Annesha Enam

Exploration of Short-Term Vehicle Utilization Choices in Households with Multiple Vehicle Types

With the growing concerns of energy sustainability, greenhouse gas emissions, and climate change, there is an increasing interest in understanding vehicle ownership and utilization decisions better so that effective policies can be implemented to reduce the negative impacts of private automobile usage. Although there is a rich body of literature on the long-term decisions of vehicle ownership and the composition of vehicles, the short-term choices of which vehicle to use from the households vehicle holdings and what distance will be traveled to access opportunities, as well as the interrelationship between the two, are less understood. The purpose of this study was to contribute to the literature on short-term vehicle utilization decisions with the use of data collected in 2009 from the National Household Travel Survey. A latent class segmentation model was estimated with alternate interrelationship structures as the latent classes. Within each latent class, the choices were modeled consistently with the interrelationship structure through the introduction of the first choice as an explanatory variable in the model of the second choice. Additionally, scale was introduced to account for differences in the choices and interrelationships across regions. Most of the model estimation results were behaviorally plausible and consistent with expectations. A significant finding was that interrelationships in both latent classes were insignificant. It was also found that the latent model, even with the insignificant interrelationships, outperformed the alternate model formulations in terms of model fit. This finding shows that the latent segments may capture unobserved heterogeneity beyond the interrelationships.

Activity Pattern Models with Well-Being Indicators

Activity-based travel demand models, particularly a class widely used in practice known as the activity schedule approach, constitute the state of the art in metropolitan travel demand forecasting. In a 2012 paper, Abou-Zeid and Ben-Akiva proposed two extensions to the specification of the activity pattern model in the activity schedule approach. This paper focuses on the econometric extension, which involves adding happiness measurement equations to an activity pattern model to enhance the measurement of utility. This extension is tested with the activity schedule model system and data provided by the Denver Regional Council of Governments in Colorado. Furthermore, a comparison of two criteria—the goodness of fit and the efficiency—of activity pattern models with and without happiness measurement equations is presented. The results indicate that happiness measures enhance the efficiency of the estimates of the activity pattern model.

Time Allocation Behavior of Twentieth-Century American Generations: GI Generation, Silent Generation, Baby Boomers, Generation X, and Millennials

In recent years, time engagement behaviors of two generations, namely Baby Boomers and Millennials have sparked much interest because these generations constitute the bulk of the American population today and they also exhibit “atypical” activity–travel patterns compared with other generations. The objective of the current research is to conduct a systematic study of the time engagement behaviors of five American generations: the GI Generation (birth year: 1901–1924), the Silent Generation (birth year: 1925–1943), Baby Boomers (birth year: 1944–1964), Generation X (birth year: 1965–1981), and Millennials (birth year: 1982–2000). Particularly, the study aims at isolating heterogeneity in behaviors associated with structural changes in the society from those associated with inherent generational characteristics. Using data from four waves (1965, 1985, 2005, and 2012) of the American Heritage and Time Use Study, the analysis explores the time engagement behaviors while accounting for the age, period, and cohort effects in addition to different socioeconomic and demographic variables. The analysis reveals that Millennials have generally delayed participation in life-changing events such as marriage and workforce entry, and have exhibited prolonged student status compared with previous generations. Millennials show lower participation in work and higher participation in discretionary activities compared with individuals of the same age group from previous generations. On the other hand, Baby Boomers clearly exhibited increased travel engagement compared with the previous generations at different stages of their lives.

Day Pattern Generation System for Jointly Modeling Tours and Stops: Bi-Level Multiple Discrete Continuous Probit Model

The primary objective of this study was to contribute to the literature on activity pattern generation. In this paper, a new framework is proposed for simultaneously modeling the following tour and stop-making decisions: the number and purpose of tours conducted in a day, time allocated to different tours, number and purpose of stops conducted within each tour, and time allocated to different stops. The framework represents time as a continuous entity and explicitly considers the time constraints within which an individual operates when generating tours and stops. In addition, the framework is capable of accounting for the interrelationships across different tour- and stop-level decisions. The model formulation that operationalizes the proposed framework imitates a bi-level structure in which the participation (whether to pursue) and time allocation (how much time) decisions for daily tours are modeled at the upper level. Within each tour, participation and time allocation decisions for different stops are modeled at the lower level. The model formulation for the bi-level structure builds on the utility theoretic multiple discrete continuous probit modeling approach. The proposed framework and model formulation are demonstrated with an empirical case study using data from the 2008–2009 National Household Travel Survey. Replication and forecasting results are presented to demonstrate the feasibility and applicability of the proposed framework and model formulation. The results provide evidence in support of the bi-level structure and its ability to capture the various constraints and interrelationships across tour- and stop-level participation and time allocation decisions.