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Assessing the Impact of Transit and Personal Characteristics on Mode Choice of TOD Users

Assessing the Impact of Transit and Personal Characteristics on Mode Choice of TOD Users Ms Deepti Muley, Dr Jonathan Bunker and Prof Luis Ferreira Queensland University of Technology, Brisbane. In this presentation…. Introduction Need for this study Case study TOD

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Assessing the Impact of Transit and Personal Characteristics on Mode Choice of TOD Users

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  1. Assessing the Impact of Transit and Personal Characteristics on Mode Choice of TOD Users Ms Deepti Muley, Dr Jonathan Bunker and Prof Luis Ferreira Queensland University of Technology, Brisbane

  2. In this presentation… • Introduction • Need for this study • Case study TOD • Overview of data collection • Mode shares of TOD users • Mode share comparisons • Travel demand of TOD users • Conclusions

  3. Need for this study • Comparison required to understand transport benefits of TODs • Mode shares of TOD users need to be understood • Accurate travel demand models for TODs are needed • Past Studies • Concentrate principally on residents’ data • No significant previous Australian case studies

  4. Kelvin Grove Urban Village (KGUV) • 3km (1.8mi) from Brisbane’s Central Business District • Development underway • Size: 16.57 Ha (approx. 41 acre) • Mixed land uses • Education oriented development • Next to existing QUT Kelvin Grove • campus (12,000 students) • Close to many recreational facilities KGUV @ 2008

  5. Details of transit service 800m Overall good quality of PT service 400m 0.5km

  6. TOD user groups for KGUV Residents Non-student residents, Student residents Students Y8-12 High School students, University students Employees Retail employees, Professional employees Shoppers

  7. Overview of data collection

  8. Mode share for employees at KGUV 46%

  9. Mode share for students at KGUV 85%

  10. Mode share for shoppers at KGUV 71%

  11. Mode share for residents at KGUV 78%

  12. Mode share comparison for work trips • Population approx 1.8M, average annual household income approx USD$44,000

  13. Mode share comparison for education trips • Population approx 1.8M, average annual household income approx USD$44,000

  14. Mode share comparison for shopping trips • Population approx 1.8M, average annual household income approx USD$44,000

  15. Mode share comparison for residents (Considering first trip of the day) • Population approx 1.8M, average annual household income approx USD$44,000

  16. Travel demand analysis • Mode choice • Personal characteristics • Transit characteristics • Logistic regression analysis

  17. Coefficients of Logistic regression analysis

  18. Sensitivity of an employee’s sustainable mode choice, p(1), with age group

  19. Sensitivity of a student’s sustainable mode choice, p(1), with age group

  20. Sensitivity of a shopper’s sustainable mode choice, p(1), with age group

  21. Sensitivity of a resident’s sustainable mode choice, p(1), with age group

  22. Conclusions • KGUV highly attractive to • young adults • More walk, cycle and public • transport trips compared to • Greater Brisbane and Inner • Northern Brisbane users • Mode shares principally • dependant on age group , LOS • and employment status

  23. Scope of future research & future applications • Detailed comparison with other suburbs • Travel demand modelling for TODs • Planning future TODs

  24. Contact author: Deepti Muley d.muley@student.qut.edu.au

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