The Value of Green in Transportation Decisions

To address issues of climate change, information about greenhouse gas emissions is more and more often being presented to people. For example, labels and signs displaying pounds and kilograms of CO2 are showing up in trip planners, in car advertisements, and even in restaurant menus. This is being done under the assumption that the information about the environmental impacts of different alternatives will encourage more sustainable behavior. However, little is known about whether or not this strategy affects the choices people make. In order for people to change their behavior for the benefit of reduced emissions, they first need to place positive value on those reductions. This research aims to answer three questions regarding the efficacy of presenting people with this information: first, do people place significant value on reducing their greenhouse gas emissions, second, can people consistently value a pound of CO2, and third, how does this value of green vary across the population? To answer these questions, five experiments were designed and conducted using discrete choice experiments, a framework typically used to investigate how people value reducing their travel time. UC Berkeley students and residents of the San Francisco Bay Area made stated and revealed preference transportation choices from a set of alternatives. With knowledge of the attributes of their chosen alternative as well as those of their available alternates, their choices were analyzed using logit, mixed logit, and hybrid choice models. The findings include that not only can people consistently interpret and place value on the pounds of CO2 associated with their transportation alternatives, but also that there exists a discrete distribution in the value of green. For all except one experiment, the best models indicate that while a majority of the population does not act as though they care about reducing their greenhouse gas emissions, there is a small group with a willingness to pay of $2.68 per pound of CO2

The Value of Green in Transportation Decisions

To address issues of climate change, information about greenhouse gas emissions is more and more often being presented to people. For example, labels and signs displaying pounds and kilograms of CO2 are showing up in trip planners, in car advertisements, and even in restaurant menus. This is being done under the assumption that the information about the environmental impacts of different alternatives will encourage more sustainable behavior. However, little is known about whether or not this strategy affects the choices people make. In order for people to change their behavior for the benefit of reduced emissions, they first need to place positive value on those reductions. This research aims to answer three questions regarding the efficacy of presenting people with this information: first, do people place significant value on reducing their greenhouse gas emissions, second, can people consistently value a pound of CO2, and third, how does this value of green vary across the population? To answer these questions, five experiments were designed and conducted using discrete choice experiments, a framework typically used to investigate how people value reducing their travel time. UC Berkeley students and residents of the San Francisco Bay Area made stated and revealed preference transportation choices from a set of alternatives. With knowledge of the attributes of their chosen alternative as well as those of their available alternates, their choices were analyzed using logit, mixed logit, and hybrid choice models. The findings include that not only can people consistently interpret and place value on the pounds of CO2 associated with their transportation alternatives, but also that there exists a discrete distribution in the value of green. For all except one experiment, the best models indicate that while a majority of the population does not act as though they care about reducing their greenhouse gas emissions, there is a small group with a willingness to pay of $2.68 per pound of CO2

Revealing the Value of “Green ” and the Small Group with a Big Heart in Transportation Mode Choice

sustainabilit

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

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

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

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.Civil and Environmental Engineering

The Quantified Traveler: Changing transport behavior with personalized travel data feedback

Experiments using smartphones to influence behavior have been growing rapidly in many fields, especially in health and fitness research, and studies on eco-feedback technologies. In these studies, users are first tracked to understand their baseline behaviors, then measured continuously while they receive feedback about their actions. In transportation, studies using smartphones to change behavior have been limited due to the difficulty in even tracking users in the first place. Collecting data from smartphones in a battery efficient manner is a large research problem, and behavior change studies depend on being able to track travel behaviors. We developed an automated travel diary system which efficiently and uobtrusively collected travel data using smartphones and ran an experiment to evaluate how people’s awareness of their transportation behavior, attitudes towards sustainable transportation, intentions to change behavior, and measured travel behavior changed. For three weeks, 135 participants used an application on their iPhone or Android smartphone which unobstrusively tracked their location and sent data to a server which processed their data into trips and attributes related to their trips, such as time spent traveling, amount of money spent for transportation, amount of CO2 emitted, and calories burned during travel. Learning from prior work in eco-feedback studies and behavior change studies about health and fitness, a webpage was designed in which participants received feedback on their travel data along with trends and comparisons with various peer groups. Using surveys administered before and after the experiment, we measured a statistically significant change in partcipants’ awareness of statistics related to their travel behavior, and an intention to drive less and walk more amongst the “mainly-driving” group of the study population. In addition, a significant decrease in the amount of driving and increase in the amount of walking was measured. However, in a regression analysis, we were not able to find statistically significant covariates explaining what types of people and travelers were more likely to shift

Quantified Traveler: Travel Feedback Meets the Cloud to Change Behavior

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

Quantified Traveler: Travel Feedback Meets the Cloud to Change Behavior

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