Can scientists understand human behavior enough to figure out what drives the choices you make? This is actually called the “science of decision making,” and this is what Anna Sperlock, a behavioral economist at the Lawrence Berkeley National Laboratory, specializes in.
Spurlock leads the WholeTraveler transport behavior study, a three-year project that attempts to analyze why and when some people adopt certain technologies, such as electric vehiclescycling, cycling (such as Uber and Lyft) and shopping online, while others do not.
Research is part SMART (Consortium of Mobile Systems and Simulations for Accelerated Transport Research)It is a multi-year consortium of several national laboratories, designed to further understand the energy implications and capabilities of advanced technology and mobility services. The SMART Mobility Consortium consists of five key research areas: connected and automated vehicles, mobility decision science, multimodal transportation, urban science and an improved fueling infrastructure, and is funded by the Department of Energy's Vehicle Technology (VTO) (DOE) ) Energy Efficiency Mobility Systems (EEMS) Program.
The WholeTraveler study began with Online survey In 2018, more than 1,000 residents of the San Francisco Bay area responded. The survey included questions regarding car ownership, locations, travel to work, demographics, personality traits, and a life history calendar that examined travel behavior related to major milestones and events from 20 to 50 years old. survey results provided researchers of the Berkeley lab with a storehouse of data and became the main cornerstone of the SMART Mobility scientific mobility solution.
Q. What do you do as a behavioral economist, and how do you apply this to study energy use?
I study how people make decisions on energy-related topics, such as energy-efficient appliances or products, utility pricing programs, or transportation. How do they exchange decisions regarding energy or cost or other factors? And what are the implications of these decisions? He takes the domains that matter for the energy “pie”, and these domains, finds out what drives people's behavior and how we can understand the behavior that underlies it.
Some of what I do may relate to concepts that come from psychology, but when it comes to behavior, a lot depends on the data. For most of the work that I do, we have some data that either directly tracks people's choices or what they have done, or see the consequences of this. We use machine learning methods to identify and identify patterns, and we use statistical and econometric analysis to test hypotheses.
Q. What was the motivation for the WholeTraveler study?
When we started evaluating the literature, we found that there was very limited data that tracked people over a long period of time. Longitudinal examinations are very expensive. But with our life history calendar, we were able to achieve this. We could ask about their choice in a shorter time, for example, every day; in the medium term, for example, choosing which vehicle they own or whether they own the vehicle; and in the long run, for example, where they live and whether they have children, and understand how they are interconnected.
There is a great need for a better understanding of the dynamic relationship between long-term decisions and life transitions that can influence the choice of transport. What life events cause changes in transport behavior and for whom? How permanent or flexible are these changes? What types of solutions can lead to a more energy-efficient transport system if we can understand what is behind certain behavior in this holistic sense, as well as barriers to other types of options?
We also found that there was very little survey data on some emerging transportation technologies and services, such as connected and automated vehicles, e-commerce and delivery, driveways, and travel sharing. The main goal of SMART Mobility is to understand the impact of these technologies and services at the system level and how they will change people's behavior and how it will affect the transportation system. We wanted to cover all of the key emerging transport innovations and their relationship to several aspects of transport behavior, so we called it the WholeTraveler study.
Q. What were some of the most interesting research findings?
There were a couple of things. On the one hand, the results that we obtain from life history calendar data are of interest to the transport research community. This is an area that has not been sufficiently studied. We get an idea of how key life events, such as graduation, partnerships, childbirth, are related to your choice of transport.
For example, we found that for people whose children were between the ages of 26 and 32, having a child is associated with a statistically significant increase in the likelihood of their regular driving. But if they had children under the age of 26, they were less likely to travel regularly. And if they had children older, having a child did not affect driving speed. When we plunged into the basic pattern, we found that – and this is not a big surprise – with the age of people, there is a tendency to increase dependence on cars. Those who had children over 32 years old were already quite dependent on the car, while those who had their first child aged 26 to 32 years old, the child quickly switched to more frequent driving. On the other hand, for those with small children, they were less likely to work full time because of having children and, therefore, were less likely to work while driving.
And in this regard, we found that, as soon as people reach a certain level of dependence on the car, the habit becomes very persistent. This was already somewhat known, but we showed it in a new way. Therefore, when you think about it in terms of politics, when you project human behavior, some of the strengths of these persistence patterns may be important for the correct modeling of these patterns.
Q. What are the implications for the future of all these emerging transport technologies and services?
There are all these consultant reports with the eyes of these innovations in the field of transport. Some say things like speed car ownership by 2030, it will be halved, or 95% of people by the year will rely on services such as cycling and sharing.
But I see patterns associated with things like children, and the power of car addiction tendencies, and I'm skeptical that these types of projections can be realistic. For some people, there are real obstacles to abandoning or abandoning a personal car, depending on their life context and related limitations.
We still have a lot of work to do. We would like to expand the survey beyond the Gulf region and integrate life history calendar data into a simulation model of land use and transport so that we can better understand the degree of energy impact of these types of life transitions.
Lawrence Berkeley National Laboratory
How we choose: applying the “science of decision-making” to transportation behavior (2020, March 13)
restored March 14, 2020
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