Advertisement

Travel Plan Impacts

  • Chris De GruyterEmail author
Chapter
  • 251 Downloads
Part of the Springer Theses book series (Springer Theses)

Abstract

This chapter focuses on the effectiveness of travel plans at a set of case study sites. The aim of the chapter is to evaluate the effectiveness of travel plans for new residential developments, while considering the potential for self-selection bias effects.

Keywords

Control Site Propensity Score Match Travel Behaviour Residential Development Travel Survey 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Arlington County Commuter Services. (2013). Residential building transportation performance monitoring study. Virginia, US: Arlington.Google Scholar
  2. Arrington, G., & Cervero, R. (2008). TCRaP report 128: Effects of TOD on housing, parking and travel. Washington, D.C., US: Transportation Research Board of the National Academies.Google Scholar
  3. Australian Bureau of Statistics. (2011). Census of population and housing data. Retrieved September 25, 2014 from http://www.abs.gov.au/census.
  4. Australian Bureau of Statistics. (2011b). Reflecting a nation: Stories from the 2011 Census. Australia: Canberra.Google Scholar
  5. Australian Government. (2006). Census and statistics act 1905. Australia: Canberra.Google Scholar
  6. BioRegional. (2009). BedZED seven years on: The impact of the UK’s best known eco-village and its residents. UK.Google Scholar
  7. Cairns, S., Sloman, L., Newson, C., Anable, J., Kirkbride, A., & Goodwin, P. (2004). Smarter choices—changing the way we travel. UK: Department for Transport.Google Scholar
  8. Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22(1), 31–72.CrossRefGoogle Scholar
  9. Cao, X., Mokhtarian, P., & Handy, S. (2009). Examining the impacts of residential self-selection on travel behaviour: A focus on empirical findings. Transport Reviews, 29(3), 359–395.CrossRefGoogle Scholar
  10. Cao, X., Xu, Z., & Fan, Y. (2010). Exploring the connections among residential location, self-selection, and driving: Propensity score matching with multiple treatments. Transportation Research Part A, 44(10), 797–805.Google Scholar
  11. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, New Jersey, US: Lawrence Erlbaum Associates.zbMATHGoogle Scholar
  12. Department for Transport. (2005). Making residential travel plans work: Guidelines for new development. London, UK.Google Scholar
  13. Department of Transport, Planning and Local Infrastructure. (2010). Victorian integrated survey of travel and activity data. Retrieved September 25, 2014, from http://www.dtpli.vic.gov.au/.
  14. De Gruyter, C., Rose, G., & Currie, G. (In Press). ‘Travel Plans for New Residential Developments: Measuring Self-Selection Effects to Better Understand Travel Behaviour Impacts’. Transportation Research Record: Journal of the Transportation Research Board, No. 2564.Google Scholar
  15. De Gruyter, C., Rose, G., & Currie, G. (2015). ‘Understanding Travel Plan Effectiveness for New Residential Developments’. Transportation Research Record: Journal of the Transportation Research Board, No. 2537, 126–136.Google Scholar
  16. Enoch, M. (2012). Sustainable transport, mobility management and travel plans. Surrey, England: Ashgate Publishing Limited.Google Scholar
  17. Heinrich, C., Maffioli, A., & Vazquez, G. (2010). A primer for applying propensity-score matching: Impact-evaluation guidelines, Technical Notes No. IDB-TN-161, Inter-American Development Bank.Google Scholar
  18. Institute of Transportation Engineers. (2008). Trip generation (8th ed.). US: Washington D.C.Google Scholar
  19. Kahneham, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.CrossRefzbMATHGoogle Scholar
  20. Lee, J. S., Zegras, C., Ben-Joseph, E., & Park, S. (2014). Does urban living influence baby boomer’ travel behaviour? Journal of Transport Geography, 35, 21–29.CrossRefGoogle Scholar
  21. Melia, S. (2009). Potential for carfree development in the UK. PhD thesis, University of West England.Google Scholar
  22. Melia, S., Barton, H., & Parkhurst, G. (2013). Potential for carfree development in the UK. Urban Design and Planning, 166(DP2), 136–145.CrossRefGoogle Scholar
  23. Mokhtarian, P., & Cao, X. (2008). Examining the impacts of residential self-selection on travel behaviour: A focus on methodologies. Transportation Research Part B, 42, 204–228.CrossRefGoogle Scholar
  24. Naess, P. (2009). Residential self-selection and appropriate control variables in land use: Travel studies. Transport Reviews, 29(3), 293–324.MathSciNetCrossRefGoogle Scholar
  25. Roads & Maritime Services. (2013). Guide to traffic generating developments: Updated traffic surveys. Australia: New South Wales Government.Google Scholar
  26. Roads and Traffic Authority. (2002). Guide to traffic generating developments. Australia: New South Wales.Google Scholar
  27. Rye, T. (2002). Travel plans: Do they work? Transport Policy, 9, 287–298.CrossRefGoogle Scholar
  28. WSP. (2014). Does car ownership increase car use? A study of the use of car parking within residential developments in London, Commissioned by the Berkeley Group, UK.Google Scholar
  29. Howlett, R., & Watson, T. (2010). Travel planning in Victoria—a new strategic approach to sustaining communities. in Paper Presented to 33rd Australasian Transport Research Forum (ATRF), Canberra, Australia.Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.Department of Civil Engineering, Institute of Transport StudiesMonash UniversityClaytonAustralia

Personalised recommendations