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Amir Abbas Rassafi

Amir Abbas Rassafi. Sharif University of Technology. Quantitative Methods in the Assessment of Sustainable Transport. PhD Candidate of Transportation Engineering. The Presentation Outline. Problem Definition The Emergence of Sustainable Development (SD) Quantification of SD

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Amir Abbas Rassafi

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  1. Amir Abbas Rassafi Sharif University of Technology Quantitative Methods in the Assessment of Sustainable Transport PhD Candidate of Transportation Engineering

  2. The Presentation Outline • Problem Definition • The Emergence of Sustainable Development (SD) • Quantification of SD • - Assessment • - Modeling • The proposed Method for Assessment • - Elasticity • - Database Characteristics • - Data Reduction • - Final Database • - Elasticity Analysis • - Aggregation and Its Different Scopes

  3. The Presentation Outline • Applications: • - Concordance Analysis • - Data Envelopment Analysis • - Posets / Hasse Diagram Technique • The proposed Method for Modeling • - Chaos • - Predator-Prey Model • Conclusions

  4. Problem Definition • The Objective Is to Quantify the Concept of Sustainable Transport (ST) in a National Scope • Transport Has Major Interactions with the Other Sectors: • Transport vs. Economy • Transport vs. Environment • Transport vs. Social Aspects

  5. Problem Definition • Transport vs. Economy • Measuring the Economic Wealth of the Countries • The Role of Transport in Economy • + Value Added of the Car Industries • + Value Added of the Transport Services (Supply Chain / People Movement) • + Accessibility • The Impact of Economy on Transport

  6. Problem Definition • Transport vs. Environment • Non-Renewable Resource Depletion • Pollution

  7. Problem Definition • Transport vs. Social Aspect • Equity • Public Participation • Traffic Accidents

  8. SD Development Growth The Emergence of SD • The Growth Chain

  9. Quantification of SD • Assessment • Indicators / Criteria • Modeling • System Dynamics / Differential and Difference Equations / Optimization

  10. The proposed Method for Assessment • Why Elasticity? • - A Comprehensive Measure • - Comparative Assessment • - Finding Trends • - Temporal Consideration

  11. The proposed Method for Assessment • Preliminary Database • - National Data Reported Annually • - References (WB, UN, IRU) • - 450 Variables in 3+1 Categories • - Missing Data

  12. The proposed Method for Assessment • Data Reduction • - Cut-Off Rule • - Factor Analysis Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. Factor analysis is often used in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables. - Factor Analysis

  13. The proposed Method for Assessment - Factor Analysis • Data reduction tool • Removes redundancy or duplication from a set of correlated variables • Represents correlated variables with a smaller set of “derived” variables. • Factors are formed that are relatively independent of one another. • Components: • latent variables: factors • observed variables

  14. The proposed Method for Assessment - Factor Analysis • Data reduction tool • Removes redundancy or duplication from a set of correlated variables • Represents correlated variables with a smaller set of “derived” variables. • Factors are formed that are relatively independent of one another. • Components: • latent variables: factors • observed variables

  15. Sustainability Index T NT C H M R A E S U D U D U D U D V B V B P F P F P F P F P F P F P F P F P F P F The proposed Method for Assessment • Final Database The Variable Selection Structure

  16. The proposed Method for Assessment • Final Database The Selected Variables

  17. The proposed Method for Assessment • Final Database The Selected Variables

  18. The proposed Method for Assessment • 432 Elasticity Values for Each Country Elasticity of Y with respect to X • Elasticity Analysis Transport Variable Non-Transport Variable

  19. The proposed Method for Assessment Z Score Mean • Aggregation Composite Index of Group G WRT X Standard Deviation Coefficient

  20. Applications Concordance Analysis -Alternate plans are ranked by a series of pairwise comparisons across the set of objectives in a rank-ordering technique. -Alternatives  Countries - objectives  Composite Indices

  21. Applications Concordance Analysis Concordance Analysis -A concordance index calculates the degree to which one alternative plan is preferred to another for a given weighting structure on the objectives. -A discordance index calculates the degree to which one alternative plan is dominated by another.

  22. Applications Concordance Analysis -The Countries with Greater Values for Net Concordance and Less Values for Net Discordance Are Better Than the Others.

  23. Applications Concordance Analysis -Alternatives (Countries) That Perform Better Than Average on Both Concordance and Discordance Are Defined as Non-dominated.

  24. Applications Data Envelopment Analysis -If given DMUs, A and B, is capable of producing Y(A) and Y(B) units of output with X(A) and X(B) inputs, respectively, then other DMUs should also be able to do the same if they were to operate efficiently.

  25. efficient frontier OUTPUT1/INPUT virtual DMU for C A C’ B Efficiency= OC/OC’ C O OUTPUT2/INPUT Applications Data Envelopment Analysis

  26. Applications Partial Order Theory Partial order theory and Hasse diagram technique appears to be a promising tool for environmental decision-making issues. A partial order on a set P is a relation ≤ P2 that is: reflexive (x ≤ x), antisymmetric (x ≤ y and y ≤ x imply x = y), and transitive (x ≤ y, and y ≤ z imply x ≤ z). Partially Ordered Set (POSET)

  27. Applications Partial Order Theory Hasse Diagram Technique (HDT) The ranking probabilities and average ranks of the countries based on linear extensions for the partial order

  28. Applications Partial Order Theory Hasse Diagram Technique (HDT) The average rank and the ranking interval of the countries base on the partial order

  29. Proposed Method for Modeling Stability and Sustainability Definition. A system is called sustainable, if it is dynamically stable and non-chaotic, subject to the constraints (standards, tolerance levels, thresholds, etc.) levied upon its components (which in our case are EES components).

  30. Proposed Method for Modeling Chaos Nonlinearity Sensitivity to the parameters Self-similarity Deterministic

  31. Proposed Method for Modeling An Example: Predator Prey Model

  32. Proposed Method for Modeling An Example: Predator Prey Model Bifurcation diagram (T vs. β) for α=1.0, μ=0.6, δ=0.5.

  33. Proposed Method for Modeling An Example: Predator Prey Model “Policy space” for the system in which the system is stable, when μ=0.1

  34. Conclusions • SD Is a Qualitative Value. • Transport Plays a Key Role in SD Studies. • Quantification Attempts: • Assessment / Modeling • Elasticity Is a More Comprehensive Indicator

  35. Conclusions PERSPECTIVE INDICES ISSUES Level of interpretation, analysis and aggregation INDICATORS ANALYSED DATA PRIMARY DATA DETAIL

  36. Conclusions • Multi-Criteria Approaches Are Useful Tools in the SD Assessments. • A Dynamical Definition of SD Can Be a Useful Method for Policy Making.

  37. Thank you The End

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