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Experimental Economics in Transportation: A Focus on Social Influences and the Provision of Information

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  • Gaker, David
  • Zheng, Yanding
  • Walker, Joan

Abstract

A major aspect of transportation planning is understanding behavior: how to predict it and how to influence it over the long term. Behavioral models in transportation are predominantly rooted in the classic microeconomic paradigm of rationality. However, there is a long history in behavioral economics of raising serious questions about rationality. Behavioral economics has made inroads in transportation in the areas of survey design, prospect theory, and attitudinal variables. Further infusion into transportation could lead to significant benefits in terms of increased ability to both predict and influence behavior. The aim of this research is to investigate the transferability of findings in behavioral economics to transportation, with a focus on lessons regarding personalized information and social influences. We designed and conducted three computer experiments using UC Berkeley students: one on personalized-information and route choice, one on social influences and auto ownership, and one combining information and social influences and pedestrian safety. Our findings suggest high transferability of lessons from behavioral economics and great potential for influencing transport behavior. We found that person- and trip-specific information regarding greenhouse gas emissions has significant potential for increasing sustainable behavior, and we are able to quantify this Value of GREEN at around $0.24/pound of greenhouse gas avoided. Congruent with lessons from behavioral economics, we found that information on peer compliance of pedestrian laws had a stronger influence on pedestrian safety behavior than information on the law, citation rates, or accident statistics. We also found that social influences positively impact the decision to buy a hybrid car over a conventional car or forgo a car altogether.

Suggested Citation

  • Gaker, David & Zheng, Yanding & Walker, Joan, 2010. "Experimental Economics in Transportation: A Focus on Social Influences and the Provision of Information," University of California Transportation Center, Working Papers qt7vg9m3r1, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt7vg9m3r1
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    References listed on IDEAS

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    Cited by:

    1. Chorus, Caspar G., 2015. "Models of moral decision making: Literature review and research agenda for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 16(C), pages 69-85.
    2. Kim Kaleva Kaivanto & Peng Zhang, 2016. "A Resolution of Emissions-Estimate Confusion for Informing Flight Choice," Working Papers 115969274, Lancaster University Management School, Economics Department.
    3. David Gaker & Joan L. Walker, 2013. "Revealing the Value of “Green” and the Small Group with a Big Heart in Transportation Mode Choice," Sustainability, MDPI, vol. 5(7), pages 1-15, July.
    4. Prawira Belgiawan & Jan-Dirk Schmöcker & Maya Abou-Zeid & Joan Walker & Tzu-Chang Lee & Dick Ettema & Satoshi Fujii, 2014. "Car ownership motivations among undergraduate students in China, Indonesia, Japan, Lebanon, Netherlands, Taiwan, and USA," Transportation, Springer, vol. 41(6), pages 1227-1244, November.
    5. Pettifor, H. & Wilson, C. & Axsen, J. & Abrahamse, W. & Anable, J., 2017. "Social influence in the global diffusion of alternative fuel vehicles – A meta-analysis," Journal of Transport Geography, Elsevier, vol. 62(C), pages 247-261.
    6. Metcalfe, Robert & Dolan, Paul, 2012. "Behavioural economics and its implications for transport," Journal of Transport Geography, Elsevier, vol. 24(C), pages 503-511.
    7. Lu, Chung-Cheng & Liu, Jiangtao & Qu, Yunchao & Peeta, Srinivas & Rouphail, Nagui M. & Zhou, Xuesong, 2016. "Eco-system optimal time-dependent flow assignment in a congested network," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 217-239.
    8. Anowar, Sabreena & Eluru, Naveen & Hatzopoulou, Marianne, 2017. "Quantifying the value of a clean ride: How far would you bicycle to avoid exposure to traffic-related air pollution?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 66-78.
    9. Elisa Borowski & Amanda Stathopoulos, 2022. "Protection or Peril of Following the Crowd in a Pandemic-Concurrent Flood Evacuation," Papers 2202.00229, arXiv.org.
    10. Vivek Choudhary & Masha Shunko & Serguei Netessine & Seongjoon Koo, 2022. "Nudging Drivers to Safety: Evidence from a Field Experiment," Management Science, INFORMS, vol. 68(6), pages 4196-4214, June.
    11. Nayer Daher & Farhana Yasmin & Min Ru Wang & Ehsan Moradi & Omid Rouhani, 2018. "Perceptions, Preferences, and Behavior Regarding Energy and Environmental Costs: The Case of Montreal Transport Users," Sustainability, MDPI, vol. 10(2), pages 1-20, February.
    12. Kim, Kyungah & Moon, Sungho & Kim, Junghun, 2023. "How far is it from your home? Strategic policy and management to overcome barriers of introducing fuel-cell power generation facilities," Energy Policy, Elsevier, vol. 182(C).
    13. Daziano, Ricardo A. & Chiew, Esther, 2012. "Electric vehicles rising from the dead: Data needs for forecasting consumer response toward sustainable energy sources in personal transportation," Energy Policy, Elsevier, vol. 51(C), pages 876-894.
    14. Konstadinos G. Goulias & Ram M. Pendyala, 2014. "Choice context," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 5, pages 101-130, Edward Elgar Publishing.

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