Intelligent Urban Mobility: Decision Support Systems for Sustainable Transportation explores the role of technology in enabling greener, more accessible transportation in cities worldwide. This book provides insights into leveraging decision support systems to drive positive change by focusing on applied soft computing techniques, artificial intelligence, and algorithms for fuzzy systems. Researchers and professionals will find actionable information on mitigating congestion and emissions through sustainable mobility initiatives, which bridges the gap between theory and real-world practice.
The book also offers technical guidance and expert perspectives on the application of decision support systems to evaluate and optimize planning for sustainable transit options. The book highlights innovative models and frameworks for analyzing mobility options and planning sustainable transport systems. It is an essential resource for researchers, graduate students, and professionals in transportation, urban planning, civil engineering, and decision sciences who aim to redesign city transportation to reduce environmental impact and carbon emissions.
1. 1 Introduction - p. 103 2. 2 Methodological framework - p. 107 3. 3 Case study - p. 112 4. 4 Results and implications - p. 133 5. 5 Conclusion - p. 143 6. Chapter 7. Identifying effective risk management policies for sustainable urban transportation projects - p. 149 7. Chapter 8. Flexible linguistic consensus decision making for sustainable transport practices - p. 163 8. 1 Introduction - p. 163 9. 2 Preliminaries - p. 166 10. 3 Quantitative processing of FLEs and determination of attribute weights - p. 168 11. 4 A novel flexible linguistic adaptive consensus reaching model - p. 172 12. 5 A detailed description of the decision-making process - p. 175 13. 6 Case study - p. 177 14. 7 Managerial implication - p. 188 15. 8 Conclusion and future direction - p. 189 16. Chapter 9. Optimization-based decision making for sustainable transport: Insights into the aviation industry - p. 193 17. Chapter 10. A generalized Hellinger distance-based spherical fuzzy TOPSIS method for sustainable transport systems selection - p. 211 18. 1 Introduction - p. 211 19. 2 Preliminaries - p. 213 20. 3 New distance measure for SFSs - p. 214 21. 4 Numerical comparisons - p. 218 22. 5 TOPSIS for sustainable transport systems selection - p. 220 23. 6 Conclusion - p. 226 24. Chapter 11. Fuzzy multicriteria decision making for assessing public transportation systems for sustainable transportation - p. 229 25. Chapter 12. Determining enablers for the application of sustainable and resilient urban transport systems using T-spherical fuzzy DEMATEL-ISM model - p. 259 26. 1 Introduction - p. 259 27. 2 Literature review - p. 261 28. 3 Research methodology - p. 266 29. 4 Application of the proposed method - p. 271 30. 5 Comparative analysis - p. 276 31. 6 Managerial implications - p. 278 32. 7 Conclusions - p. 280 33. Chapter 13. Selection of the solution for the integration of sustainable and resilient urban transport systems using linguistic T-spherical fuzzy COCOSO’B method - p. 287 34. 1 Introduction - p. 287 35. 2 Literature review - p. 291 36. 3 Proposed methodologies - p. 293 37. 4 Case study - p. 298 38. 5 Discussion - p. 308 39. 6 Managerial implications - p. 310 40. 7 Conclusion - p. 310 41. Chapter 14. Sustainable AI-assisted energy-efficient edge cloud system for industrial Internet of Things applications - p. 315 42. Index - p. 327
Intelligent Urban Mobility: Decision Support Systems for Sustainable Transportation explores the role of technology in enabling greener, more accessible transportation in cities worldwide. This book provides insights into leveraging decision support systems to drive positive change by focusing on applied soft computing techniques, artificial intelligence, and algorithms for fuzzy systems. Researchers and professionals will find actionable information on mitigating congestion and emissions through sustainable mobility initiatives, which bridges the gap between theory and real-world practice.
The book also offers technical guidance and expert perspectives on the application of decision support systems to evaluate and optimize planning for sustainable transit options. The book highlights innovative models and frameworks for analyzing mobility options and planning sustainable transport systems. It is an essential resource for researchers, graduate students, and professionals in transportation, urban planning, civil engineering, and decision sciences who aim to redesign city transportation to reduce environmental impact and carbon emissions.
1. 1 Introduction - p. 103 2. 2 Methodological framework - p. 107 3. 3 Case study - p. 112 4. 4 Results and implications - p. 133 5. 5 Conclusion - p. 143 6. Chapter 7. Identifying effective risk management policies for sustainable urban transportation projects - p. 149 7. Chapter 8. Flexible linguistic consensus decision making for sustainable transport practices - p. 163 8. 1 Introduction - p. 163 9. 2 Preliminaries - p. 166 10. 3 Quantitative processing of FLEs and determination of attribute weights - p. 168 11. 4 A novel flexible linguistic adaptive consensus reaching model - p. 172 12. 5 A detailed description of the decision-making process - p. 175 13. 6 Case study - p. 177 14. 7 Managerial implication - p. 188 15. 8 Conclusion and future direction - p. 189 16. Chapter 9. Optimization-based decision making for sustainable transport: Insights into the aviation industry - p. 193 17. Chapter 10. A generalized Hellinger distance-based spherical fuzzy TOPSIS method for sustainable transport systems selection - p. 211 18. 1 Introduction - p. 211 19. 2 Preliminaries - p. 213 20. 3 New distance measure for SFSs - p. 214 21. 4 Numerical comparisons - p. 218 22. 5 TOPSIS for sustainable transport systems selection - p. 220 23. 6 Conclusion - p. 226 24. Chapter 11. Fuzzy multicriteria decision making for assessing public transportation systems for sustainable transportation - p. 229 25. Chapter 12. Determining enablers for the application of sustainable and resilient urban transport systems using T-spherical fuzzy DEMATEL-ISM model - p. 259 26. 1 Introduction - p. 259 27. 2 Literature review - p. 261 28. 3 Research methodology - p. 266 29. 4 Application of the proposed method - p. 271 30. 5 Comparative analysis - p. 276 31. 6 Managerial implications - p. 278 32. 7 Conclusions - p. 280 33. Chapter 13. Selection of the solution for the integration of sustainable and resilient urban transport systems using linguistic T-spherical fuzzy COCOSO’B method - p. 287 34. 1 Introduction - p. 287 35. 2 Literature review - p. 291 36. 3 Proposed methodologies - p. 293 37. 4 Case study - p. 298 38. 5 Discussion - p. 308 39. 6 Managerial implications - p. 310 40. 7 Conclusion - p. 310 41. Chapter 14. Sustainable AI-assisted energy-efficient edge cloud system for industrial Internet of Things applications - p. 315 42. Index - p. 327
