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By: M Nkosi Dr Mathetha Mokonyama Dr Paul Mokilane

PROSPECTS FOR USING CORRESPONDENCE ANALYSIS TO CHARACTERISE TRAVEL DEMAND FOR PLANNING AUTHORITIES WITH LIMITED TRANSPORT MODELLING RESOURCES. 38 th Southern African Transport Conference 8 July 2019. By: M Nkosi Dr Mathetha Mokonyama Dr Paul Mokilane. About the presentation

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By: M Nkosi Dr Mathetha Mokonyama Dr Paul Mokilane

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  1. PROSPECTS FOR USING CORRESPONDENCE ANALYSIS TO CHARACTERISE TRAVEL DEMAND FOR PLANNING AUTHORITIES WITH LIMITED TRANSPORT MODELLING RESOURCES 38th Southern African Transport Conference 8 July 2019 By: M Nkosi Dr Mathetha Mokonyama Dr Paul Mokilane

  2. About the presentation Currently used methods Correspondence Analysis (CA) and Logistic Regression Case study Concluding remarks

  3. About the presentation • Overview of the current used methods • The use of CA, supplemented by LR to showcase the initial characterisation of travel demand

  4. Current planning practices: Entry requirements • High quality and quantity data requirements • Technical human resource requirements • Specialised software requirements

  5. Process followed to estimate travel demand

  6. About the Correspondence Analysis • Classification technique • Graphical in nature • There are no assumption on the underlying distribution hence able accommodate • binary, • ordinal or • nominal

  7. About the Multi Logistical regression used • The following form was used • logistic regression used variables generated from CA

  8. Case study

  9. Household travel surveys • Purpose • To assist with evidence led transport planning. • To measure the performance of the transport system. • Used for strategic transport models • Typically comprised of: • Household Characteristics • Person attributes • Trip making • Use of and attitudes towards public transport • services

  10. Process followed: • Limit the dataset in the household survey to part of the network that have the variety of service • Assume there is adequate access • Use variables from the CA to calibrate the LR models • Calibrate the regression models for each modes.

  11. Using CA to obtain variable

  12. Case Study: results

  13. Concluding remarks • CA and LR is possible alternative method • Not meant to replace Discrete choice models in characterising travel demand especially for the bigger municipalities, • Useful for municipalities where there are constraints in terms of resources • Largely samples required to better calibrate the models

  14. Thank you…….

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