Maya Abou-Zeid

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 the user to alternate mode or trip decisions that are more sustainable or otherwise beneficial to society, while still meeting the subjects 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.

Hybrid choice models

Choice modelling is an increasingly important technique for forecasting and valuation, with applications in fields such as transportation, health and environmental economics. For this reason it has attracted attention from leading academics and practitioners and methods have advanced substantially in recent years. This Handbook, composed of contributions from senior figures in the field, summarises the essential analytical techniques and discusses the key current research issues. It will be of interest to academics, students and practitioners in a wide range of areas.

Mass effects and mobility decisions

Abstract This paper presents a review of the literature on mass effects and their importance in choice behavior, i.e. how individual behavior is influenced by the behavior of others, with the goal of extracting lessons for transportation policy. An overview of psychological theories explaining the process underlying mass effects and conformity behavior is given. This is followed by a presentation of evidence of mass effects on choice behavior both outside and within transportation planning, covering contexts ranging from long term choices such as residential location to short term decisions such as driving behavior. Based on this review, modeling approaches for studying mass effects and their data requirements are then synthesized, highlighting the advantages and limitations of each. The paper concludes with a discussion of the importance of leveraging the power of mass effects for designing transportation policies aimed at promoting sustainable and safe mobility, and of challenges for future work in this area.

A Model of Travel Happiness and Mode Switching

Abstract In previous research (Abou-Zeid et al., 2008), we postulated that people report different levels of travel happiness under routine and nonroutine conditions and supported this hypothesis through an experiment requiring habitual car drivers to switch temporarily to public transportation. This chapter develops a general modeling framework that extends random utility models by using happiness measures as indicators of utility in addition to the standard choice indicators, and applies the framework to modeling happiness and travel mode switching using the data collected in the experiment. The model consists of structural equations for pretreatment (remembered) and posttreatment (decision) utilities and explicitly represents their correlations, and measurement equations expressing the choice and the pretreatment and posttreatment happiness measures as a function of the corresponding utilities. The results of the empirical model are preliminary but support the premise that the extended modeling framework, which includes happiness, will potentially enhance behavioral models based on random utility theory by making them more efficient.

Methodological issues in modelling time-of-travel preferences

We address three methodological issues that arise when modelling time-of-travel preferences: unequal period lengths, schedule delay in the absence of desired time-of-travel data and the 24-hour cycle. Varying period length is addressed by using size variables. Schedule delay is treated by assuming either arrival or departure time sensitivity and using market segment specific utility functions of time-of-travel, or using distributions of the desired times-of-travel. The 24-hour cycle is modelled by using a trigonometric utility functional form. These methodologies are demonstrated in the context of a tour-based travel demand model using the 2000 Bay Area travel survey.

Minnesota Pay-as-You-Drive Pricing Experiment

The Minnesota Department of Transportation carried out a pay-as-you-drive demonstration simulating the replacement of the fixed costs of vehicle ownership and operation with variable costs that gave drivers explicit price signals about travel decisions and alternatives. The objective was to estimate the reduction in mileage due to the mileage-based pricing scheme. The study consisted of market assessment surveys and a field experiment. The experiment is the focus of this paper. The experimental design divided participants into three groups: a control-only group, a treatment-then-control group, and a control-then-treatment group. Participants in the treatment phase were subjected to per-mile prices, and the mileage of all participants was recorded for the entire study duration. Two types of analyses were conducted. Aggregate analyses using bootstrap methods to determine groupwise changes in mileage showed that participants reduced their mileage when charged on a per-mile basis, with the greatest reduction during the summer period when trips could be more discretionary in nature. In addition, to understand better the variance in mileage sensitivity to per-mile prices, disaggregate analyses were performed by using a matching method that matched members of the treatment group to those of the control group based on the probability of participation in the experiment and their baseline mileage. The resulting percentage change in mileage was regressed against the percentage change in price and lifestyle variables. The price elasticity of peak-period mileage was found to be negative. However, in both aggregate and disaggregate analyses, the price effect was statistically insignificant as a result of the small sample size.

Modeling anger and aggressive driving behavior in a dynamic choice–latent variable model

This paper develops a hybrid choice-latent variable model combined with a Hidden Markov model in order to analyze the causes of aggressive driving and forecast its manifestations accordingly. The model is grounded in the state-trait anger theory; it treats trait driving anger as a latent variable that is expressed as a function of individual characteristics, or as an agent effect, and state anger as a dynamic latent variable that evolves over time and affects driving behavior, and that is expressed as a function of trait anger, frustrating events, and contextual variables (e.g., geometric roadway features, flow conditions, etc.). This model may be used in order to test measures aimed at reducing aggressive driving behavior and improving road safety, and can be incorporated into micro-simulation packages to represent aggressive driving. The paper also presents an application of this model to data obtained from a driving simulator experiment performed at the American University of Beirut. The results derived from this application indicate that state anger at a specific time period is significantly affected by the occurrence of frustrating events, trait anger, and the anger experienced at the previous time period. The proposed model exhibited a better goodness of fit compared to a similar simple joint model where driving behavior and decisions are expressed as a function of the experienced events explicitly and not the dynamic latent variable.

Satisfaction and Travel Choices

Linkages between activities, travel, and overall subjective well-being (SWB) are analyzed. SWB is broadly defined as the evaluation of the cognitive and affective components of human experiences. Developments in the measurement of subjective well-being (SWB) and the application of SWB research to travel are reviewed, with a particular emphasis on a modelling framework linking SWB to travel attributes and travel behaviour. Empirical evidence from the measurement of SWB in activities and travel has shown that travel plays a role for overall well-being and that the timing at which travel well-being is measured matters due to the dynamic nature of well-being. It is argued that SWB and utility are the same but a distinction needs to be made among the different notions of utility. Consequently, an extended random utility model framework with SWB measures as additional indicators of utility is presented, and an application of this framework to travel mode choice is shown to yield an improved model of travel mode choice.

Pedestrian–vehicular interactions in a mixed street environment

ABSTRACT We develop a methodology to analyze pedestrian-vehicular interactions in urban streets in a mixed traffic environment, and then apply it to Bliss Street, an urban street in Beirut. Data on the street was collected before and after a crosswalk was installed using videography, radar speed guns, and manual counts. A pedestrian gap acceptance model indicated that installing the crosswalk did not have any significant effect on the pedestrians’ sensitivity to waiting time, gap size, or the speed of the approaching vehicles. However, it caused reductions in the speed of approaching vehicles which in turn encouraged pedestrians to accept shorter gaps. A micro-simulation model indicated that the crosswalk would reduce the speed on the street slightly, with significant reductions observed if more pedestrians who currently cross at midblock locations shift to use the crosswalk. The results of this study can be used to test interventions for enhancing pedestrian safety in Lebanon, and are generalizable to similar contexts in developing countries.

Comparative assessment of driving behavior at signalized intersections using driving simulators

ABSTRACT The objective of this article is to investigate the differences in driving behavior and red-light violations between drivers in two countries: Lebanon and the United States of America. To realize the stated objective, two driving simulators were utilized. The first simulator is located at the American University of Beirut (AUB), Lebanon. The second simulator is located at the George Washington University (GWU), United States of America. An elaborate experimental scheme involving the occurrence of frustrating events at signalized intersections was designed, and 35 students from GWU and 81 students from AUB participated in the experiments. Detailed trajectory data was collected, and students were compared based on three surrogate measures: number of red-light violations, time-to-junction, average and maximum velocities. The results indicated that frustrating events occurring at intersections elicit red-light violations and speeding for both samples. In addition, speeding and red-light violations follow increasing trends as students drive through successive signalized intersections and experience frustrating events. In a postdriving survey, AUB students indicated that they engage in risky driving behavior more than GWU students. On the other hand, GWU students indicated that they are more likely to violate traffic rules and also committed more red-light violations in the simulator. The results of this study have implications on driving education and enforcement, as they indicate that driving violations might not be necessarily related to risky or aggressive driving, but to an individuals tendency to violate traffic rules in general.