We describe the design and evaluation of a system named Quantified Traveler (QT). QT is a Computational Travel Feedback System. Travel Feedback is an established programmatic method whereby travelers record travel in diaries, and meet with a counselor who guides her to alternate mode or trip decisions that are more sustainable or otherwise beneficial to society, while still meeting the subject's mobility needs. QT is a computation surrogate for the counselor. Since counselor costs can limit the size of travel feedback programs, a system such as QT at the low costs of cloud computing, could dramatically increase scale, and thereby sustainable travel. QT uses an app on the phone to collect travel data, a server in the cloud to process it into travel diaries and then a personalized carbon, exercise, time, and cost footprint. The subject is able to see all of this information on the web. We evaluate with 135 subjects to learn if subjects let us use their personal phones and data-plans to build travel diaries, whether they actually use the website to look at their travel information, whether the design creates pro-environmental shifts in psychological variables measured by entry and exit surveys, and finally whether the revealed travel behavior records reduced driving. Before and after statistical analysis and the results from a structural equation model suggest that the results are a qualified success.3

While it is increasingly popular to broadcast information regarding environmental impact, little is known regarding the effects that this information has on human behavior. This research aims to provide insight into whether, and to what extent, presenting environmental attributes of transport alternatives influences individual transport decisions. We designed and conducted three experiments in which subjects (UC Berkeley undergraduates) were presented with hypothetical scenarios of transport decisions, including auto purchase choice, mode choice, and route choice. We analyzed their decisions via a choice model to determine how they value reducing their emissions relative to other attributes. We found that our subjects are willing to adjust their behavior to reduce emissions, exhibiting an average willingness to pay for emissions reduction, or value of green (VoG), of 15 cents per pound of CO 2 saved. Despite concern that people cannot meaningfully process quantities of CO 2 , we found evidence to the contrary in our subject pool in that the estimated VoG was consistent across context (the wide range of transport decisions that we presented) and presentation (e.g., whether the information was presented in tons or pounds, or whether a social reference point of the emissions of an average person was provided). We also found significant heterogeneity in VoG, with most of the respondents valuing green somewhere between 0 and 70 cents per pound and with women, on average, willing to pay 7 cents more per saved pound than men. While the findings are encouraging, further work is required to determine whether they hold outside of a lab environment and with a more representative pool of subjects.

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. Three computer experiments were designed and conducted by using University of California, 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. The findings suggest high transferability of lessons from behavioral economics and great potential for influencing transport behavior. It was found that person- and trip-specific information regarding greenhouse gas emissions has significant potential for increasing sustainable behavior, and it was possible to quantify this value of green at around $0.24/lb of greenhouse gas avoided. Congruent with lessons from behavioral economics, information on peer compliance with pedestrian laws was found to have a stronger influence on pedestrian safety behavior than information on the law, citation rates, or accident statistics. It was also found that social influences positively affect the decision to buy a hybrid car over a conventional car or to forgo a car altogether.

Abstract:To address issues of climate change, people are more and more being presented with the greenhouse gas emissions associated with their alternatives. Statements of pounds or kilograms of CO 2 are showing up in trip planners, car advertisements, and even restaurant menus under the assumption that this information influences behavior. This research contributes to the literature that investigates how travelers respond to such information. Our objective is to better understand the -value of green‖ or how much travelers are willing to pay in money in order to reduce the CO 2 associated with their travel. As with previous work, we designed and conducted a mode choice experiment using methods that have long been used to study value of time. The contributions of this paper are twofold. First, we employ revealed preference data, whereas previous studies have been based on stated preferences. Second, we provide new insight on how the value of green is distributed in the population. Whereas previous work has specified heterogeneity either systematically or with a continuous distribution, we find that a latent class choice model specification better fits the data and also is attractive behaviorally. The best fitting latent class model has two classes: one large class (76% of the sample) who are not willing to spend any time or money to reduce their CO 2 and a second class (24% of the sample) who value reducing their CO 2 at a very high rate of $2.68 per pound of reduction-our so-called small group with a big heart. We reanalyzed three datasets that we had previously collected and found considerable robustness of this two class result.

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