freight flows 24815.5.3. Receivers’ perspective 25315.6. Multi-criteria evaluation 25315.6.1. Setup 25315.6.2. Outcome 25415.7. Discussion 25615.8. Conclusion 25715.9. Acknowledgements 25815.10. Bibliography 259Chapter 16. Use of CNG for Urban Freight Transport: Comparisons Between France and Brazil 261Leise Kelli DE OLIVEIRA and Diana DIZIAIN16.1. Introduction 26116.2. Brief literature review 26316.3. Methodology 26416.4. Brazilian case 26416.5. French case 26516.6. Comparison of Brazilian and French experience 26716.7. Conclusion 26816.8. Acknowledgements 26816.9. Bibliography 268Chapter 17. Using Cost–Benefit Analysis to Evaluate City Logistics Initiatives: An Application to Freight Consolidation in Small- and Mid-Sized Urban Areas 271Johan HOLMGREN17.1. Introduction 27117.2. Characteristics of city logistics and some terminology 27317.2.1. Efficiency in city logistics 27417.2.2. Evaluation methods 27517.3. Potential costs and benefits of implementing urban consolidation centers 27917.4. Coordinated freight distribution in Linköping 28017.5. Evaluating urban freight initiatives by cost–benefit analysis 28117.6. The problem of cost allocation 28617.7. Conclusion 28617.8. Bibliography 287Chapter 18. Assumptions of Social Cost–Benefit Analysis for Implementing Urban Freight Transport Measures 291Izabela KOTOWSKA, Stanisław IWAN, Kinga KIJEWSKA and Mariusz JEDLIŃSKI18.1. Introduction 29118.2. The assumptions for utilization of SCBA in city logistics 29518.2.1. External air pollution cost 29618.2.2. Marginal climate change costs 29918.2.3. Marginal accident costs 30118.2.4. Congestion costs 30218.2.5. Marginal external noise costs 30418.2.6. Employment growth and development of local economy 30518.2.7. Final calculations 30818.3. Conclusions 31018.4. Acknowledgements 31018.5. Bibliography 310Chapter 19. Barriers to the Adoption of an Urban Logistics Collaboration Process: A Case Study of the Saint-Etienne Urban Consolidation Centre 313Kanyarat NIMTRAKOOL, Jesus GONZALEZ-FELIU and Claire CAPO19.1. Introduction 31319.2. Background and theoretical framework 31519.2.1. The stakeholders in an urban logistics collaboration project 31519.2.2. Urban Consolidation Centre (UCC) as an organizational innovation 31619.2.3. Barriers in urban logistics projects 31819.3. Research methodology 32019.3.1. The research approach 32019.3.2. Qualitative study: selection of respondents 32019.3.3. Quantitative analysis: purpose and CBA methodology 32119.4. Results 32219.4.1. The UCC of Saint-Etienne: background and objectives 32219.4.2. Operation aspects 32319.4.3. The conditions of economic viability of Saint-Etienne’s UCC 32419.4.4. Barriers identified by stakeholders 32619.5. Conclusions 32819.6. Bibliography 328Chapter 20. Logistics Sprawl Assessment Applied to Locational Planning: A Case Study in Palmas (Brazil) 333Lilian dos Santos Fontes Pereira BRACARENSE, Thiago Alvares ASSIS, Leise Kelli DE OLIVEIRA and Renata Lúcia Magalhães DE OLIVEIRA20.1. Introduction 33320.2. Logistics sprawl and the importance of logistics facilities’ location 33420.3. Methodology 33520.4. Area of study 33920.4.1. Logistics sprawl assessment and scenario comparison 34220.5. Conclusion 34720.6. Acknowledgements 34820.7. Bibliography 348Chapter 21. Are Cities’ Delivery Spaces in the Right Places? Mapping Truck Load/Unload Locations 351Anne GOODCHILD, Barb IVANOV, Ed MCCORMACK, Anne MOUDON, Jason SCULLY, José Machado LEON and Gabriela GIRON VALDERRAMA21.1. Introduction 35121.2. Moving more goods, more quickly 35221.3. Establishment of a well-defined partnership 35321.4. The Final 50 Feet project 35421.5. Getting granular 35621.6. Mapping the city’s freight delivery infrastructure 35821.6.1. Step 1: collect existent data 35821.6.2. Step 2: develop survey to collect freight bay and loading dock data 35821.6.3. Preliminary site visits 35921.6.4. Initial survey form and the pilot survey 36021.6.5. Step 3: implement the survey 36321.7. Research results 36621.8. Conclusion 36821.9. Bibliography 368List of Authors 369Index 375

Eiichi Taniguchi, Kyoto University, Japan. Russell G. Thompson, The University of Melbourne, Australia.

• Preface xvChapter 1. Urban Logistics Spaces: What Models, What Uses and What Role for Public Authorities? 1Danièle PATIER and Florence TOILIER1.1. Introduction 11.2. Literature review 21.3. ULS typology  . 41.3.1. The Urban Logistics Zone (ULZ) or freight village 41.3.2. The Urban Distribution Center (UDC) 61.3.3. Vehicle Reception Points (VRP) 91.3.4. Goods Reception Points (GRP) 121.3.5. The Urban Logistics Box (ULB) 131.3.6. Mobile Urban Logistics Spaces (mULS) 151.4. Recommendations 181.5. Conclusion 191.6. Bibliography 20Chapter 2. Dynamic Management of Urban Last-Mile Deliveries 23Tomislav LETNIK, Matej MENCINGER and Stane BOZICNIK2.1. Introduction 232.2. Review of urban freight loading bay problems and solutions 252.3. Information system for dynamic management of urban last-mile deliveries 262.4. Algorithm for dynamic management of urban freight deliveries 292.5. Application of the model to a real case 322.6. Conclusions 332.7. Bibliography 34Chapter 3. Stakeholders’ Roles for Business Modeling in a City Logistics Ecosystem: Towards a Conceptual Model 39Giovanni ZENEZINI, J.H.R. VAN DUIN, Lorant TAVASSZY and Alberto DE MARCO3.1. Introduction 393.2. Research background 413.2.1. Business model concept 413.2.2. Business ecosystem 423.2.3. Role-based networks and ecosystems 433.3. The CL business model framework: roles, business entities and value exchanges 433.4. City logistics concepts and role assignment 483.4.1. Parcel lockers installation: MyPUP 483.4.2. Urban consolidation centers 513.4.3. Business model implications 543.5. Conclusions 553.6. Bibliography 56Chapter 4. Establishing a Robust Urban Logistics Network at FEMSA through Stochastic Multi-Echelon Location Routing 59André SNOECK, Matthias WINKENBACH and Esteban E. MASCARINO4.1. Introduction 594.2. Strategic distribution network design 624.2.1. Distribution network 634.2.2. Network cost 634.2.3. Distribution cost 644.2.4. Optimization model 654.3. Solution scheme 674.3.1. Scenario generation and selection 674.3.2. Design generation 684.3.3. Design evaluation 684.4. Case study 684.4.1. Data and parameters 694.4.2. Analysis results 704.5. Results 714.5.1. Design generation 714.5.2. Design evaluation 724.5.3. Sensitivity to cost of lost sales 734.6. Conclusion 754.7. Bibliography 75Chapter 5. An Evaluation Model of Operational and Cost Impacts of Off-Hours Deliveries in the City of São Paulo, Brazil 79Cláudio B. CUNHA and Hugo T.Y. YOSHIZAKI5.1. Introduction 795.2. Literature review 815.3. Proposed approach 845.4. Scenario generation 875.5. Results 905.6. Concluding remarks 945.7. Bibliography 94Chapter 6. Application of the Bi-Level Location-Routing Problem for Post-Disaster Waste Collection 97Cheng CHENG, Russell G. THOMPSON, Alysson M. COSTA and Xiang HUANG6.1. Introduction 976.2. Model formulation 996.3. Solution algorithm 1046.3.1. Genetic Algorithms 1046.3.2. Greedy Algorithm 1056.3.3. Simulated Annealing 1066.4. Case study 1066.4.1. Case study area 1066.5. Result analysis 1096.5.1. Models comparison 1096.5.2. Sensitivity analysis 1116.6. Conclusion 1136.7. Bibliography 114Chapter 7. Next-Generation Commodity Flow Survey: A Pilot in Singapore 117Lynette CHEAH, Fang ZHAO, Monique STINSON, Fangping LU, Jing DING-MASTERA, Vittorio MARZANO, and Moshe BEN-AKIVA7.1. Introduction 1177.2. Integrated commodity flow survey 1197.2.1. Overview 1197.3. Key survey features 1217.3.1. Sampling related supply network entities 1217.3.2. Multiple survey instruments leveraging sensing technologies 1217.3.3. A unified web-based survey platform 1227.4. Pilot survey implementation 1237.4.1. Sample design and recruitment 1247.4.2. Shipment and vehicle tracking methods 1257.4.3. Pilot survey experience and lessons learnt 1267.4.4. Preliminary data analysis 1277.5. Conclusion 1297.6. Acknowledgements 1297.7. Bibliography 130Chapter 8. City Logistics and Clustering: Impacts of Using HDI and Taxes 131Rodrigo Barros CASTRO, Daniel MERCH N, Orlando Fontes LIMA JR and Matthias WINKENBACH8.1. Introduction 1318.2. Methodology 1338.2.1. Principal component analysis 1358.2.2. K-means clustering 1358.3. Results 1358.4. Conclusion 1408.5. Bibliography 140Chapter 9. Developing a Multi-Dimensional Poly-Parametric Typology for City Logistics 143Paulus ADITJANDRA and Thomas ZUNDER9.1. Introduction 1439.2. Literature review 1449.3. Methodology 1459.4. Evaluation and analysis 1469.4.1. Inventory of all EU projects 1469.4.2. Inventory of typologies 1479.4.3. Land use typologies 1489.4.4. Measure typologies 1499.4.5. Urban freight markets 1519.4.6. Traffic flow typology 1529.4.7. Impacts 1539.4.8. Gaps 1539.5. Validation and enhancement of the inventory 1549.6. Proposed typology 1559.6.1. Approach 1559.6.2. Dimension: Why? 1579.6.3. Dimension: Where? 1579.6.4. Dimension: Who? 1589.6.5. Dimension: What? 1589.6.6. Dimension: How? 1599.7. Reflections 1599.8. Conclusion 1609.9. Acknowledgements 1609.10. Bibliography 160Chapter 10. Multi-agent Simulation with Reinforcement Learning for Evaluating a Combination of City Logistics Policy Measures 165Eiichi TANIGUCHI, Ali Gul QURESHI and Kyosuke KONDA10.1. Introduction 16510.2. Literature review 16610.3. Models 16610.4. Case studies in Osaka and Motomachi 16810.4.1. Settings 16810.4.2. Results 17010.5. Conclusion 17510.6. Bibliography 176Chapter 11. Decision Support System for an Urban Distribution Center Using Agent-based Modeling: A Case Study of Yogyakarta Special Region Province, Indonesia 179Bertha Maya SOPHA, Anna Maria Sri ASIH, Hanif Arkan NURDIANSYAH and Rahma MAULIDA11.1. Introduction 17911.2. Theoretical background 18211.2.1. Urban distribution center 18211.2.2. Decision support system of city logistics 18311.3. The proposed decision support system 18411.3.1. System characterization 18411.3.2. The logical architecture 18511.3.3. Agent-based modeling (ABM) 18711.3.4. Model verification and validation 19011.4. Example of application: the case of Yogyakarta Special Region 19111.5. Conclusion 19211.6. Acknowledgements 19311.7. Bibliography 194Chapter 12. Evaluating the Relocation of an Urban Container Terminal 197Johan W. JOUBERT12.1. Introduction 19712.2. Methodology 19912.2.1. MATSim 19912.2.2. Initial demand 20012.2.3. Alternative scenarios 20112.3. Results 20112.3.1. Directly affected vehicles 20212.3.2. Extended effects 20512.4. Conclusion 20812.5. Acknowledgements 20912.6. Bibliography 209Chapter 13. Multi-Agent Simulation Using Adaptive Dynamic Programing for Evaluating Urban Consolidation Centers 211Nailah FIRDAUSIYAH, Eiichi TANIGUCHI and Ali Gul QURESHI13.1. Introduction 21113.2. Literature review 21213.2.1. Evaluation models for city logistics measures 21213.2.2. ADP for evaluating city logistics measures 21313.3. Models 21413.3.1. Freight carrier’s MAS-ADP model 21513.3.2. Freight carrier’s MAS Q-learning model 21713.3.3. Vehicle routing problem with soft time windows (VRPSSTW) 21813.4. Case study 22013.5. Results and discussions 22113.5.1. Case 0 (base case) 22213.5.2. Case 1 22313.6. Conclusion and future work 22613.7. Bibliography 226Chapter 14. Use Patterns and Preferences for Charging Infrastructure for Battery Electric Vehicles in Commercial Fleets in the Hamburg Metropolitan Region 229Christian BLUSCH, Heike FLÄMIG and Sören Christian TRÜMPER14.1. Introduction 22914.2. State of the art/context of study 23014.3. Research goal and approach 23114.4. Method of data collection 23214.5. Results and discussion 23214.6. Conclusions 23714.7. Acknowledgements 23814.8. Bibliography 238Chapter 15. The Potential of Light Electric Vehicles for Specific Freight Flows: Insights from the Netherlands 241Susanne BALM, Ewoud MOOLENBURGH, Nilesh ANAND andWalther PLOOS VAN AMSTEL15.1. Introduction 24115.2. Definition of LEFV 24315.3. State of the art 24415.4. Methodology 24615.5. Potential of LEFV for different freight flows 24715.5.1. Selection of freight flows 24715.5.2. Description of freight flows 24815.5.3. Receivers’ perspective 25315.6. Multi-criteria evaluation 25315.6.1. Setup 25315.6.2. Outcome 25415.7. Discussion 25615.8. Conclusion 25715.9. Acknowledgements 25815.10. Bibliography 259Chapter 16. Use of CNG for Urban Freight Transport: Comparisons Between France and Brazil 261Leise Kelli DE OLIVEIRA and Diana DIZIAIN16.1. Introduction 26116.2. Brief literature review 26316.3. Methodology 26416.4. Brazilian case 26416.5. French case 26516.6. Comparison of Brazilian and French experience 26716.7. Conclusion 26816.8. Acknowledgements 26816.9. Bibliography 268Chapter 17. Using Cost–Benefit Analysis to Evaluate City Logistics Initiatives: An Application to Freight Consolidation in Small- and Mid-Sized Urban Areas 271Johan HOLMGREN17.1. Introduction 27117.2. Characteristics of city logistics and some terminology 27317.2.1. Efficiency in city logistics 27417.2.2. Evaluation methods 27517.3. Potential costs and benefits of implementing urban consolidation centers 27917.4. Coordinated freight distribution in Linköping 28017.5. Evaluating urban freight initiatives by cost–benefit analysis 28117.6. The problem of cost allocation 28617.7. Conclusion 28617.8. Bibliography 287Chapter 18. Assumptions of Social Cost–Benefit Analysis for Implementing Urban Freight Transport Measures 291Izabela KOTOWSKA, Stanisław IWAN, Kinga KIJEWSKA and Mariusz JEDLIŃSKI18.1. Introduction 29118.2. The assumptions for utilization of SCBA in city logistics 29518.2.1. External air pollution cost 29618.2.2. Marginal climate change costs 29918.2.3. Marginal accident costs 30118.2.4. Congestion costs 30218.2.5. Marginal external noise costs 30418.2.6. Employment growth and development of local economy 30518.2.7. Final calculations 30818.3. Conclusions 31018.4. Acknowledgements 31018.5. Bibliography 310Chapter 19. Barriers to the Adoption of an Urban Logistics Collaboration Process: A Case Study of the Saint-Etienne Urban Consolidation Centre 313Kanyarat NIMTRAKOOL, Jesus GONZALEZ-FELIU and Claire CAPO19.1. Introduction 31319.2. Background and theoretical framework 31519.2.1. The stakeholders in an urban logistics collaboration project 31519.2.2. Urban Consolidation Centre (UCC) as an organizational innovation 31619.2.3. Barriers in urban logistics projects 31819.3. Research methodology 32019.3.1. The research approach 32019.3.2. Qualitative study: selection of respondents 32019.3.3. Quantitative analysis: purpose and CBA methodology 32119.4. Results 32219.4.1. The UCC of Saint-Etienne: background and objectives 32219.4.2. Operation aspects 32319.4.3. The conditions of economic viability of Saint-Etienne’s UCC 32419.4.4. Barriers identified by stakeholders 32619.5. Conclusions 32819.6. Bibliography 328Chapter 20. Logistics Sprawl Assessment Applied to Locational Planning: A Case Study in Palmas (Brazil) 333Lilian dos Santos Fontes Pereira BRACARENSE, Thiago Alvares ASSIS, Leise Kelli DE OLIVEIRA and Renata Lúcia Magalhães DE OLIVEIRA20.1. Introduction 33320.2. Logistics sprawl and the importance of logistics facilities’ location 33420.3. Methodology 33520.4. Area of study 33920.4.1. Logistics sprawl assessment and scenario comparison 34220.5. Conclusion 34720.6. Acknowledgements 34820.7. Bibliography 348Chapter 21. Are Cities’ Delivery Spaces in the Right Places? Mapping Truck Load/Unload Locations 351Anne GOODCHILD, Barb IVANOV, Ed MCCORMACK, Anne MOUDON, Jason SCULLY, José Machado LEON and Gabriela GIRON VALDERRAMA21.1. Introduction 35121.2. Moving more goods, more quickly 35221.3. Establishment of a well-defined partnership 35321.4. The Final 50 Feet project 35421.5. Getting granular 35621.6. Mapping the city’s freight delivery infrastructure 35821.6.1. Step 1: collect existent data 35821.6.2. Step 2: develop survey to collect freight bay and loading dock data 35821.6.3. Preliminary site visits 35921.6.4. Initial survey form and the pilot survey 36021.6.5. Step 3: implement the survey 36321.7. Research results 36621.8. Conclusion 36821.9. Bibliography 368List of Authors 369Index 375