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Introduction and the genome of travel behavior
Chapter 1
Introduction and the genome of travel behavior Konstadinos G. Gouliasa, Adam W. Davisb, Eizabeth C. McBrideb a
Department of Geography and GeoTrans Lab, University of California, Santa Barbara, CA, United states; bUniversity of California, Santa Barbara, CA, United states
Chapter outline 1. What is the travel behavior genome? 2. Wickedness of planning problems 3. Rapidly changing backdrop 4. Mapping the travel behavior genome 4.1 Substantive problems
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4.2 Theoretical & conceptual frameworks 4.3 Behavioral measurement 4.4 Behavioral analysis 5. Coda References
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Every three years, the International Association for Travel Behavior Research organizes a conference that spans the entire spectrum of the most important developments in travel behavior research (see IATBR.ORG). This is a unique opportunity for researchers from around the world to meet and exchange ideas about new issues and methods used to understand and predict behavior. In 2018, the conference took place on the University of California, Santa Barbara campus from July 15th to 20th (www.iatbr2018.org and IATBR2018). The conference theme, Mapping the Travel Behavior Genome, highlights the great progress in developing tools to understand and map the fundamental elements of travel behavior. The conference included methods to unravel human activity-travel behavior, showcasing state-of-the-art advancements in research and practical applications. This includes the biological, motivational, cognitive, situational, and dispositional factors that drive activity-travel behavior. The conference also addressed critical issues in developing urgently needed theories and analytical methods to address imminent upheavals that transform our lives. This book contains three major sections. The first section offers a retrospective and prospective survey of travel behavior research in four chapters from keynotes at the conference. The second section is the core of the book Mapping the Travel Behavior Genome. https://doi.org/10.1016/B978-0-12-817340-4.00001-2 Copyright © 2020 Elsevier Inc. All rights reserved.
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with twenty six chapters on new research methods and findings. The last section is a report from eight brainstorming workshops that describe the research frontier in travel behavior, research challenges, and offers many paths for future research. In this introduction we review the theme of our conference and provide a summary.
1. What is the travel behavior genome? First, travel behavior is about life. In our life, we progress through life course stages from birth to K-12 and later education, developing skills in socializing with friends and family, training for jobs and careers, looking for jobs and suitable places to live and work, engaging in long term relationships, having children and raising them, retiring, and whatever else happens postretirement. We develop life long and shorter projects that define our life in terms of years, months, weeks, and days. In all this, we allocate time and other resources to activities and interactions with other people that evolve over time and space. Resources we allocate and artifacts we use include the houses we live in, the cars we purchase and drive, the schools we attend, the offices we use, the restaurants we eat in, and so forth. In this sense, travel behavior is the combination of doing things in different places at different times and how we move from one place to another. Travel behavior is also about feelings, emotions, perceptions, norms, beliefs, intentions, and attitudes. These are motivations for allocating assets to activities but also gains from these activities such as increased quality of life. Moreover, travel behavior is how to go about deciding how to do things. Perhaps we form utilities for everything we do, or perhaps we use intuition, or perhaps we do both. In this sense, the data we collect and the models we use is one way to understand the underpinning motivations of our activities and travel and the path we follow in deciding about activities and travel. The travel behavior genome evolves in historical and personal time much faster than its molecular biology genome counterpart does. The travel behavior genome adapts to new circumstances as life itself and surrounding context and situations change. Key to understanding and mapping the travel behavior genome is to discover the fundamental mechanisms of evolution and adaptation of human behavior and how different people adapt in different ways to changing situations surrounding them. If travel behavior is defined as the allocation of resources to activity participation and travel among places where these activities happen, it is more appropriate to understand travel behavior as the all encompassing combinations of activity participation and movement from one place to another. In this sense we allocate the resources of time, money, and effort to accomplish tasks and advance on our paths toward satisfying individual and collective goals. In Bourdieu’s terms, travel behavior is one expression of the creation, use, and accumulation of economic, symbolic, cultural and social capital used to pursue our life-long goals.
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Before mapping the travel behavior genome in this book, we introduce two fundamental ideas defining the backdrop of what we do as travel behavior researchers. The first is the “wickedness” of the planning issues and the related analytical tools we use. The second is a fundamental transformation of our society to a society that combines collaborative commons with capitalism commonly known as the third industrial revolution and explained later in this introduction. Both influence the way this book maps the travel behavior genome in multiple fundamental dimensions that help us understand behavior.
2. Wickedness of planning problems One important aim of travel behavior analysis and modeling is transportation planning to solve problems such as congestion, accidents, waste of resources, pollution, and inequity. Most of the transportation planning problems are “wicked” problems (Ritter and Weber, 1973). Paraphrasing the original Ritter/ Weber article, “wicked” problems have the following features: (a) they have unclear formulation of what the problem we need to solve is (vagueness); (b) their solutions emerge when they are good enough, but never optimal (unknown optimum); (c) progress occurs through a continuity of solutions that improve over time (incremental progression); (d) not all intended and unintended consequences can be traced from the beginning (lack of complete observability); (e) every solution to a problem leaves an unchangeable trace of the outcome(s) (path dependence and irreversibility); (f) we cannot enumerate all possible solutions, consequences, and outcomes (indeterminacy); (g) problems are unique in historical time and place with no repeatable paths to a solution (place-time uniqueness); (h) a problem is a symptom of another problem from different domains of the life of people (nested hierarchy of problems); (i) there are multiple paths to achieve solution(s) to multiple contemporaneous problems, but there is no clear path on how to combine different solutions for different problems (unknown solution bundling); and (j) real-life planning work does not allow testing and experimentation using the scientific method (need for different methods). In addition, Ritter and Weber advance the proposition that there is no general social theory that can balance the interests of different stakeholders, and often the ideology of the analysts will influence their solutions. A good example of a “wicked” transportation planning problem is telecommuting as a tool to combat congestion, and the paradoxical findings in the extant literature is the proof (Taskin and Devos, 2005; Boell et al., 2016; Mokhtarian, 2009). Telework market penetration is small considering the policy and economic forces involved. On the one hand, we see a push for more teleworking in public agencies (see US OPM, 2017a and the Telework Enhancement Act of 2010, Public Law 111e292), evidence of substantial benefits for agencies and employees with estimated savings reaching $11,000 per person year for a business, $2,000 to $7,000 annual savings per employee,
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claims of substantial reductions in greenhouse gas emissions and energy consumption (Sekar et al., 2018), and possibly $700 Billion a year in national savings if eligible workers would work half the time from home (US OMP, 2017a,b; Global Workplace Analytics, 2019). On the other hand, only 7% of the US-based firms make telework available, the Bureau of Labor Statistics reports a decline in working from home (US BLS, 2018), and news media reports major corporations are cutting down on telecommuting programs. Often, these reported paradoxes are not paradoxes at all. They can be explained by carefully studying the context, data definitions, and related analysis. Context within which people decide to engage in telecommuting can be discerned by differences and commonalities among settings where telework is adopted (Vilhelmson and Thulin, 2016), demographics of program participants (Gimenez-Nadal et al., 2018), technologies used (Pliskin, 1997; Messenger and Gschwind, 2016; Weinbaum et al., 2018), supervisory roles (Lautsch et al., 2009), and perspectives of adopter versus nonadopter and manager versus employee (Illegems and Verbeke, 2004). Similarly, definitions of telework can be extracted from national databases with documented data collection settings, data collection processes, and question wording. All these important details can be documented in planning projects, analyzing behavior accordingly, and contextualizing solutions to problems. Non-traditional work arrangements come in different types (i.e., ontologically different). The words used in the literature include distributed work, mobile work, remote work, smart working, workshifting, working nomads, and telecommuting. This is the outcome of not only unclear theoretical foundations, but also the changing nature of work, telework, worker, and teleworker (Sullivan, 2003). The difficulty in defining telework is also emerging from work practices and telework arrangements that are not always separable and the research literature is not clear about this (Boell et al., 2016; Cole et al., 2014). This is complicated by differences among activities performed at workplaces that determine suitability of work for telework substitution. Over time, the increasing use of information communication technology (ICT), the role it plays in work practices, waves of technological change that include cloud technology and wide bandwidth networks changes the nature of work and undermines assessment about the positive of negative outcomes of telework (Garrett and Danzinger, 2007; Holland and Bardoel, 2016; Howcroft and Taylor, 2014; Taskin and Devos, 2005; Frese, 2008; Boell et al., 2013; Harker and MacDonnell, 2012). Transformations of work imply that a home is no longer just a family’s nest and a car is not only for travel. Moreover, the concept of a job or career are evolving, innovation and entrepreneurship are accelerating, work is increasingly complex, requirements for self-reliance and personal initiative are increasing. To this we see local and global competition increasing, work is done with an emphasis on teamwork, managerial challenges are created from reduced supervision, and risks for cultural tensions
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increase with cultural diversity richness. All these ideas challenge the extant definition of work, worker, and telework (McGrath and Houlihan, 2002), but they also challenge foundational definitions of what is a home, a workplace, a school, and travel. Hence the “wicked” planning problem. Additional examples of similar wicked problems are found in the last chapter of this book.
3. Rapidly changing backdrop The second fundamental idea characterizing the backdrop of our understanding of behavior is a rapidly advancing trend today that gave rise to the disruptive technologies this conference addressed: the sharing economy. The sharing economy is disrupting the technology realm and social fabric in many ways (Laurell and Sandstrom, 2017). “Sharing economy” is an umbrella term for the technology enabling renting, selling, sharing, lending, gifting, and swapping. It is creating disruption through innovation and the use of previously unexploited assets. The sharing economy is gaining traction because it creates abundance, replacing scarcity with decreasing marginal costs and increasing returns (Rifkin, 2014; Acquier et al., 2017; Geissinger et al., 2018). The two forerunner industries experiencing this disruption are transportation and accommodation, with many other industries following the trend. This disruption is creating major shifts in labor markets, transforming workplace conceptions, and challenging traditional ideas about the foundation of travel behavior theories, data needed, and modeling. In economic terms, rental marketplaces create new gains from trade between consumers, enable consumers access to additional surplus when they cannot afford ownership, increase attractiveness of higher quality products because they become more affordable, increase manufacturer surplus, possibly cause rapid depreciation of assets for owners, and possibly shift production volumes to lower profit margins (Fraiberger and Sundararajan, 2015). Workers in this new “gig” economy are becoming venture laborers that have the benefits of flexibility and profits, but they also assume all the risk of doing business (Neff, 2012; Ravenelle, 2017). Work itself changes to become entrepreneurial and service product- or good-oriented, and work “[is] temporary, contract-based, low paid, and provides no training, health, or retirement benefits” (Kenney and Zysman, 2016). All this means that increasing returns are concentrated in the hands of platform owners, the conditions and benefits of workers are worse compared to traditional work, inequalities are not checked, and social connections can be absent. In contrast, when workspace designs are conceived in a way that maximizes flexibility and social connections, major gains in energy efficiency can occur (Randall, 2015). In fact, Babb et al. (2018) describe the concept of “coworking-spaces” that improve entrepreneurial performance through learning and community building. Developments in transportation are indicative of not only the gains and losses of sharing but also the melting of traditional roles of service users and
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providers. Further, automation such as self-driving cars changes working space and co-working spaces in ways that make a vehicle a connected mobile workplace. Autonomous connected vehicles and automated transportation systems form an ensemble of technologies that is already replacing operatordriven construction, mining, and agriculture vehicles with robotic vehicles. This increases productivity and efficiency because the vehicles can be used 24/ 7. These changes transform the entire industry, the workplace and the worker. They eliminate some types of jobs while creating demand for other jobs that require a more advanced level of knowledge and skill with ICT. Transportation is experiencing this radical modification with Mobility as a Service. Early examples are Uber and Lyft that provide transportation on-demand, compete with traditional public transportation, and create a new entrepreneurial labor force. The ultimate example of melting the boundaries between workplace and other places is when automated commuting vehicles become mobile workspaces (Keseru and Macharis, 2018). This is already happening with long-distance commuting on buses, trains, and airplanes that offer ICT for business travelers. Autonomous connected vehicles (self-driving cars - robocars) push this radical transformation even further by eliminating boundaries between places, with the added freedom in time allocation and use of mobile spaces. Transformations of the work and worker that a robocar will enable or force are not yet known or imagined. The car becomes the enabling technology, an advanced technology-equipped office, and presumably a traveling cabin that resembles the business class cocoons found in airliners today. Private industry envisions these vehicles as shared assets in a way that decreases scarcity and increases access to opportunities in unprecedented ways (Gao et al., 2016). The examples above show that we already live in an environment in which traditional ontologies about activity and travel as well as places (home, work, school) are shifting and are enriched by other entities, the definitions of which have yet unknown meanings. For example, our inquiry for ontologies that looks into the future of work needs to cast a wide net around fundamental transformations of time and space. One example is polychronicity (Kaufman et al., 1991), which is the combination of activities within the same time block. This challenges the more traditional time classification into distinguishable activities such as work, leisure, chores, and errands. In this context, time cannot be perceived as a linear progression and may be allocated in a way that multiple tasks are accomplished within the same block of time. Technology influences propensity for polychronic time use with negative and positive impacts, and not accounting for this in analysis leads to measurement issues (Kaufman-Scarborough, 2006; Kenyon and Lyons, 2007). Space separation is also challenged by technology and one such challenge is telepresence. Telepresence is intended here as the use of devices that allow two-way synchronous communication and physical interaction of multiple remote environments. This changes how we think about locations, spaces and places,
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and human interaction. Although telepresence is conceptually easy to understand and was described long ago as an enabling technology helping a person to be active socially while performing work remotely (Minsky, 1980; Stauer, 1992), it is still considered to be a challenge (Marlow et al., 2017) with a clear potential for radically transforming work, the worker and telework (Baldwin, 2019). Similar considerations can be made for all other kinds of activities such as education, shopping, and socializing, leaving us with consistent questions about the ontology of the activity itself.
4. Mapping the travel behavior genome Our travel behavior genome is changing in the midst of uncertainties about fundamental definitions of the transportation planning problems and the shifting ground of what reality is today and will be like in the future. To map the travel behavior genome, we take a pragmatic approach using four major dimensions or lenses of the research presented in this book: substantive problems addressed in the chapters, theoretical and conceptual frameworks adopted, behavioral measurement, and behavioral analysis to analyze each problem.
4.1 Substantive problems The repertory of travel behavior analysis represented in this book follows the 50 þ years tradition of understanding, modeling, and predicting mode choice and switching modes of people. The obvious desire is to help people move away from using a car as a single driver and walk, bike, and share private and public transportation with other people. The repertory, however, is expanded in terms of policies studied to include parking pricing, provision of direct monetary incentives, and infrastructure catering to particular types of vehiclesdsuch as optimal location of electric car charging stations. The chapters in this book also show the increasing interest in understanding how people will approach, use, and include autonomous vehicles in their everyday life. As expected, researchers are also interested in understanding how the new and expanded on-demand mobility will change choice behavior. Chapters in this book examine these new substantive problems in mode choice. They include willingness to pay and the change in the value of travel time savings when people switch to any form of autonomy (private vs. shared, complete vs. Partial) and any distance traveled (long distance vs. short distance). They also include equity and distributional justice and differences across generations in accepting these relatively new modes as they become available. Some of the newer problems studied are changes in behavior before and after a mode option is added, real life experiments, and in-laboratory virtual reality experiments. These newer studies incorporate attitudes, subjective well-being, social capital, and social influence; additionally, the studies
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explore choices as group decisions and the power in decision making. We also have chapters exploring spatio-temporal patterns of behavioral facets in cities from a bird-eye view to better understand what people do in time and space. In research, activity participation and time use are integral in travel behavior analysis. The activity-based approach is now a standard substantive problem to examine. We find chapters that explore human interaction and its relationship with time use decisions, scheduling of activities and travel in a conflict resolution framework, propensity to allocate time in different activities at different life cycle stages, and time allocation to different activities by workers and non-workers. We also find chapters exploring the fragmentation of place-activity-travel sequences and correlating these with the built environment, incorporating social capital and influence in choice models of time allocation, understanding task/time allocation and power relationships within households, understanding time use in privately owned autonomous vehicles, and explorations on pedestrian movements sequencing and associated daily patterns. Similarly, analyzing attitudes jointly with behavior has become another standard in understanding choices. This is very different from past conferences. This book includes chapters on on-demand travel options and relationship with attitudes, intention to use autonomous vehicles and attitudes about autonomous vehicles, model transferability across different locations aided by attitudes, testing the change of attitudes when the infrastructure is radically modified, and the influence of past experiences and future life plans on current behavior. Research chapters and the workshop reports also show the maturity of research on understanding quality of life as motivation for and outcome of travel behavior. Notably absent from the book are substantive research problems about goods movement (freight). This is explained further and a research program outlined in the concluding chapter with a report from the research workshops at IATBR2018.
4.2 Theoretical & conceptual frameworks The lion’s share of the theoretical framework underlying the chapters in this book belongs to the random utility microeconomic theory, with the important expansion of including latent constructs. These take many different and complementary forms, but a substantial portion of them use these latent constructs to incorporate beliefs, attitudes, perceptions, heterogeneity in preferences, social influence, and power of decision making in the utility framework. This framework has been applied to time allocation and mode choices. A key contribution in this expanded microeconomic framework is a successful attempt to include differences among decision makers in terms of who they are, capture context of their choices, and use more flexible structures modeling individual and group preferences.
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Consistent with a long-held tradition in travel behavior research, many chapters develop ad-hoc conceptual frameworksdoften based on past research and the combination of ideas from different sources in the literaturedthat are either at the individual or household level and can be tested with data. In this book, this includes long distance travel, daily schedules for agent-based simulation, time allocation to activities, and data-driven discovery of spatio-temporal patterns. Each of these conceptual frameworks includes causeeffect and correlates with a variety of social, demographic, and spatial characteristics that are in turn tested with and confirmed by data. We also see an emergence of conceptual frameworks that are purely datadriven and presented as spatio-temporal patterns. These are often depicted on maps and based on correlations among multiple variables. These data come from passive data collection, field observations, experiments in a laboratory, or just model-based simulations. This is a different way of developing theory. There are many advantages to this approach to theory building. We can study routine and extreme behavioral responses, develop and display patterns at different levels of spatial and temporal aggregation, and develop conceptual frameworks that operate at the level of movements of one human being, a neighborhood, city, region, or an entire state and using multiple time blocks such as an hour, a day, a week, or even a year. In this way, we study behavior in multilevel spatio-temporal settings and contexts, test hypotheses at different levels of aggregation, and verify behaviorally richer microlevel models and relationships between a macro level and micro levels.
4.3 Behavioral measurement The measurement of behavior includes qualitative and quantitative methods, and as the workshops at the end of this book show, both sources of data are required to map the travel behavior genome. Human interaction is a key aspect of understanding travel behavior, and the majority of travel behavior studies are about individuals and their groups with emphasis on the household (i.e., group of people living together), which is the fundamental unit in which resources are allocated and consumed. Many of the chapters in this book use data from household surveys. Often in these surveys, every person in the household is interviewed and for every person a daily diary is collected. These surveys have included travel diaries, place-based diaries, and time use diaries on one or multiple days. This type of data enables the study of social interactions, spatio-temporal patterns of behavior, and exploration of different resource allocation patterns that lead to different behaviors. Many chapters, however, focus on a particular aspect of a substantive problem. For this reason, personal interviews are the preferred sources of data. These interviews include questions about skeleton/typical travel behavior, experiments in the form of stated choices/stated preferences, and intentions and beliefs/attitudes about an artifact (e.g., a self-driving car). They are also
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combinations of past behavior (i.e., with retrospective questions) and answers to hypothetical questions (e.g., choice among multiple options or plans about the future). Some of the examples here also include longitudinal data in the form of before something important happensdsuch as new infrastructured and after it has happened. Online, in-person, and in-laboratory are the means to measure behavior. The method of measurement depends on the substantive problem. These data collection projects collect data about choices of people and their background social and demographic characteristics, beliefs/attitudes, and perceptions. A third group of data sourced is from passively (as opposed to interviews that are considered active interaction with a respondent) collected data. These include GPS traces from a variety of devices (wearable GPS, phones, vehicles), arrival and departure times at stationary locations (stations of bicycles), transaction data at points of arrivaledeparture (e.g., public transport stations, tickets), user-contributed data in online media (geotagged), and microdata describing the built environment. This is the type of data that enable the study of spatio-temporal behaviors in long periods and for large areas. The chapters in this book, however, also show some new ways to procure data for travel behavior representing new directions of travel behavior data provision. For example, in one chapter the authors discuss the design of a virtual reality driving simulator and the types of data one can extract about learning environments. In another chapter, the authors designed a stated preference experiment combined with videos and revealed preference data collection. Chapters in this book also discuss the combination of revealed and stated preference data, combination of household travel diaries with archival information at fine spatial resolution, scenario data in simulation experiments, and real-life experiments with detailed tracking of the participants. These new behavioral measurement methods are discussed further in the summary of the IATBR2018 workshops in the last chapter of this book.
4.4 Behavioral analysis Behavioral analysis in this book is predominantly quantitative expanding past analytical frameworks adding flexibility in our ability to address substantive problems. However, as the workshops show, qualitative methods are also a must for understanding travel behavior in depth. Random utility discrete choice models are the most popular in this book and the conference due to massive developments in integrating the models with many other behavioral facets in the form of latent variables. This type of modeling is used for revealed preference data, stated preference data, and combinations of the two. Logit and Probit are the preferred functional forms for these models. The discrete choice models in this book represent the stateof-the-art in behavioral analysis, and they are mixed logit models, hybrid choice models, extensions of discrete choice models with latent variables in
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longitudinal settings, and models to represent multiple discreteness incorporating the choice of continuous variables in the same theoretical and data analytic framework (e.g., Multiple Discrete-Continuous Extreme Value models). A chapter is also broaching the subject of models with different decision weights representing power relations within a household. These are applied in both cross-sectional and longitudinal settings. This represents evidence not only of support for expanded and new theoretical frameworks, but also of added flexibility in modeling behavior accounting for a variety of factors to empirically test relationships among a wide array of behavioral facets. In addition to choice models, chapters in this book also use regression techniques to test correlation among a variety of observed and latent variables. These include Multinomial Logit, Probit, Ordered Probit on single behavioral facets, and Structural Equations Models (including versions that can handle combinations of categorical data, count data, and continuous data). These are flexible tools that one can use to combine data from different sources, explore new multivariate relationships, and test hypotheses about these relationships. Cluster analysis is also emerging as another technique to detect and describe data patterns and express these patterns in groups. In our travel behavior toolbox represented by the chapters in this book, we have simple kmeans cluster analysis, cluster analysis based on Probit models, hierarchical clustering combined with regression, point-pattern analysis using Densitybased spatial clustering, and pattern recognition with Mixed Markov Latent Class models. It is also worth mentioning that we see an emergence of spatiotemporal pattern recognition techniques that use survey data, archival data, and/or data from microsimulation experiments. Some of the techniques in this group have been labeled machine learning, and we return to this in the workshops chapter of this book.
5. Coda All four mapping dimensions of the travel behavior genome show that the strategy of addressing wicked problems by travel behavior research is twopronged. On the one hand, we expand the domain of behavioral analysis to include behavioral facets that are at a higher level than travel, including activities, human interaction, and location choices. On the other hand, we expand modeling frameworks to include psycho-social behavioral measures. These mapping dimensions also show an energetic push to map the changing environment and define new ontologies and sources of measurement of behavior as a response to the changing reality. We are still left with theoretical, methodological, and behavioral measurement questions, as the workshop summaries at the end of this book show. In closing, the travel behavior genome is ever-evolving, and its path of evolution is complex in a similar way to the wicked transportation planning
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problems. It is still unknown what type of substantive problems we will need to address due to the uncertainty of the potential upheaval from automation, global trends, changes in society, and internal changes to the individuals we observe. As the last chapter of this book shows, however, travel behavior analysts are ready to tackle these challenges. In fact, this last chapter shows the analytical challenge is to track behavioral context, revise our theoretical frameworks and models when needed to incrementally revise fundamental ontological definitions, develop new techniques, and create new opportunities for data collection about behavior.
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14 Mapping the Travel Behavior Genome Sullivan, C., 2003. What’s in a name? Definitions and conceptualisations of teleworking and homeworking. New Technology, Work and Employment 18 (3), 158e165. Taskin, L., Devos, V., 2005. Paradoxes from the individualization of human resource management: the case of telework. Journal of Business Ethics 62 (1), 13e24. United States Bureau of Labor Statistics, 2018. American Time Use Survey Summary. https:// www.bls.gov/news.release/atus.nr0.htm. United States Office of Personnel Management, 2017a. Status of Telework in the Federal Government: Report to Congress Fiscal Year 2016. OPM.GOV and Telework.gov. United States Office of Personnel Management, 2017b. telework.gov: Telework Enhancement Act of 2010, Public Law 111-292. Vilhelmson, B., Thulin, E., 2016. Who and where are the flexible workers? Exploring the current diffusion of telework in Sweden. New Technology, Work and Employment 31 (1), 77e96. Weinbaum, C., Triezenberg, B.L., Meza, E., Luckey, D., 2018. Understanding Government Telework. RAND Corporation.
Introduction and the genome of travel behavior Konstadinos G. Gouliasa, Adam W. Davisb, Eizabeth C. McBrideb a
Department of Geography and GeoTrans Lab, University of California, Santa Barbara, CA, United states; bUniversity of California, Santa Barbara, CA, United states
Chapter outline 1. What is the travel behavior genome? 2. Wickedness of planning problems 3. Rapidly changing backdrop 4. Mapping the travel behavior genome 4.1 Substantive problems
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4.2 Theoretical & conceptual frameworks 4.3 Behavioral measurement 4.4 Behavioral analysis 5. Coda References
8 9 10 11 12
Every three years, the International Association for Travel Behavior Research organizes a conference that spans the entire spectrum of the most important developments in travel behavior research (see IATBR.ORG). This is a unique opportunity for researchers from around the world to meet and exchange ideas about new issues and methods used to understand and predict behavior. In 2018, the conference took place on the University of California, Santa Barbara campus from July 15th to 20th (www.iatbr2018.org and IATBR2018). The conference theme, Mapping the Travel Behavior Genome, highlights the great progress in developing tools to understand and map the fundamental elements of travel behavior. The conference included methods to unravel human activity-travel behavior, showcasing state-of-the-art advancements in research and practical applications. This includes the biological, motivational, cognitive, situational, and dispositional factors that drive activity-travel behavior. The conference also addressed critical issues in developing urgently needed theories and analytical methods to address imminent upheavals that transform our lives. This book contains three major sections. The first section offers a retrospective and prospective survey of travel behavior research in four chapters from keynotes at the conference. The second section is the core of the book Mapping the Travel Behavior Genome. https://doi.org/10.1016/B978-0-12-817340-4.00001-2 Copyright © 2020 Elsevier Inc. All rights reserved.
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with twenty six chapters on new research methods and findings. The last section is a report from eight brainstorming workshops that describe the research frontier in travel behavior, research challenges, and offers many paths for future research. In this introduction we review the theme of our conference and provide a summary.
1. What is the travel behavior genome? First, travel behavior is about life. In our life, we progress through life course stages from birth to K-12 and later education, developing skills in socializing with friends and family, training for jobs and careers, looking for jobs and suitable places to live and work, engaging in long term relationships, having children and raising them, retiring, and whatever else happens postretirement. We develop life long and shorter projects that define our life in terms of years, months, weeks, and days. In all this, we allocate time and other resources to activities and interactions with other people that evolve over time and space. Resources we allocate and artifacts we use include the houses we live in, the cars we purchase and drive, the schools we attend, the offices we use, the restaurants we eat in, and so forth. In this sense, travel behavior is the combination of doing things in different places at different times and how we move from one place to another. Travel behavior is also about feelings, emotions, perceptions, norms, beliefs, intentions, and attitudes. These are motivations for allocating assets to activities but also gains from these activities such as increased quality of life. Moreover, travel behavior is how to go about deciding how to do things. Perhaps we form utilities for everything we do, or perhaps we use intuition, or perhaps we do both. In this sense, the data we collect and the models we use is one way to understand the underpinning motivations of our activities and travel and the path we follow in deciding about activities and travel. The travel behavior genome evolves in historical and personal time much faster than its molecular biology genome counterpart does. The travel behavior genome adapts to new circumstances as life itself and surrounding context and situations change. Key to understanding and mapping the travel behavior genome is to discover the fundamental mechanisms of evolution and adaptation of human behavior and how different people adapt in different ways to changing situations surrounding them. If travel behavior is defined as the allocation of resources to activity participation and travel among places where these activities happen, it is more appropriate to understand travel behavior as the all encompassing combinations of activity participation and movement from one place to another. In this sense we allocate the resources of time, money, and effort to accomplish tasks and advance on our paths toward satisfying individual and collective goals. In Bourdieu’s terms, travel behavior is one expression of the creation, use, and accumulation of economic, symbolic, cultural and social capital used to pursue our life-long goals.
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Before mapping the travel behavior genome in this book, we introduce two fundamental ideas defining the backdrop of what we do as travel behavior researchers. The first is the “wickedness” of the planning issues and the related analytical tools we use. The second is a fundamental transformation of our society to a society that combines collaborative commons with capitalism commonly known as the third industrial revolution and explained later in this introduction. Both influence the way this book maps the travel behavior genome in multiple fundamental dimensions that help us understand behavior.
2. Wickedness of planning problems One important aim of travel behavior analysis and modeling is transportation planning to solve problems such as congestion, accidents, waste of resources, pollution, and inequity. Most of the transportation planning problems are “wicked” problems (Ritter and Weber, 1973). Paraphrasing the original Ritter/ Weber article, “wicked” problems have the following features: (a) they have unclear formulation of what the problem we need to solve is (vagueness); (b) their solutions emerge when they are good enough, but never optimal (unknown optimum); (c) progress occurs through a continuity of solutions that improve over time (incremental progression); (d) not all intended and unintended consequences can be traced from the beginning (lack of complete observability); (e) every solution to a problem leaves an unchangeable trace of the outcome(s) (path dependence and irreversibility); (f) we cannot enumerate all possible solutions, consequences, and outcomes (indeterminacy); (g) problems are unique in historical time and place with no repeatable paths to a solution (place-time uniqueness); (h) a problem is a symptom of another problem from different domains of the life of people (nested hierarchy of problems); (i) there are multiple paths to achieve solution(s) to multiple contemporaneous problems, but there is no clear path on how to combine different solutions for different problems (unknown solution bundling); and (j) real-life planning work does not allow testing and experimentation using the scientific method (need for different methods). In addition, Ritter and Weber advance the proposition that there is no general social theory that can balance the interests of different stakeholders, and often the ideology of the analysts will influence their solutions. A good example of a “wicked” transportation planning problem is telecommuting as a tool to combat congestion, and the paradoxical findings in the extant literature is the proof (Taskin and Devos, 2005; Boell et al., 2016; Mokhtarian, 2009). Telework market penetration is small considering the policy and economic forces involved. On the one hand, we see a push for more teleworking in public agencies (see US OPM, 2017a and the Telework Enhancement Act of 2010, Public Law 111e292), evidence of substantial benefits for agencies and employees with estimated savings reaching $11,000 per person year for a business, $2,000 to $7,000 annual savings per employee,
4 Mapping the Travel Behavior Genome
claims of substantial reductions in greenhouse gas emissions and energy consumption (Sekar et al., 2018), and possibly $700 Billion a year in national savings if eligible workers would work half the time from home (US OMP, 2017a,b; Global Workplace Analytics, 2019). On the other hand, only 7% of the US-based firms make telework available, the Bureau of Labor Statistics reports a decline in working from home (US BLS, 2018), and news media reports major corporations are cutting down on telecommuting programs. Often, these reported paradoxes are not paradoxes at all. They can be explained by carefully studying the context, data definitions, and related analysis. Context within which people decide to engage in telecommuting can be discerned by differences and commonalities among settings where telework is adopted (Vilhelmson and Thulin, 2016), demographics of program participants (Gimenez-Nadal et al., 2018), technologies used (Pliskin, 1997; Messenger and Gschwind, 2016; Weinbaum et al., 2018), supervisory roles (Lautsch et al., 2009), and perspectives of adopter versus nonadopter and manager versus employee (Illegems and Verbeke, 2004). Similarly, definitions of telework can be extracted from national databases with documented data collection settings, data collection processes, and question wording. All these important details can be documented in planning projects, analyzing behavior accordingly, and contextualizing solutions to problems. Non-traditional work arrangements come in different types (i.e., ontologically different). The words used in the literature include distributed work, mobile work, remote work, smart working, workshifting, working nomads, and telecommuting. This is the outcome of not only unclear theoretical foundations, but also the changing nature of work, telework, worker, and teleworker (Sullivan, 2003). The difficulty in defining telework is also emerging from work practices and telework arrangements that are not always separable and the research literature is not clear about this (Boell et al., 2016; Cole et al., 2014). This is complicated by differences among activities performed at workplaces that determine suitability of work for telework substitution. Over time, the increasing use of information communication technology (ICT), the role it plays in work practices, waves of technological change that include cloud technology and wide bandwidth networks changes the nature of work and undermines assessment about the positive of negative outcomes of telework (Garrett and Danzinger, 2007; Holland and Bardoel, 2016; Howcroft and Taylor, 2014; Taskin and Devos, 2005; Frese, 2008; Boell et al., 2013; Harker and MacDonnell, 2012). Transformations of work imply that a home is no longer just a family’s nest and a car is not only for travel. Moreover, the concept of a job or career are evolving, innovation and entrepreneurship are accelerating, work is increasingly complex, requirements for self-reliance and personal initiative are increasing. To this we see local and global competition increasing, work is done with an emphasis on teamwork, managerial challenges are created from reduced supervision, and risks for cultural tensions
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increase with cultural diversity richness. All these ideas challenge the extant definition of work, worker, and telework (McGrath and Houlihan, 2002), but they also challenge foundational definitions of what is a home, a workplace, a school, and travel. Hence the “wicked” planning problem. Additional examples of similar wicked problems are found in the last chapter of this book.
3. Rapidly changing backdrop The second fundamental idea characterizing the backdrop of our understanding of behavior is a rapidly advancing trend today that gave rise to the disruptive technologies this conference addressed: the sharing economy. The sharing economy is disrupting the technology realm and social fabric in many ways (Laurell and Sandstrom, 2017). “Sharing economy” is an umbrella term for the technology enabling renting, selling, sharing, lending, gifting, and swapping. It is creating disruption through innovation and the use of previously unexploited assets. The sharing economy is gaining traction because it creates abundance, replacing scarcity with decreasing marginal costs and increasing returns (Rifkin, 2014; Acquier et al., 2017; Geissinger et al., 2018). The two forerunner industries experiencing this disruption are transportation and accommodation, with many other industries following the trend. This disruption is creating major shifts in labor markets, transforming workplace conceptions, and challenging traditional ideas about the foundation of travel behavior theories, data needed, and modeling. In economic terms, rental marketplaces create new gains from trade between consumers, enable consumers access to additional surplus when they cannot afford ownership, increase attractiveness of higher quality products because they become more affordable, increase manufacturer surplus, possibly cause rapid depreciation of assets for owners, and possibly shift production volumes to lower profit margins (Fraiberger and Sundararajan, 2015). Workers in this new “gig” economy are becoming venture laborers that have the benefits of flexibility and profits, but they also assume all the risk of doing business (Neff, 2012; Ravenelle, 2017). Work itself changes to become entrepreneurial and service product- or good-oriented, and work “[is] temporary, contract-based, low paid, and provides no training, health, or retirement benefits” (Kenney and Zysman, 2016). All this means that increasing returns are concentrated in the hands of platform owners, the conditions and benefits of workers are worse compared to traditional work, inequalities are not checked, and social connections can be absent. In contrast, when workspace designs are conceived in a way that maximizes flexibility and social connections, major gains in energy efficiency can occur (Randall, 2015). In fact, Babb et al. (2018) describe the concept of “coworking-spaces” that improve entrepreneurial performance through learning and community building. Developments in transportation are indicative of not only the gains and losses of sharing but also the melting of traditional roles of service users and
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providers. Further, automation such as self-driving cars changes working space and co-working spaces in ways that make a vehicle a connected mobile workplace. Autonomous connected vehicles and automated transportation systems form an ensemble of technologies that is already replacing operatordriven construction, mining, and agriculture vehicles with robotic vehicles. This increases productivity and efficiency because the vehicles can be used 24/ 7. These changes transform the entire industry, the workplace and the worker. They eliminate some types of jobs while creating demand for other jobs that require a more advanced level of knowledge and skill with ICT. Transportation is experiencing this radical modification with Mobility as a Service. Early examples are Uber and Lyft that provide transportation on-demand, compete with traditional public transportation, and create a new entrepreneurial labor force. The ultimate example of melting the boundaries between workplace and other places is when automated commuting vehicles become mobile workspaces (Keseru and Macharis, 2018). This is already happening with long-distance commuting on buses, trains, and airplanes that offer ICT for business travelers. Autonomous connected vehicles (self-driving cars - robocars) push this radical transformation even further by eliminating boundaries between places, with the added freedom in time allocation and use of mobile spaces. Transformations of the work and worker that a robocar will enable or force are not yet known or imagined. The car becomes the enabling technology, an advanced technology-equipped office, and presumably a traveling cabin that resembles the business class cocoons found in airliners today. Private industry envisions these vehicles as shared assets in a way that decreases scarcity and increases access to opportunities in unprecedented ways (Gao et al., 2016). The examples above show that we already live in an environment in which traditional ontologies about activity and travel as well as places (home, work, school) are shifting and are enriched by other entities, the definitions of which have yet unknown meanings. For example, our inquiry for ontologies that looks into the future of work needs to cast a wide net around fundamental transformations of time and space. One example is polychronicity (Kaufman et al., 1991), which is the combination of activities within the same time block. This challenges the more traditional time classification into distinguishable activities such as work, leisure, chores, and errands. In this context, time cannot be perceived as a linear progression and may be allocated in a way that multiple tasks are accomplished within the same block of time. Technology influences propensity for polychronic time use with negative and positive impacts, and not accounting for this in analysis leads to measurement issues (Kaufman-Scarborough, 2006; Kenyon and Lyons, 2007). Space separation is also challenged by technology and one such challenge is telepresence. Telepresence is intended here as the use of devices that allow two-way synchronous communication and physical interaction of multiple remote environments. This changes how we think about locations, spaces and places,
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and human interaction. Although telepresence is conceptually easy to understand and was described long ago as an enabling technology helping a person to be active socially while performing work remotely (Minsky, 1980; Stauer, 1992), it is still considered to be a challenge (Marlow et al., 2017) with a clear potential for radically transforming work, the worker and telework (Baldwin, 2019). Similar considerations can be made for all other kinds of activities such as education, shopping, and socializing, leaving us with consistent questions about the ontology of the activity itself.
4. Mapping the travel behavior genome Our travel behavior genome is changing in the midst of uncertainties about fundamental definitions of the transportation planning problems and the shifting ground of what reality is today and will be like in the future. To map the travel behavior genome, we take a pragmatic approach using four major dimensions or lenses of the research presented in this book: substantive problems addressed in the chapters, theoretical and conceptual frameworks adopted, behavioral measurement, and behavioral analysis to analyze each problem.
4.1 Substantive problems The repertory of travel behavior analysis represented in this book follows the 50 þ years tradition of understanding, modeling, and predicting mode choice and switching modes of people. The obvious desire is to help people move away from using a car as a single driver and walk, bike, and share private and public transportation with other people. The repertory, however, is expanded in terms of policies studied to include parking pricing, provision of direct monetary incentives, and infrastructure catering to particular types of vehiclesdsuch as optimal location of electric car charging stations. The chapters in this book also show the increasing interest in understanding how people will approach, use, and include autonomous vehicles in their everyday life. As expected, researchers are also interested in understanding how the new and expanded on-demand mobility will change choice behavior. Chapters in this book examine these new substantive problems in mode choice. They include willingness to pay and the change in the value of travel time savings when people switch to any form of autonomy (private vs. shared, complete vs. Partial) and any distance traveled (long distance vs. short distance). They also include equity and distributional justice and differences across generations in accepting these relatively new modes as they become available. Some of the newer problems studied are changes in behavior before and after a mode option is added, real life experiments, and in-laboratory virtual reality experiments. These newer studies incorporate attitudes, subjective well-being, social capital, and social influence; additionally, the studies
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explore choices as group decisions and the power in decision making. We also have chapters exploring spatio-temporal patterns of behavioral facets in cities from a bird-eye view to better understand what people do in time and space. In research, activity participation and time use are integral in travel behavior analysis. The activity-based approach is now a standard substantive problem to examine. We find chapters that explore human interaction and its relationship with time use decisions, scheduling of activities and travel in a conflict resolution framework, propensity to allocate time in different activities at different life cycle stages, and time allocation to different activities by workers and non-workers. We also find chapters exploring the fragmentation of place-activity-travel sequences and correlating these with the built environment, incorporating social capital and influence in choice models of time allocation, understanding task/time allocation and power relationships within households, understanding time use in privately owned autonomous vehicles, and explorations on pedestrian movements sequencing and associated daily patterns. Similarly, analyzing attitudes jointly with behavior has become another standard in understanding choices. This is very different from past conferences. This book includes chapters on on-demand travel options and relationship with attitudes, intention to use autonomous vehicles and attitudes about autonomous vehicles, model transferability across different locations aided by attitudes, testing the change of attitudes when the infrastructure is radically modified, and the influence of past experiences and future life plans on current behavior. Research chapters and the workshop reports also show the maturity of research on understanding quality of life as motivation for and outcome of travel behavior. Notably absent from the book are substantive research problems about goods movement (freight). This is explained further and a research program outlined in the concluding chapter with a report from the research workshops at IATBR2018.
4.2 Theoretical & conceptual frameworks The lion’s share of the theoretical framework underlying the chapters in this book belongs to the random utility microeconomic theory, with the important expansion of including latent constructs. These take many different and complementary forms, but a substantial portion of them use these latent constructs to incorporate beliefs, attitudes, perceptions, heterogeneity in preferences, social influence, and power of decision making in the utility framework. This framework has been applied to time allocation and mode choices. A key contribution in this expanded microeconomic framework is a successful attempt to include differences among decision makers in terms of who they are, capture context of their choices, and use more flexible structures modeling individual and group preferences.
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Consistent with a long-held tradition in travel behavior research, many chapters develop ad-hoc conceptual frameworksdoften based on past research and the combination of ideas from different sources in the literaturedthat are either at the individual or household level and can be tested with data. In this book, this includes long distance travel, daily schedules for agent-based simulation, time allocation to activities, and data-driven discovery of spatio-temporal patterns. Each of these conceptual frameworks includes causeeffect and correlates with a variety of social, demographic, and spatial characteristics that are in turn tested with and confirmed by data. We also see an emergence of conceptual frameworks that are purely datadriven and presented as spatio-temporal patterns. These are often depicted on maps and based on correlations among multiple variables. These data come from passive data collection, field observations, experiments in a laboratory, or just model-based simulations. This is a different way of developing theory. There are many advantages to this approach to theory building. We can study routine and extreme behavioral responses, develop and display patterns at different levels of spatial and temporal aggregation, and develop conceptual frameworks that operate at the level of movements of one human being, a neighborhood, city, region, or an entire state and using multiple time blocks such as an hour, a day, a week, or even a year. In this way, we study behavior in multilevel spatio-temporal settings and contexts, test hypotheses at different levels of aggregation, and verify behaviorally richer microlevel models and relationships between a macro level and micro levels.
4.3 Behavioral measurement The measurement of behavior includes qualitative and quantitative methods, and as the workshops at the end of this book show, both sources of data are required to map the travel behavior genome. Human interaction is a key aspect of understanding travel behavior, and the majority of travel behavior studies are about individuals and their groups with emphasis on the household (i.e., group of people living together), which is the fundamental unit in which resources are allocated and consumed. Many of the chapters in this book use data from household surveys. Often in these surveys, every person in the household is interviewed and for every person a daily diary is collected. These surveys have included travel diaries, place-based diaries, and time use diaries on one or multiple days. This type of data enables the study of social interactions, spatio-temporal patterns of behavior, and exploration of different resource allocation patterns that lead to different behaviors. Many chapters, however, focus on a particular aspect of a substantive problem. For this reason, personal interviews are the preferred sources of data. These interviews include questions about skeleton/typical travel behavior, experiments in the form of stated choices/stated preferences, and intentions and beliefs/attitudes about an artifact (e.g., a self-driving car). They are also
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combinations of past behavior (i.e., with retrospective questions) and answers to hypothetical questions (e.g., choice among multiple options or plans about the future). Some of the examples here also include longitudinal data in the form of before something important happensdsuch as new infrastructured and after it has happened. Online, in-person, and in-laboratory are the means to measure behavior. The method of measurement depends on the substantive problem. These data collection projects collect data about choices of people and their background social and demographic characteristics, beliefs/attitudes, and perceptions. A third group of data sourced is from passively (as opposed to interviews that are considered active interaction with a respondent) collected data. These include GPS traces from a variety of devices (wearable GPS, phones, vehicles), arrival and departure times at stationary locations (stations of bicycles), transaction data at points of arrivaledeparture (e.g., public transport stations, tickets), user-contributed data in online media (geotagged), and microdata describing the built environment. This is the type of data that enable the study of spatio-temporal behaviors in long periods and for large areas. The chapters in this book, however, also show some new ways to procure data for travel behavior representing new directions of travel behavior data provision. For example, in one chapter the authors discuss the design of a virtual reality driving simulator and the types of data one can extract about learning environments. In another chapter, the authors designed a stated preference experiment combined with videos and revealed preference data collection. Chapters in this book also discuss the combination of revealed and stated preference data, combination of household travel diaries with archival information at fine spatial resolution, scenario data in simulation experiments, and real-life experiments with detailed tracking of the participants. These new behavioral measurement methods are discussed further in the summary of the IATBR2018 workshops in the last chapter of this book.
4.4 Behavioral analysis Behavioral analysis in this book is predominantly quantitative expanding past analytical frameworks adding flexibility in our ability to address substantive problems. However, as the workshops show, qualitative methods are also a must for understanding travel behavior in depth. Random utility discrete choice models are the most popular in this book and the conference due to massive developments in integrating the models with many other behavioral facets in the form of latent variables. This type of modeling is used for revealed preference data, stated preference data, and combinations of the two. Logit and Probit are the preferred functional forms for these models. The discrete choice models in this book represent the stateof-the-art in behavioral analysis, and they are mixed logit models, hybrid choice models, extensions of discrete choice models with latent variables in
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longitudinal settings, and models to represent multiple discreteness incorporating the choice of continuous variables in the same theoretical and data analytic framework (e.g., Multiple Discrete-Continuous Extreme Value models). A chapter is also broaching the subject of models with different decision weights representing power relations within a household. These are applied in both cross-sectional and longitudinal settings. This represents evidence not only of support for expanded and new theoretical frameworks, but also of added flexibility in modeling behavior accounting for a variety of factors to empirically test relationships among a wide array of behavioral facets. In addition to choice models, chapters in this book also use regression techniques to test correlation among a variety of observed and latent variables. These include Multinomial Logit, Probit, Ordered Probit on single behavioral facets, and Structural Equations Models (including versions that can handle combinations of categorical data, count data, and continuous data). These are flexible tools that one can use to combine data from different sources, explore new multivariate relationships, and test hypotheses about these relationships. Cluster analysis is also emerging as another technique to detect and describe data patterns and express these patterns in groups. In our travel behavior toolbox represented by the chapters in this book, we have simple kmeans cluster analysis, cluster analysis based on Probit models, hierarchical clustering combined with regression, point-pattern analysis using Densitybased spatial clustering, and pattern recognition with Mixed Markov Latent Class models. It is also worth mentioning that we see an emergence of spatiotemporal pattern recognition techniques that use survey data, archival data, and/or data from microsimulation experiments. Some of the techniques in this group have been labeled machine learning, and we return to this in the workshops chapter of this book.
5. Coda All four mapping dimensions of the travel behavior genome show that the strategy of addressing wicked problems by travel behavior research is twopronged. On the one hand, we expand the domain of behavioral analysis to include behavioral facets that are at a higher level than travel, including activities, human interaction, and location choices. On the other hand, we expand modeling frameworks to include psycho-social behavioral measures. These mapping dimensions also show an energetic push to map the changing environment and define new ontologies and sources of measurement of behavior as a response to the changing reality. We are still left with theoretical, methodological, and behavioral measurement questions, as the workshop summaries at the end of this book show. In closing, the travel behavior genome is ever-evolving, and its path of evolution is complex in a similar way to the wicked transportation planning
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problems. It is still unknown what type of substantive problems we will need to address due to the uncertainty of the potential upheaval from automation, global trends, changes in society, and internal changes to the individuals we observe. As the last chapter of this book shows, however, travel behavior analysts are ready to tackle these challenges. In fact, this last chapter shows the analytical challenge is to track behavioral context, revise our theoretical frameworks and models when needed to incrementally revise fundamental ontological definitions, develop new techniques, and create new opportunities for data collection about behavior.
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