Venu M Garikapati

Activity patterns, time use, and travel of millennials: a generation in transition?

ABSTRACT Millennials, defined in this study as those born between 1979 and 2000, became the largest population segment in the United States in 2015. Compared to recent previous generations, they have been found to travel less, own fewer cars, have lower driver’s licensure rates, and use alternative modes more. But to what extent will these differences in behaviour persist as millennials move through various phases of the lifecycle? To address this question, this paper presents the results of a longitudinal analysis of the 2003–2013 American Time Use Survey data series. In early adulthood, younger millennials (born 1988–1994) are found to spend significantly more time in-home than older millennials (born 1979–1985), which indicates that there are substantial differences in activity-time use patterns across generations in early adulthood. Older millennials are, however, showing activity-time use patterns similar to their prior generation counterparts as they age, although some differences – particularly in time spent as a car driver – persist. Millennials appear to exhibit a lag in adopting the activity patterns of predecessor generations due to delayed lifecycle milestones (e.g. completing their education, getting jobs, marrying, and having children) and lingering effects of the economic recession, suggesting that travel demand will resume growth in the future.

Integrating life-cycle environmental and economic assessment with transportation and land use planning

The environmental outcomes of urban form changes should couple life-cycle and behavioral assessment methods to better understand urban sustainability policy outcomes. Using Phoenix, Arizona light rail as a case study, an integrated transportation and land use life-cycle assessment (ITLU-LCA) framework is developed to assess the changes to energy consumption and air emissions from transit-oriented neighborhood designs. Residential travel, commercial travel, and building energy use are included and the framework integrates household behavior change assessment to explore the environmental and economic outcomes of policies that affect infrastructure. The results show that upfront environmental and economic investments are needed (through more energy-intense building materials for high-density structures) to produce long run benefits in reduced building energy use and automobile travel. The annualized life-cycle benefits of transit-oriented developments in Phoenix can range from 1.7 to 230 Gg CO2e depending on the aggressiveness of residential density. Midpoint impact stressors for respiratory effects and photochemical smog formation are also assessed and can be reduced by 1.2-170 Mg PM10e and 41-5200 Mg O3e annually. These benefits will come at an additional construction cost of up to

Development of Vehicle Fleet Composition Model System for Implementation in Activity-Based Travel Model

410 million resulting in a cost of avoided CO2e at

Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies

16-29 and household cost savings.

Multiple discrete-continuous model of activity participation and time allocation for home-based work tours

The development of a vehicle fleet composition and utilization model system that may be incorporated into a larger activity-based travel demand model is described. It is of interest and importance to model household vehicle fleet composition and utilization behavior because the energy and environmental impacts of personal travel are dependent not only on the number of vehicles but also on the mix of vehicles that a household owns and the extent to which different vehicles are used. A vehicle composition (fleet mix) and utilization model system was developed for integration into the activity-based travel demand model that was being developed for the greater Phoenix metropolitan area in Arizona. At the heart of the vehicle fleet mix model system is a multiple discrete continuous extreme value model capable of simulating vehicle ownership and use patterns of households. Vehicle choices are defined by a combination of vehicle body type and age category and the model system is capable of predicting vehicle composition and utilization patterns at the household level. The model system is described and results are presented of a validation and policy sensitivity analysis exercise demonstrating the efficacy of the model.

Latent-Segmentation-Based Approach to Investigating Spatial Transferability of Activity-Travel Models

Considerable interest exists in modeling and forecasting the effects of autonomous vehicles on travel behavior and transportation network performance. In an autonomous vehicle (AV) future, individuals may privately own such vehicles, use mobility-on-demand services provided by transportation network companies that operate shared AV fleets, or adopt a combination of those two options. This paper presents a comprehensive model system of AV adoption and use. A generalized, heterogeneous data model system was estimated with data collected as part of the Puget Sound, Washington, Regional Travel Study. The results showed that lifestyle factors play an important role in shaping AV usage. Younger, urban residents who are more educated and technologically savvy are more likely to be early adopters of AV technologies than are older, suburban and rural individuals, a fact that favors a sharing-based service model over private ownership. Models such as the one presented in this paper can be used to predict the adoption of AV technologies, and such predictions will, in turn, help forecast the effects of AVs under alternative future scenarios.

Estimating Household Travel Energy Consumption in Conjunction with a Travel Demand Forecasting Model

Activity-based travel demand models use the notion of tours or trip chains as the fundamental building blocks of daily traveler activity-travel patterns. Travelers may undertake a variety of tours during the course of a day, and each tour may include one or more stops where individuals participate in and devote time to the pursuit of activities. This paper presents a framework capable of simulating the complete composition of a tour and offers an approach to model the mix of activities and the time allocated to various activities in a tour. Embedded in the framework is a multiple discrete-continuous extreme value modeling component that was used to model the simultaneous decisions of participating in one or more activities in the course of a tour and of allocating time to each of the activities in the tour. The model was estimated with travel survey data collected in 2008 in the Greater Phoenix Metropolitan Area in Arizona. Validation and policy simulation exercises were conducted to examine the efficacy of the model. The model was found to perform well in replicating tour patterns in the estimation sample and responded in a behaviorally intuitive manner in the context of a policy sensitivity test.

Investigation of Heterogeneity in Vehicle Ownership and Usage for the Millennial Generation

Spatial transferability of travel demand models has been an issue of considerable interest, particularly for small- and medium-sized planning areas that often do not have the resources and staff time to collect large-scale travel survey data and estimate model components native to the region. Traditional approaches to identifying geographic contexts that may borrow and transfer models between one another involve the exogenous a priori identification of a set of variables used to characterize the similarity between geographic regions. However, this ad hoc procedure presents considerable challenges because it is difficult to identify the most appropriate criteria a priori. To address that issue, this paper proposes a latent segmentation approach: the most appropriate criteria for identifying areas with similar profiles are determined endogenously in the model estimation phase. The end products are a set of optimal criteria for clustering regions as well as a fully transferred model, segmented to account for heterogeneity in the population. The method is demonstrated, and its efficacy established through a case study in this paper that uses the National Household Travel Survey data set for information on weekday activities of nonworkers in nine regions in the states of California and Florida. The estimated model is then applied to a context withheld from the original estimation to assess its performance. The method is found to offer a robust mechanism for identifying latent segments and establishing criteria for transferring models between areas.

Introducing latent psychological constructs in injury severity modeling: Multivehicle and multioccupant approach

This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.

Enhanced Synthetic Population Generator That Accommodates Control Variables at Multiple Geographic Resolutions

This study explored differences in activity travel behavior within the millennial generation to understand better how their choices might shape transportation systems of the future. Through the estimation of a generalized heterogeneous data model on a mobility attitude survey data set targeting millennials, this study investigates heterogeneity among millennials with respect to their driver’s license–holding status, vehicle ownership, and commute mode choice. After self-selection effects are accounted for, age, parenting status, and location of residence have a substantial and statistically significant influence on automobile-oriented mobility choices. Millennials seem to become more automobile-oriented as they age and gain economic resources. Parenthood is associated with an increase in driver’s license holding and personal vehicle ownership; however, in general, it does not seem to have a direct effect on commute mode choice. For all types of millennials, mode choice seems to be strongly correlated with residence location. Thus, the development of a well-connected public transit system and dense, mixed land use are still the key ingredients for reducing the car commute. Planning professionals should explore ways to retain millennials in the city core so that their sustainable patterns of transportation mode use can be preserved.