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The transportation engineering field is currently experiencing a profound transformation driven by technological evolution, which highlights the importance of preparing students for the types of careers that will be available to them in the future. Although transportation engineering programs in the United States are typically at the graduate level, the majority of existing research has focused on undergraduate courses. This study focuses on master's-level transportation engineering curricula, with the goal of investigating how changes in employment opportunities and day-to-day work responsibilities of transportation engineers over the coming 5-10 years will inform the topics that graduate-level curricula should include to set students up for future success. The study consists of in-depth interviews with a range of academics and practitioners and subsequent analyses of interview transcripts using thematic analysis methods. Seven themes were derived, pertaining to three categories: future opportunities, identified skills, and program structure observations. The three thematic categories are not independent, and their interactions with one another hold information that can lead to recommendations for the design of transportation engineering master's programs.

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.

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.

With the advent of ubiquitous mobile sensing and self-tracking groups, travel demand researchers have a unique opportunity to combine these two developments to improve the state of the art of travel diary collection. While the use of mobile phones and the inference of travel diaries from GPS and sensor data allows for lower-cost, longer surveys, we show how the self-tracking movement can be leveraged to interest people in participating over a longer period of time. By compiling personalized feedback and statistics on participants’ travel habits during the survey, we can provide the participants with direct value in exchange for their data collection effort. Moreover, the feedback can be used to provide statistics that influence people’s awareness of the footprint of their transportation choices and their attitudes, with the goal of moving them toward more sustainable transportation behavior.We describe an experiment that we conducted with a small sample in which this approach was implemented. The participants allowed us to track their travel behavior over the course of two weeks, and they were given access to a website they were presented with their trip history, statistics and peer comparisons. By means of an attitudinal survey that we asked the participants to fill out before and after the tracking period, we determined that this led to a measurable change in people’s awareness of their transportation footprint and to a positive shift in their attitudes toward sustainable transportation.

The rise of e-commerce has led to substantial changes in personal travel and activities. We systematically reviewed empirical studies on the relationship between online shopping and personal travel behaviour. We synthesised and assessed the evidence for four types of effects on various travel outcomes, including trip frequency, travel distance, trip chaining, mode choice, and time use. In 42 articles reviewed, we found more evidence that online shopping substitutes for shopping travel. Most studies to date have focused on trip frequency but neglected other travel outcomes. Very few studies have considered the modification effect, which has significant implications for travel demand management. In sum, previous studies have not reached a consensus on the dominant effect of online shopping, in part due to the diversity in variable measurements, types of goods, study areas, and analytic methods. A limitation of previous studies is the reliance on cross-sectional surveys, which hinders the distinction between short- and long-term behaviours and between modification, complementarity, and substitution effects. Our study provides an agenda for future research on this topic and discusses policy implications related to land use, behavioural changes, data collection, and modelling for practitioners who wish to incorporate e-commerce in planning for sustainable urban systems. [ABSTRACT FROM AUTHOR]

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 the user 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 application (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 the system with 135 subjects to learn whether subjects will 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. [ABSTRACT FROM AUTHOR]