Quantified Traveler: Travel Feedback Meets the Cloud to Change Behavior

Abstract

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

Apps, activities and travel: an conceptual exploration based on activity theory

“…Hence, the factors that maximize behavioural change according to various behavioural theories (goal setting, social comparison) are not uniformly appreciated by participants, and may potentially lead to drop out or not signing up. Finally, a contradiction was reported between the consortium's aim to change participants' behaviour but also their attitudes, and participants' autonomous decision making (Jariyasunant et al 2015). Direct questions regarding the effects of fossil fuels on greenhouse effect, asked before and after the study, showed a decline in reported awareness.…”

“…Travellers may be compared to the average of the other participants (sometimes called the community), or to self-set goals. In this section, we discuss the activity system based on a number of reported studies of travel feedback apps (Gabrielli and Maimone 2013;Wells et al 2014;Broll et al 2012;Meloni and di Teulada 2015;Jariyasunant et al 2015;Brazil et al 2013). …”

“…From the traveller's viewpoint, outcomes may relate to her travel behaviour. Various studies report that travellers' behaviour in response to the App changed toward increased use of environmental travel modes, and less car use (Gabrielli and Maimone 2013;Jariyasunant et al 2015). The studies are unclear about whether participants feel that their travel has improved and become more pleasant.…”

Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App

“…A large number of studies has focused on the recording and analysis of mobility and activity data using smartphones, usually by utilizing location (GPS) and accelerometer data ( [18], [19], [20], [21], [22], [23], [24], [25], [26]). Issues such as suitability of sensors for activity recognition [27], accuracy of transport mode classification [28], [29] and energy consumption of the app [30] are well researched areas.…”

A Feasibility Study of the Effect of Phone-Based Feedback of Other Commuters’ Subjective Experiences on Driver Intentions to Change

“…The measures reported back to subjects often include reports, per mode, of quantifiable aspects such as cost, calories consumed or CO 2 produced [8].…”