, Volume 44, Issue 6, pp 1279–1292 | Cite as

Estimating the trip generation impacts of autonomous vehicles on car travel in Victoria, Australia

  • Long T. TruongEmail author
  • Chris De Gruyter
  • Graham Currie
  • Alexa Delbosc


Autonomous vehicles (AVs) potentially increase vehicle travel by reducing travel and parking costs and by providing improved mobility to those who are too young to drive or older people. The increase in vehicle travel could be generated by both trip diversion from other modes and entirely new trips. Existing studies however tend to overlook AVs’ impacts on entirely new trips. There is a need to develop a methodology for estimating possible impacts of AVs on entirely new trips across all age groups. This paper explores the impacts of AVs on car trips using a case study of Victoria, Australia. A new methodology for estimating entirely new trips associated with AVs is proposed by measuring gaps in travel need at different life stages. Results show that AVs would increase daily trips by 4.14% on average. The 76+ age group would have the largest increase of 18.5%, followed by the 18–24 age group and the 12–17 age group with 14.6 and 11.1% respectively. If car occupancy remains constant in AV scenarios, entirely new trips and trip diversions from public transport and active modes would lead to a 7.31% increase in car trips. However increases in car travel are substantially magnified by reduced car occupancy rates, a trend evidenced throughout the world. Car occupancy would need to increase by at least 5.3–7.3% to keep car trips unchanged in AV scenarios.


Autonomous vehicles Driverless Induced demand Car trips Life stages 



An earlier version of this paper was presented at the Transportation Research Board (TRB) 96th Annual Meeting in Washington, D.C., in January 2017.


  1. Anderson, J.M., Kalra, N., Stanley, K.D., Sorensen, P., Samaras, C., Oluwatola, O.A.: Autonomous Vehicle Technology A Guide for Policymakers. RAND Corporation, Santa Monica (2014)Google Scholar
  2. Bansal, P., Kockelman, K.M.: Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies. In: Transportation Research Board 95th Annual Meeting, Washington (2016)Google Scholar
  3. Bansal, P., Kockelman, K.M., Singh, A.: Assessing public opinions of and interest in new vehicle technologies: an Austin perspective. Transp. Res. Part C Emerg. Technol. 67, 1–14 (2016)CrossRefGoogle Scholar
  4. Bierstedt, J., Gooze, A., Gray, C., Peterman, J., Raykin, L., Walters, J.: Effects of Next-Generation Vehicles on Travel Demand and Highway Capacity. FP Think (2014)Google Scholar
  5. Childress, S., Nichols, B., Charlton, B., Coe, S.: Using an activity-based model to explore the potential impacts of automated vehicles. Transp. Res. Rec. J. Transp. Res. Board 2493, 99–106 (2015)CrossRefGoogle Scholar
  6. Currie, G., Gammie, F., Waingold, C., Paterson, D., Vandersar, D.: Rural and regional young people and transport: improving access to transport for young people in rural and regional Australia. National Youth Affairs Research Scheme (2005)Google Scholar
  7. Davidson, P., Spinoulas, A.: Autonomous vehicles: what could this mean for the future of transport? In: Australian Institute of Traffic Planning and Management (AITPM) National Conference, Brisbane, Queensland (2015)Google Scholar
  8. Delbosc, A., Currie, G.: Changing demographics and young adult driver license decline in Melbourne, Australia (1994–2009). Transportation 41(3), 529–542 (2014)CrossRefGoogle Scholar
  9. DPTI: SA Becomes First Australian Jurisdiction to Allow On-Road Driverless Car Trials. Department of Planning, Transport and Infrastructure, Adelaide (2016)Google Scholar
  10. Fagnant, D.J., Kockelman, K.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 77, 167–181 (2015)CrossRefGoogle Scholar
  11. Fagnant, D.J., Kockelman, K.M.: The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transp. Res. Part C Emerg. Technol. 40, 1–13 (2014)CrossRefGoogle Scholar
  12. Greenblatt, J.B., Saxena, S.: Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles. Nat. Clim. Change 5(9), 860–863 (2015)CrossRefGoogle Scholar
  13. Gucwa, M.: The mobility and energy impacts of automated cars. In: Automated Vehicles Symposium, San Francisco, CA (2014)Google Scholar
  14. Guerra, E.: Planning for cars that drive themselves: Metropolitan Planning Organizations, regional transportation plans, and autonomous vehicles. J. Plan. Educ. Res. 36(2), 210–224 (2016)CrossRefGoogle Scholar
  15. Harper, C., Mangones, S., Hendrickson, C.T., Samaras, C.: Bounding the potential increases in vehicles miles traveled for the non-driving and elderly populations and people with travel-restrictive medical conditions in an automated vehicle environment. In: Transportation Research Board 94th Annual Meeting, Washington (2015)Google Scholar
  16. Heinrichs, D., Cyganski, R.: Automated driving: how it could enter our cities and how this might affect our mobility decisions. disP Plan. Rev. 51(2), 74–79 (2015)CrossRefGoogle Scholar
  17. Hoogendoorn, R., Arem, B.V., Hoogendoorn, S.: Automated driving, traffic flow efficiency, and human factors. Transp. Res. Rec. J. Transp. Res. Board 2422, 113–120 (2014)CrossRefGoogle Scholar
  18. ITF: Urban mobility system upgrade: How shared self-driving cars could change city traffic. International Transport Forum (2015)Google Scholar
  19. Kim, K., Rousseau, G., Freedman, J., Nicholson, J.: The travel impact of autonomous vehicles in metro atlanta through activity-based modeling. In: The 15th TRB National Transportation Planning Applications Conference (2015)Google Scholar
  20. Kyriakidis, M., Happee, R., de Winter, J.C.F.: Public opinion on automated driving: results of an international questionnaire among 5000 respondents. Transp. Res. Part F Traffic Psychol. Behav. 32, 127–140 (2015)CrossRefGoogle Scholar
  21. LaMondia, J.J., Fagnant, D.J., Qu, H., Barrett, J., Kockelman, K.: Long-distance travel mode-shifts due to automated vehicles: a statewide mode-shift simulation experiment and travel survey analysis. In: Transportation Research Board 95th Annual Meeting, Washington (2016)Google Scholar
  22. Levinson, D.: Climbing mount next: the effects of autonomous vehicles on society. Minnesota J. Law Sci. Technol. 16(2), 787–809 (2015)Google Scholar
  23. Liang, X., Correia, G.H.d.A., van Arem, B.: Optimizing the service area and trip selection of an electric automated taxi system used for the last mile of train trips. Transp. Res. Part E Logist. Transp. Rev. 93, 115–129 (2016)CrossRefGoogle Scholar
  24. Litman, T.: Autonomous vehicle implementation predictions: implications for transport planning. Victoria Transport Policy Institute, Victoria (2015)Google Scholar
  25. Malokin, A., Circella, G., Mokhtarian, P.L.: How do activities conducted while commuting influence mode choice? testing public transportation advantage and autonomous vehicle scenarios. In: Transportation Research Board 94th Annual Meeting, Washington DC (2015)Google Scholar
  26. Milakis, D., Van Arem, B., Van Wee, G.: Policy and society related implications of automated driving: a review of literature and directions for future research. Delft University of Technology, Delft (2015)Google Scholar
  27. Morrow, W.R., Greenblatt, J.B., Sturges, A., Saxena, S., Gopal, A., Millstein, D., Shah, N., Gilmore, E.A.: Key factors influencing autonomous vehicles’ energy and environmental outcome. In: Meyer, G., Beiker, S. (eds.) Road Vehicle Automation. Springer, Berlin (2014)Google Scholar
  28. NHTSA: U.S. Department of Transportation Releases Policy on Automated Vehicle Development. National Highway Traffic Safety Administration, U.S. Department of Transportation, (2013)Google Scholar
  29. Schoettle, B., Sivak, M.: A survey of public opinion about autonomous and self-driving vehicles in the US, the UK, and Australia. The University of Michigan, Transportation Research Institute, Ann Arbor (2014)Google Scholar
  30. Shaz, K., Corpuz, G.: Serving passengers—are you being served? In: 4th Annual PATREC Research Forum (2008)Google Scholar
  31. Shladover, S.E.: Cooperative (rather than autonomous) vehicle-highway automation systems. IEEE Intell. Transp. Syst. Mag. 1(1), 10–19 (2009)CrossRefGoogle Scholar
  32. Sivak, M., Schoettle, B.: Influence of current nondrivers on the amount of travel and trip patterns with self-driving vehicles. The University of Michigan, Transportation Research Institute, Ann Arbor (2015)Google Scholar
  33. Spieser, K., Treleaven, K., Zhang, R., Frazzoli, E., Morton, D., Pavone, M.: Toward a systematic approach to the design and evaluation of automated mobility-on-demand systems: a case study in Singapore. In: Meyer, G., Beiker, S. (eds.) Road Vehicle Automation, pp. 229–245. Springer, Berlin (2014)CrossRefGoogle Scholar
  34. Vicroads: Traffic Monitor 2012–13. VicRoads (2015)Google Scholar
  35. Wadud, Z., MacKenzie, D., Leiby, P.: Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles. Transp. Res. Part A Policy Pract. 86, 1–18 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Public Transport Research Group, Department of Civil Engineering, Monash Institute of Transport StudiesMonash UniversityClaytonAustralia

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