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In this study, the most recent American Time Use Surveys containing reported activity-based emotions and sensations information before (10,378 respondents in 2013) and during (6,902 respondents in 2021) the COVID-19 pandemic are used to assess if time use related individuals' subjective wellbeing (SWB) decreased in the pandemic. Given that the coronavirus has been shown to strongly influence activity decisions and social interactions, sequence analysis is applied to find daily time allocation patterns and changes in daily time allocation. Then, those derived daily patterns and other activity-travel factors, as well as social and demographic, temporal, spatial, and other contextual characteristics are added as explanatory variables in regression models of SWB measures. This provides a holistic framework of exploring the direct and indirect effects (via activity-travel schedules) of the recent pandemic on SWB while controlling for contexts such as the life assessments, daily schedule of activities, and living environment. The results show that respondents in the COVID year reported a new time allocation pattern that has a substantial amount of time at home, and they experienced more negative emotions. Three relatively happier daily patterns in 2021 contained substantial amounts of outdoor and indoor activities. In addition, no significant correlation was observed between metropolitan areas and individuals' SWB in 2021. However, comparisons among states show Texas and Florida residents experienced more positive wellbeing presumably due to fewer COVID-related restrictions.

Movement is manifested through a series of patterns at multiple spatial and temporal scales. Movement data today are becoming available at increasingly fine-grained temporal granularity. These observations often represent multiple behavioral modes and complex patterns along the movement path. However, the relationships between the observation scale of movement data and the analysis scales at which movement patterns are captured remain understudied. This article aims at investigating the role of temporal scale in movement data analytics. It takes up an important question of “how do decisions surrounding the scale of movement data and analyses impact our inferences about movement patterns?” Through a set of computational experiments in the context of human movement, we take a systematic look at the impact of varying temporal scales on common movement analytics techniques including trajectory analytics to calculate movement parameters (e.g., speed, path tortuosity), estimation of individual space usage, and interactions analysis to detect potential contacts between multiple mobile individuals.

This paper uses movement as a marker to study interactions in humans and animals to better understand their collective behaviors. Interaction is an important driving force in social and ecological systems. It can also play a significant role in the transmission of infectious diseases and viruses as witnessed during the ongoing COVID-19 pandemic. Although a number of approaches have been developed to analyze interaction using movement data sets, these methods mainly capture concurrent and dyadic interaction (i.e. when two individuals have direct contact or move synchronously in the spatial proximity of each other). Less work has been done on tracing interaction between multiple individuals, especially when the interaction occurs with a delay or via indirect contact (i.e. when individuals visit the same location asynchronously). This paper introduces a new Object-oRiented Time-Geographic Analytical approach (ORTEGA) to extract concurrent and delayed interaction patterns between individuals in space and time. The method leverages the time-geography framework to incorporate the effects of uncertainty and gaps in movement data in the analysis of interaction and tracing contact patterns. Using two different case studies and real GPS tracking data, the method is evaluated in (1) detecting patterns of dyadic, intra and interspecific interactions between two apex predators, tigers and leopards in Thailand; and (2) tracing potential contacts between a large group of individuals of the same and different households in San Jose, California. The results indicate that tigers and leopards have an awareness of each other and their interaction is mainly indirect and delayed. In the human context, the results show that while individuals of the same household have more concurrent interaction, members of different households follow similar patterns asynchronously exhibiting delayed interaction. The delayed interactions and potential asynchronous contacts are often underestimated by the common digital contact tracing technologies. With this study we show how a generic method can be used to identify interesting movement patterns across the human and animal divide.

This report provides a summary of analyses using data of long distance tours by each household from an 8-week California Household Travel Survey travel log. The first analysis, uses Structural Equations Models (SEM) and a simpler variant called Path Analysis on three censored variables (tour miles by air, miles driving, and miles by public transportation) and two categorical variables (main trip tour purpose) and number of overnight stays. The second analysis, uses Latent Class Cluster Analysis (LCCA) to identify five distinct, informative patterns of long-distance travel. This analysis shows that long-distance tours for vacation, business travel, medical, and shopping are substantially distinct in terms of their travel characteristics and correspond to different combinations of other activities in the tour and they are done by different types of households. The methods used here to identify the typology of long distance travel can be easily expanded to include a variety of other explanatory variables of this type of behavior in more focused data collection settings.

The objective of this report is to first review what we know from the literature about long distance travelers, analyze the contents of the long distance travel log of the California Household Travel Survey (CHTS), demonstrate the augmentation of the trip/tour records with destination attractiveness indicators, derive prototypical traveler profiles, and provide amore detailed analysis of long distance tours. The data are from a simplified travel log that asked respondents from households to report all the trips 50 miles or longer they made in the 8-weeks preceding the day they were assigned a full travel diary. The survey instrument used for this reporting is shown in Figure 1. In this report we identify a few issues with the data collected using this travel log, and these issues motivate us to also investigate the long distance travel reported in the daily diary. The range of variables that we can analyze depends heavily on the accuracy with which respondents reported their trips, and we found they were generally more accurate in the daily diary. However, the long distance travel log contains data that span longer periods than 24 hours and therefore a better record of trips with overnight stays away from home. Past studies of long distance travel have found that commuting by people who sought out lower cost housing is a major contributor to long distance travel, and that higher income and employed persons travel more, but there are multiple shortcomings in the literature that we seek to address here. The literature contains a variety of definitions for “long distance” travel, including ones based on distance (e.g., longer than 50 miles, 100 miles, or longer than 100 kilometers) and travel time (e.g., 40 minutes). Long distance travel researchers have considered a variety of indicators including number of long distance trips, activity before and/or after commute, mode used, time of day of trip, and destination (Georggi and Pendyala, 2000, Axhausen, 2001, Beckman and Goulias, 2008, LaMondia and Bhat, 2011, Caltrans, 2015, Shahrin et al., 2014, Holz-Rau et al., 2014). Most studies did not address trip chaining (e.g., people going to a work place, then to a leisure destination, and then back home). Very little analysis is also found in differentiating trips with an overnight stay, despite the important differences between these trips and daily commuting. The choice of analysis in past studies was presumably driven by: a) an emphasis in the literature on trips to and from work; and b) a focus on a single trip by an individual person as the unit of analysis instead of multiple trips from household members. This focus on commute trips is also reflected in the multitude of person factors used to explain variation in travel behavior in past research (Table 1.1). Table 1.1 also shows household and location characteristics that have been considered as determinants of long distance travel behavior. It is also important to note that a few researchers (de Abreu et al., 2006, 2012) consider long distance travel, car ownership, and residential and job location (and the distance between the two) as a system of joint decisions that are best explained using methods that can disentangle the complex relationships among all these behavioral facets. From this viewpoint, long distance travel (particularly for commuters) cannot be separated from the choice of work and home location and should be modeled jointly. The review in Mitra (2016) is particularly useful in mapping recent literature on long distance travel and its determinants. His findings are exactly what one would expect: age, gender, education, employment and occupation, car ownership, household structure, place of residence and workplace as well as housing cost and accessibility influence long distance travel in a variety of ways. His analysis also shows that developing traveler profiles at the level of a household (rather than the individual) is a better choice to understand how and why long distance travel happens, and our analysis follows this lead. In another analysis of CHTS, Bierce and Kurth (2014) identified an issue of underreporting of repetitive trips in the 8-week long distance data. In essence, long distance commuters did not report all their commuting trips. We find that this underreporting may also exist for longer trips, though less severely than it does for shorter ones. Identifying the correct mix of distances and overall volume of travel is particularly important when one seeks to estimate the contribution of VMT from long distance travel to California estimates of VMT (see also Chapman, 2007).

Accessibility, defined as the ease (or difficulty) with which activity opportunities can be reached from a given location, can be measured using the cumulative amount of opportunities from an origin within a given amount of travel time. These indicators can be used in regional planning and modeling efforts that aim to integrate land use with travel demand and an attempt should be made to compute at the smallest geographical area. The primary objective of this paper is to illustrate the creation of realistic space-sensitive and time-sensitive fine spatial level accessibility indicators that attempt to track availability of opportunities. These indicators support the development of the Southern California Association of Governments activity-based travel demand forecasting model that aims at a second by-second and parcel-by-parcel modeling and simulation. They also provide the base information for mapping opportunities of access to fifteen different types of industries at different periods during a day. The indicators and their maps are defined for the entire region using largely available data to show the polycentric structure of the region and to illustrate the method as a generator of choice sets in discrete choice models.

With the increased application of the activity based approach comes the inherent need to incorporate more detail regarding behavior. This need for detail has in turn created a need for both a deeper understanding and theoretical basis for behavior, and the incorporation of data collection and analysis methods to handle more behavioral detail. Because of this, the use of qualitative and mixed method approaches in travel behavior has received increased attention over the last few decades. In this paper, quantitative and qualitative methodologies are discussed and applied to data collected in Santa Barbara, California, measuring peoples’ attitudes about places (sense of place). Both quantitative and qualitative methods are applied using first a factor analysis and complementing this with a qualitative analysis of text from an open-ended question. The findings of these analyses are compared and incorporated to contribute to a greater understanding of both sense of place and behavior. Theoretical developments and implications for future research are discussed in light of analysis findings.

With the introduction and increasing reliance on the activity based approach in travel demand analysis and forecasting, discrete choice methods are more often in spatial contexts (e.g., residential location, job location, destination/activity choice). A necessity in specifying spatial choice models is the inclusion of the alternatives decision makers consider, and a realistic inclusion in the specification of the attributes of these alternatives, the characteristics of the decision making context, and the relevant characteristics of the decision maker. These details describe differences that exist among choices and individuals making choices. In the case of travel behavior, attributes of the alternatives have traditionally included attributes such as cost, distance, time, level of service and opportunities. Researchers however have recognized the benefit of attitudes in the estimation of choice models, showing improvement in explanatory power with the inclusion of attitudinal attributes of the individual. There is still however a vast expanse of unexplored attitudinal attributes in choice modeling. Particularly lacking in choice modeling is a strong theoretical underpinning of attitude formation and attitude relation to choice. Theorists in geography in the mid to late 1970s recognized and developed one such theory regarding the emotional and attitudinal association that people have with places. This became known as the theory of sense of place, which is the “affective ties with the material environment” (Tuan, 1974). This theory presents great potential in furthering the descriptive power of choice models, particularly destination choice. However, challenges abound, as this theory is rich in development, but poor in computational operationalization. In addition, everyday activity locations have not been adequately explored in sense of place quantification. In this paper, an overview of developments in discrete choice methods is presented, followed by a discussion of sense of place. Current and future work is discussed and benefits to choice modeling are presented.

Accessibility, defined as the ease (or difficulty) with which activity opportunities can be reached from a given location, can be measured using the cumulative amount of opportunities from an origin within a given amount of travel time. These indicators can be used in regional planning and modeling efforts that aim to integrate land use with travel demand and an attempt should be made to compute at the smallest geographical area. The primary objective of this paper isto illustrate the creation of realistic space-sensitive and time-sensitive fine spatial level accessibility indicators that attempt to track availability of opportunities. These indicators support the development of the Southern California Association of Governments activity-based travel demand forecasting model that aims at a second-by-second and parcel-by-parcel modeling and simulation. They also provide the base information for mapping opportunities of access to fifteen different types of industries at different periods during a day. The indicators and their maps are defined for the entire region using largely available data to show the polycentric structure of the region and to illustrate the method as a generator of choice sets in discrete choice models.

With the introduction and increasing reliance on the activity based approach in travel demand analysis and forecasting, discrete choice methods are more often in spatial contexts (e.g., residential location, job location, destination/activity choice). A necessity in specifying spatial choice models is the inclusion of the alternatives decision makers consider, and a realistic inclusion in the specification of the attributes of these alternatives, the characteristics of the decision making context, and the relevant characteristics of the decision maker. These details describe differences that exist among choices and individuals making choices. In the case of travel behavior, attributes of the alternatives have traditionally included attributes such as cost, distance, time, level of service and opportunities. Researchers however have recognized the benefit of attitudes in the estimation of choice models, showing improvement in explanatory power with the inclusion of attitudinal attributes of the individual. There is still however a vast expanse of unexplored attitudinal attributes in choice modeling. Particularly lacking in choice modeling is a strong theoretical underpinning of attitude formation and attitude relation to choice. Theorists in geography in the mid to late 1970s recognized and developed one such theory regarding the emotional and attitudinal association that people have with places. This became known as the theory of sense of place, which is the “affective ties with the material environment” (Tuan, 1974). This theory presents great potential in furthering the descriptive power of choice models, particularly destination choice. However, challenges abound, as this theory is rich in development, but poor in computational operationalization. In addition, everyday activity locations have not been adequately explored in sense of place quantification. In this paper, an overview of developments in discrete choice methods is presented, followed by a discussion of sense of place. Current and future work is discussed and benefits to choice modeling are presented.

In this project we develop a new travel demand forecasting system that integrates demographic microsimulation with urban simulation and travel demand model systems. Our research objective is to identify the barriers in integrating complex simulation models and eliminate them by offering a demonstration of problems and solutions. The basic ingredients of this new model system are: a) a dynamic demographic simulator designed and tested with repeated observations of the same individuals in another context that will be transferred to a case study in Santa Barbara, CA; b) a modified version of the recently finalized Urbansim model that will also be calibrated with data from Santa Barbara, CA; and c) travel demand models that account for intrahousehold interactions and path based accessibility that were estimated with data from California. The model system is unique because it combines within a day and across years human behavior dynamics and it will push the frontier of modeling and simulation one step further. A demonstration of a pilot test is offered using data from Santa Barbara, CA.

Current trends in transportation and urban planning are moving in the direction of integrated land use and transportation models. These models require an understanding and accurate representation of the propensity of a location to host an activity, and thus generate a need for travel. Within this necessity to represent activity propensity comes an often-neglected area of human-spatial interaction—the attractiveness of a location. The propensity of an individual to travel to a specific location is highly subjective, and can vary from person to person and develop with exposure to the location, an attribute, which is said to be experiential in nature. This experiential aspect of place has been termed by those in geography and environmental psychology as “sense of place”. An intercept survey was thus designed to attempt to measure this sense of place and gain an understanding of how this experiential aspect of place differs among people and between locations. A series of statistical analyses of the data explore differences that exist in sense of place attitudes, as well as impacts of sense of place on travel behavior. Lastly, extensions of this pilot research project are described and future applications are discussed.

The overall objective of this project is to develop an optimal resource allocation tool for the entire state of California using Geographic Information Systems and widely available data sources. As this tool evolves it will be used to make investment decisions in transportation infrastructure while accounting for their spatial and social distribution of impacts. Tools of this type do not exist due to lack of suitable planning support tools, lack of efforts in assembling data and information from a variety of sources, and lack of coordination in assembling the data. Suitable planning support tools can be created with analytical experimentation to identify the best methods and the first steps are taken in this project. Assembly of widely available data is also demonstrated in this project. Coordination of fragmented jurisdictions remains an elusive task that is left outside the project. When this project begun we confronted some of these issues and embarked in a path of feasibility demonstration in the form of a pilot project that gave us very encouraging results. In spite of this pilot nature aiming at demonstration of technical feasibility, substantive conclusions and findings are also extracted from each analytical step.In this project we have two parallel analytical tracks that are a statewide macroanalysis (called the zonal based approach herein) and an individual and household based microanalysis (called the person based approach herein). In the statewide macroanalysis we study efficiency and equity in resource allocation. Resources are intended as infrastructure availability and access to activity participation offered by the combined effect of transportation infrastructure and land use measured by indicators of accessibility. Stochastic frontiers are used to study efficiency and a particular type of inequality measurement called the Theil fractal inequality index is used to study equity in the macroanalysis. The outcome of this analysis are maps identifying places in California that enjoy higher levels of service when compared to the entire state and places which succeeded in allocating resources in a relatively better way than others. In the individual microanalysis we use the accessibility indicators from the macronalysis and expand them by defining a new set of indicators at a second level of spatial (dis)aggregation. Then we use them as explanatory factors of travel behavior with focus on the use of different travel models (e.g., driving alone, use of public transportation and so forth). As expected infrastructure availability and accessibility to activity opportunities has a significant and substantive effect on the use of different modes. Many resource allocation decisions, then, will impact behavior, which in turn influences the optimality and equity conditions. This implies that decisions about where and when to allocate resources in public and private transportation needs to account for changes in behavior in a dynamic fashion, using scenarios of accessibility provision and assessing their impact by studying activity and travel behavior changes.

Many studies have examined the impact that the built environment has on physical activity, and much of the existing research posits that if communities will provide and improve active infrastructure such as trails, sidewalks, and bike lanes, people will become more physically active. However, most of these studies have used cross-sectional methods which have allowed them to establish correlations but not behavioral causality. In this pilot project a longitudinal design is used to evaluate the impact neighborhood trail development to assess a trail construction impact on active travel behavior and overall physical activity among suburban residents. A sample of suburban residents in West Valley City, Utah was surveyed both before and after the construction of a class-one trail in their neighborhood using a preliminary household survey, individual activity diaries completed at three pre-assigned time points (before and twice after the trail’s construction), new resident surveys, and a trail user’s intercept survey. This intervention technique performs a more direct test of causality by looking at the same group of residents over time and analyzing if individual changes in behavior occur following the construction of the trail. In the paper we show that trail neighborhood residents did not use the facility after it was build, new residents did not move to the neighborhood because of the trail, and the users of the trail come from elsewhere. We also report trail amenities that appear to be more desirable.

In this study using data from Sylhet, Bangladesh, transportation expenditures are examined in detail for different income groups living in different natural and built environments. We employ Tobit models and a dataset of more than 22,000 persons in 4162 households to correlate transportation expenditures by separate income groups to land use, transportation and other infrastructure, as well as household and worker within household characteristics. Among other findings, urban high income households are not negatively affected by their environments. They have the ability to increase their transportation expenditures to reach most destinations and have access to almost all opportunities. This is vastly different from the urban poor; they are restricted by distance because most of their trips take place on foot. Moreover, rural workers are less affected by environmental indicators, presumably because they are restricted by their location and the lack of road networks near their residence. The lack of modal choices may be the determining factor on transportation expenditures. Lack of basic services increase expenditures, showing a link between underdeveloped or temporary housing and lack of near work opportunities.

In this paper land use densities by type of employment and infrastructure supply are used together with social and demographic characteristics to explain non-motorized travel, transit use, and solo driving in California. The land use database, the highway network database, and the travel survey used for the analysis here covers the entire state of Califonia. Land use and infrastructure have a significant, substantial, and very different role for each behavior indicator used here. They alternate in significance and importance depending on the specific behavior analyzed. We also performed experiments to identify the appropriate geographical aggregation by comparing US Census tract vs US Census block group based land use densities and infrastructure densities. Regression models gave us mixed results leading us to suggest the use of a combination between the two geographies. Next steps are also outlined in the paper.

Physical inactivity has become a dominant feature of most American’s lives over the past quarter century. This has spurred an entire research domain straddling several different disciplines. Although model development within the field of travel behavior as a whole continues today with more momentum than ever, the focus on active mode choice has largely been overlooked and left to a small fragment of transportation and public health researchers. Research regarding active mode choice has been primarily conducted outside the field of travel behavior and has utilized research methods designed for other purposes. This leads to results which address behavioral causality in a superficial way while also neglecting the role of residential self-selection. This paper provides an overview of existing travel behavior analysis regarding active mode choice, presents potential threats to validity in this type of research, and critiques existing intervention methodologies. Additionally, a conceptual model of active travel behavior is presented and the roots of each component are discussed. By applying the rigor of travel behavior research to the subfield of active travel behavior research, and incorporating the conceptual model provided, great strides can be made relatively quickly in understanding animate mode choice and active travel behavior.

This paper addresses the relationship between travel behavior and land use patterns using a Structural Equations Modeling framework.The proposed model structure in this paper is by design heavily influenced by a model developed for Lisbon (1) to allow comparisons. In that paper the existence of significant effects of land use patterns in travel behavior was found. The travel behavior variables included in the model are multidimensional and comprehend both short term, number of trips by mode and trip scheduling, and long term, home location, car and pass ownership, mobility decisions. The modeled land use variables measure the levels of urban intensity and density, diversity, both in terms of types of uses and the mix between jobs and inhabitants and the public transport supply levels,. The land use patterns are described both at the residence and employment zones..In order to explicitly account for self selection bias the land use variables are explicitly modeled as functions of socioeconomic attributes of individuals and their households.The Seattle findings are presented and then compared them to the Lisbon findings. Many commonalities between the two environments were found but also many important differences.

This paper addresses the relationship between travel behavior and land use patterns using a Structural Equations Modeling framework.The proposed model structure in this paper is by design heavily influenced by a model developed for Lisbon (1) to allow comparisons. In that paper the existence of  significant effects of land use patterns in travel behavior was found. The travel behavior variables included in the model are multidimensional and comprehend both short term, number of trips by mode and trip scheduling, and long term, home location, car and pass ownership, mobility decisions. The modeled land use variables measure the levels of urban intensity and density, diversity, both in terms of types of uses and the mix between jobs and inhabitants and the public transport supply levels,. The land use patterns are described both at the residence and employment zones.. In order to explicitly account for self selection bias the land use variables are explicitly modeled as functions of socioeconomic attributes of individuals and their households. The Seattle findings are presented and then compared them to the Lisbon findings. Many commonalities between the two environments were found but also many important differences.

Assessments of transportation investment from a “social efficiency” viewpoint are absent from transportation policy analysis and marketing practice. This is mainly due to the lack of tools capable to assess the role of transportation infrastructure investment on the provision of activity opportunities to residents of each locality. In this report, we demonstrate a tool that identifies specific locations in an entire state where resource allocation has succeeded in maximizing benefits to the public. In addition, the tool and the Geographic Information System maps derived from this tool show which locations in California fail to be optimal and require their residents to travel excessively to pursue the same amount of activities when compared to other optimal locations around the state where travelling enables better time allocation. Efficiency is measured using stochastic frontier regression analysis and a wide variety of derived land use and transportation infrastructure indicators as inputs. The outputs examined are indices of location opportunities including retail, education, health, and manufacturing. The tool thus developed shows which demographic segments suffer the most from suboptimal time allocation and what type of investment is needed to alleviate this suffering. This new tool also shows the distribution of benefits of the transportation system and identifies differences in benefits across regions. In addition to the substantive findings about and mapping of the relative investment efficiency in California, this report presents a method to assess efficiency in a multi-objective environment.

The impressive movement forward of transportation modeling and simulation emerges from three related but distinct sources. The first source is a fundamental change in planning practice that one could name dynamic planning practice to indicate the existence of bi-directional time (from the past to the future and from the future to today), as well as, assessment cycles and adjustments taking place within the short term, medium term, and long term horizons. These cycles are also bidirectional in time. This source contains three fundamental directions of practice that are inventory creation and maintenance, strategy measurement and evaluation, forecasting and backcasting. The second source is a vision that generates the substantive problems that we need to solve and the specific policies we need to examine. It is named sustainable and green visions. Problems and solutions in this general area motivate and inspire contemporary substance and content of policies throughout the world. One can identify three complementary and mutually strengthening directions in the economy, environment, and society that are the three fundamental pillars of sustainability. The third source is the never ending research for improved understanding of the world surrounding us. This source is named new research and technology to capture the most important elements of new discovery and new techniques enabling new discovery but also modeling and simulation. Key directions of inquiry within research and technology are theory building, modeling and simulation, and enabling technologies.