Intercity Passenger Rail in the Context of Dynamic Travel Markets (2016)

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14 Chapter 2 concerns a review of the key lessons learned from the literature review, which is presented in full in NCRRP Web-Only Document 2, and documents the data collection process, including the surveys undertaken in conjunction with the project, and presents an overview of the results. 2.1 Highlights from the Project Bibliography This research project included an extensive literature review, which is presented in its entirety in the project bibliography in NCRRP Web-Only Document 2. The bibliography includes the formal citations and the authors’ abstracts for the most relevant articles, most of which were published in peer-reviewed journals. Other articles, such as agency documents, may not include formal abstracts. The following sections present highlights from the full literature review by theme. 2.1.1 Theme 1: Trends and Differences by Generation NCRRP 03-02 was largely concerned with change that might happen in the future, particularly in terms of possible changes in cultures, attitudes, and preferences related to more sustainable modes of travel, such as rail and intercity bus. The first theme includes literature reviews of two somewhat independent concepts. The first concept is general trends, including a possible decrease in VMT by various submarkets in the United States and in other places in the Western world. The second concept is somewhat more complicated, as it covers attitudinal and behavioral differences by age segment and possible shifts in behavior over time. That a younger age group has a different set of attitudes and behaviors toward transportation does not mean there is a shift occurring between generations—nor does it exclude the possibility. As they age, those in the younger age group could adjust their attitudes and behaviors to look more like the older genera- tions, particularly once they enter their child-rearing years. The information assembled in the bibliography reveals that something has changed, but there is little agreement on what this change means. To some, the change in national VMT per capita— shown in Figure 6 of this document and discussed in the article by McCahill (2014) of the State Smart Transportation Initiative—suggests that personal decision making is shifting away from auto orientation. For others, the lowered personal VMT reflects lower levels of employment, and a lack of disposable income. The results of the literature search identify the need for a rigorous examination of the relation- ship between transportation behavior and the motivations that influence behavior. For many researchers, there is no discordance at work when viewing the decreased levels of auto trip making. In the vision of Dutzik and Baxandall (2013), this represents a major shift in the culture of a C H A P T E R 2 Previous Research and the Collection of New Data

Previous Research and the Collection of New Data 15 generation. In the view of others, it represents the result of bad economic conditions for many members of the millennial generation [see, for example, Ralph (2015), quoted below]. Figure 6 shows FHWA traffic monitoring data for per-person VMT over time. The fact that automobile travel in the United States has decreased for millennials seems unmistakable from Figure 7 in Chapter 1, shown there as VMT per driver. However, the reasons behind this decrease are worthy of more policy research attention. Work under way at the University of California at Los Angeles, including that reported in Blumenberg et al. (2013), explores the depth of the unemployment (or underemployment) problem for millennials. A series of studies by Le Vine and Jones (2012) on British travel, whose original abstracts are included in the bibliography, shows a strong pattern of decrease in the auto mileage of males under 50, with a weaker pattern of increase in males over 50. This is a startling contrast to the lack of such a decrease in females under 50, with increases reported for every age group above 30. Important work by Kuhnimhof et al. (2012) allows more detailed observation of the role of gender in these patterns. In Germany, they found a sharp decline in auto travel for men after 2000 and a much more moderate decline for women. In the UK, they found a sharp decline for men and a much smaller decline for women, in a generally similar time frame. Later work of Le Vine, Jones et al. (2014) and Le Vine, Latinopoulos et al. (2014a,b) explore the UK patterns in more detail, finding one group to be driven by financial constraints, while a second group had more nuanced reasons for the delay in obtaining a driver’s license. Le Vine et al. establishes that the delayed driver’s licenses were not associated with greater Internet browsing or greater environmental sensitivity. Two additional works on this theme were published in 2015. The PhD thesis of Kelcie Ralph presents a highly original work which examines the type of information that can be mined from the NHTS, in this case using a form of market segmentation to better interpret the data. The NHTS has very limited content about values and preferences (except about the journey to school), but Ralph was able to creatively draw conclusions in spite of these data limitations. Concerning the supposed rise of a new, urbane age cohort, Ralph (2015) concluded that . . . economic constraints, role deferment, and racial/ethnic compositional changes in the population primarily explain the travel trends during this period. The evidence in support of preferences and residential location explanations was substantially more limited. The concluding chapter contextualizes these findings, arguing that a large and growing share of young adults suffer from transportation disadvantage. At about the same time, Noreen McDonald of the University of North Carolina at Chapel Hill, in a well-received article in the Journal of the American Planning Association, used the same basic data sources to examine the sources of the drop in VMT. Again, the author did not utilize any studies of values, attitudes, and preferences in the article. McDonald (2015) was able to estimate the portion of the total phenomenon of lowered VMT attributable to several causal forces: Among young adults, lifestyle-related demographic shifts, including decreased employment, explain 10% to 25% of the decrease in driving; millennial-specific factors such as changing attitudes and use of virtual mobility (online shopping, social media) explain 35% to 50% of the drop in driving; and the general dampening of travel demand that occurred across all age groups accounts for the remaining 40%. 2.1.2 Theme 2: Long-Distance Travel This rail research project focused on intercity travel, and because it looked at rail in a competitive environment, the focus was primarily on those trips between 100 and 500 miles, which narrowed the focus somewhat. Theme 2 explores these long-distance issues. In terms of building the methodological tools to better understand longer-distance markets, the US DOT has undertaken a series of projects that were beneficial to the research team. Steer

16 Intercity Passenger Rail in the Context of Dynamic Travel Markets Davies Gleave produced a series of methodological reports for US DOT, one of which is titled, HSIPR Best Practices: Ridership and Revenue Forecasting. Some of the key concepts recom- mended in that report re-appear in the FRA’s Project CONNECT, which includes a travel fore- casting model that breaks out diversion to rail from auto, direct air, and connecting air services. A similar model, just for diversion to rail from air, was developed through Airport Cooperative Research Program (ACRP) Project 03-23, and has been refined in this project and its application documented in Chapter 9. This ACRP model allows an exceptional level of policy sensitivity, as it is designed to let the analyst vary all of the input assumptions for various forms of sensitivity analysis and policy exploration. A most relevant article in the literature is “Travel Behavior of the Lone Rangers: An Application of Attitudinal Structural Equation Modeling to Intercity Transportation Market Segmentation” by Ripplinger et al. (2011, Appendix Figure 10) in North Dakota. This paper is (by its own description) highly influenced by an earlier paper by Outwater et al. (2003) titled, “Use of Structural Equation Modeling for an Attitudinal Market Segmentation Approach to Mode Choice and Ridership Forecasting,” which focused on within-metropolitan-area travel in the San Francisco Bay Area. This paper was an early example of the direct use of market segmentation to predict changes is travel behavior. 2.1.3 Theme 3: Attitudinal Theories Applied to Transportation NCRRP 03-02 explored the relationship between values, attitudes, and preferences concerning longer-distance travel choices and the actual travel behaviors undertaken. As such, it benefited from a long tradition of research in the application of attitudes to the understanding of trans- portation behavior. A major contribution to the field was made in the 1970s by Dobson et al. (1978). More recently, a wide variety of theories of the role of attitudes, lifestyles, and behavior have been applied and quickly summarized in Holz-Rau and Scheiner (2010). As noted previously, original abstracts for these articles are reproduced in the bibliography in NCRRP Web-Only Document 2. Most of the research concerning the effect of attitudes in the explanation of transportation behavior starts with references to Ajzen’s TPB or its predecessor, the Theory of Reasoned Action (Fishbein and Ajzen 2010). Ajzen’s diagram explaining the theory is shown as Figure 38 in Chapter 4. The review of the literature shows that very few TPB studies have had the luxury of benefiting from a longitudinal design: a major exception is the article titled, “Choice of Travel Mode in the Theory of Planned Behavior: The Roles of Past Behavior, Habit, and Reasoned Action” by Bamberg et al. (2003), which both examines recorded intent to change mode to the bus after a free pass is implemented and captures actual change of transportation mode after the implemen- tation of the bus pass. This article is recommended to readers interested in the role of attitudes in impacting mode choice and those who want to understand the TPB better. Importantly, almost all articles that apply the TPB to transportation behavior tend to embellish it by adding one, or several, additional factors as direct predictors of intent. The paper, titled “Reduced Use of Environmentally Friendly Modes of Transportation Caused by Perceived Mobil- ity Necessities: An Extension of the Theory of Planned Behavior,” by Haustein and Hunecke (2007) first ran a reference model of the TPB without the additional factor, and then again with it. In this work, the authors suggest that, for their purposes, a concept of “perceived mobility neces- sities” would improve the overall model performance. This would perhaps be an example of a modification for one kind of application of the theory, and not a need to modify the theory itself. Of interest to the work plan is the conceptual model of attitudes toward transportation developed by Noblet et al. (2014). These authors hypothesized that two underlying factors—environmental concern and support environmental group—influenced the three basic predictors in the TPB:

Previous Research and the Collection of New Data 17 attitude, subjective norm, and perceived behavioral control. An ambitious attempt to integrate a very wide variety of physical and cognitive processes was proposed by Spears et al. (2013) in a study of travel behavior in Los Angeles, which places the TPB in the center of a much more elaborate over-arching structure. Perhaps the most ambitious proposal to integrate physical and cognitive factors was in the Van Acker et al. (2010) article, “When Transport Geography Meets Social Psychology: Toward a Conceptual Model of Travel Behaviour.” There a core set of factors from transport geography (i.e., locational behavior, activity behavior, and travel behavior) is allowed to interact with a set of “perceptions, attitudes, and preferences” for each factor from transport geography. From the vantage point of social psychology, there is a large body of literature building on the concept of “reasoned action,” which emphasizes the process of deliberately thinking through the implications of decisions like the choice of mode of long-distance transportation (Fishbein and Ajzen 2010). There is also a significant body of literature that reflects the concern that much of the process of travel choice simply reflects habit based on a history of repetitive actions (Aarts et al. 1997). And, of course, there are theories that bypass this dichotomy in general and propose different frameworks altogether, such as that of Triandis (1989). 2.1.4 Theme 4: Hybrid Models to Integrate Attitudes The fourth theme concerns those theories and methods specifically under discussion for the improvement of mode choice modeling by the inclusion of attitudes and preferences into the model estimation process. In addition to reflecting the approaches based in social psychology, this theme includes a part of a growing body of literature concerning new and developing approaches to mode choice modeling in fields such as marketing, specifically including attitudes and preferences along with times and costs in the prediction process. Theme 4 includes the important article “Extended Framework for Modeling Choice Behavior” (Ben-Akiva, McFadden et al. 1999). As the original article can be located on the Internet, those who are interested in the progress reported in Chapter 5 are encouraged to read the full article. It states the basic logic in support of an integrated model as well as anything written since. Theme 4 also presents the first references in the literature review to the ICLV models. An early ICLV model is presented in the paper by Vredin-Johansson et al. (2006), titled “The Effects of Attitude and Personality Traits on Mode Choice.” Temme et al. (2008) showed how such a complex model could be built with publicly available structural equation modeling software. In an early example of the kind of integration we implemented in Chapter 5. Temme et al. concluded that this model format developed in the transportation domain could be more widely applied in marketing studies. Building on this work, Paulssen et al. (2014) created an ambitious model (see Figure 8) that spells out how key personality traits—power, hedonism, and security—are associated with more short-term attitudes predicting mode choice—comfort/convenience, ownership, and flexibility. This model deals with several of the key factors identified in NCRRP 03-02 in that it connects longer-term values—or, in the case of Paulssen et al. (2014), personality traits—to more immediate and short-term attitudes, which are assumed to directly influence mode choice. Readers with a particular interest in Chapter 6, concerning new approaches to scenario analysis, should note the article by Chakraborty and McMillan (2015), “Scenario Planning for Urban Planners: Toward a Practitioner’s Guide,” from the Journal of the American Planning Association. A thoughtful critique of the application of the new methods is provided by Chorus and Kroesen (2014) in their article “On the (Im-)Possibility of Deriving Transport Policy Impli- cations from Hybrid Choice Models.” In fact, some of the format established in Chapter 6 was

18 Intercity Passenger Rail in the Context of Dynamic Travel Markets developed with this critique in mind. And, finally the thoughtful article “Statistical Properties of Integrated Choice and Latent Variable Models” by Vij and Walker (2015) helps provide a setting for determining the best application of the new tools. 2.1.5 Theme 5: Environmental Motivations and Strategies A major theme of much work in Europe concerning attitudes toward transportation, and mode choice in particular, is that of the role of pro-environmental concerns on actual travel decisions. The documentation of the literature review presents a cross section of published research concerning the role of pro-environmental attitude on transportation behavior. Most of the work is based on the experience of European populations, not North American populations. Clearly, the role of environmental motivations as contributors to the choice of bus or rail over auto or air needs some further research. But, a major review of the literature has not found much to support the concept that Americans are currently considering the global environmental implications of their modal choices in the markets of interest to NCRRP 03-02. The international data seem to be somewhat more nuanced, but not completely clear. For instance, looking at the idea that environmental sensitivity might be a factor in the recent pattern of delayed and lowered rates of drivers’ licenses in Britain, Le Vine et al. (2014) found that only 1% of those sampled even mentioned the issue. After creating a structural equations model based on the TPB, Grob (1995) Source: Paulssen et al. (2014, p. 877, Figure 2: ICLV model of travel mode choice as posited by the value–attitude– behavior hierarchy of cognition). © Springer Science+Business Media New York 2013, used with permission of Springer. Figure 8. Advanced ICLV model using three long-term values from the social psychological literature. Figure redacted for on‐line use; available in printed version. 

Previous Research and the Collection of New Data 19 concluded that “No effects on environmental behavior from factual knowledge was found.” Likewise, Line et al. (2010) found their sample of young people claimed “that their current envi- ronmentally friendly travel behaviors (such as walking or cycling to school) are not influenced by the issue of climate change. . . .” Delbosc and Currie (2013) reported that, in their panels of young people in Australia, “not one person in the sample spontaneously mentioned that environmental concerns shaped their travel choices; even when prompted, these concerns were far removed from travel decisions.” Vredin-Johansson et al. noted, “Previous research has . . . shown little support for environmental criteria being of importance in travel mode choices . . .” (2006, p. 509). On the other hand, the growing body of literature on the propensity to buy an electric vehicle reveals an interesting variety of motivations, including some which are pro- environmental in nature. The article by Rasouli and Timmermans (2013), “The Effect of Social Adoption on the Intention to Purchase Electric Cars: A Stated Choice Approach,” explores the role of normative pressure in the choice of vehicle purchase: “Results indicate that although social influence plays a less significant role than attributes of electric cars in the buying process, different elements of social networks do exert an influence on people’s buying decisions. These effects vary between friends, relatives, colleagues, and the larger peer group.” 2.1.6 Theme 6: Information Technologies and the Productivity of Time While no one can predict the future, one can reasonably predict that the enormous rise in the acceptance and use of ICT will have some impact on travel over the next 50 years. Whatever future intercity travel will look like, it is safe to assume that it will be influenced by ICT. The work plan allows the exploration of this in two somewhat different formats. First, the wide acceptance of ICT could allow a fundamental shift in the way in which in-vehicle time on public modes is evaluated. If one mode (e.g., car) does not allow for productive use of time, while a second mode (e.g., train) does allow for such productivity, the absolute importance of time minimization in mode choice might decrease over time. Second, the predicted emergence of real-time information to support the multisegment, multimodal trip may decrease the sense of anxiety that is associated with a complicated trip compared to the door-to-door convenience of the private automobile. A good introduction to this field comes from several published articles by Schwieterman and Fischer, including “Privacy Invades Public Space: The Growing Use of Portable Electronic Technology on Intercity Buses, Trains and Planes between 2009 and 2010” (2011b) and related articles about the new market for curbside bus services. The value placed on such use in the vehicle is explored by Dong et al. (2013) and Ettema et al. (2012). The question of the role of ICT in preparing the traveler for the trip is explored by Dzietkan (2008), Farag and Lyons (2012), and Kenyon and Lyons (2003). The possibility that more time online equates to lower levels of VMT is challenged by Le Vine et al. (2014a) in “Establishing the Links Between Online Activity and Car Use: Evidence from a Combined Travel Diary and Online-Activity Pseudo-Diary Data Set.” In one of a series of articles (some of which are in Bibliography Theme 1), they conclude, “It was found that, net of other effects, Internet usage is positively associated with car use.” 2.1.7 Theme 7: Application of Market Segmentation Techniques A key technical strategy in the integration of attitudes with more traditional predictive factors is the use of market segmentation. Essentially, by dividing the full sample into market segments, the model builder, or the analyst in general, can explicitly deal with attitudes, values, and prefer- ences while retaining the traditional prediction power of times, costs, and basic demographics. Anable (2005) was a pioneer in making segmentation a key strategy for policy analysis, based on her application of the TPB.

20 Intercity Passenger Rail in the Context of Dynamic Travel Markets An excellent introduction to segmentation for anyone involved in transportation analysis is provided by TCRP Report 36: A Handbook: Using Market Segmentation to Increase Transit Rider- ship (Elmore-Yalch 1998). While TCRP Report 36 represents a good introduction to the subject, the methods recommended may be somewhat dated. A more recent approach to market seg- mentation methodology is provided by Vermunt and Magidson (2005a), who are the developers of the Latent Gold software package available to the public. This statistical method was applied to the question of a safe driving culture by Coogan et al. (2014). Another state-of-the-art application of segmentation technology was provided by Ayvalik et al. (2008) in a study of potential markets for transit-oriented development in the San Francisco Bay Area. This followed upon similar work by Cambridge Systematics for transit riders in San Mateo County (Zhou et al. 2004). Karash et al. applied this kind of market research for transit in TCRP Report 123: Understanding How Individuals Make Travel and Location Decisions: Implications for Public Transportation (2008). There, the sample was divided into five segments, assembled by their similarity of attitudes relevant to increasing transit ridership. In an early effort, this 2004 survey exposed the different market segments to new ideas, such as the idea that people could get transit and traffic data sent right to their phones. The utilization of specific market segments was helpful in interpreting the results of the survey. A pioneering research effort in the analysis of improving the sustainability of travel was the article (and doctoral dissertation) by Anable (2005), “‘Complacent Car Addicts’ or ‘Aspiring Environmentalists’? Identifying Travel Behaviour Segments Using Attitude Theory.” Anable undertook a market segmentation procedure for change in modal behavior based on the con- struct of the TPB, as expanded. The Anable work is important in that it utilized a specific theory within social psychology (the TPB) to help order, analyze, and interpret the meaning of the attitudes and beliefs revealed in the survey process. 2.2 Data Collection Methodologies 2.2.1 Data Collected to Support the Project’s Goals The research team surveyed more than 6,000 individuals to understand their long-term values and short-term attitudes and subjected more than 5,000 of them to a stated choice exercise through which they indicated their preference for the choice of a long-distance mode, based on highly detailed market conditions. 2.2.2 Survey Sampling Frame and Eligibility Requirements The overall survey sample was divided into two markets: the NEC and the Cascade Corridor. Since this project was research driven, the research team set out to obtain a sample that provided good coverage and enough samples of various income, age, and home location distributions rather than strictly being population-proportional. Sampling Sources and Procedure Data were collected through several different sampling strategies. For the NEC survey sampling, the research team utilized the e-mail addresses of respondents from the Northeast Corridor Commission’s Automobile Origin–Destination (OD) Study who indicated that they would be willing to participate in future research. Afterwards, the research team coordinated with commercial sample providers and recruited additional respondents. A supplemental, pur- chased online sample was targeted to counterbalance some of the demographic skew in the NEC Auto OD sample (e.g., the sample was older, more likely to own car). For the Cascade Corridor,

Previous Research and the Collection of New Data 21 no preexisting sample existed and the entire sample was purchased from a commercial sample provider. Data Cleaning and Sample Size A total of 6,184 completed surveys were collected. To ensure that only valid responses were accepted, the data were cleaned based on the following criteria: • Respondents were excluded if they completed the entire survey in less than 6 minutes, since that survey completion time did not allow respondents to read, process, and respond to the questions that were presented to them. • Respondents were excluded if they consistently “straight-lined.” Straight-lining refers to the response set whereby respondents select the same answer choice on an attitudinal battery (e.g., always selecting “strongly agree” for all attitudinal questions that are presented on a screen). To be excluded based on straight-lining, a respondent had to straight-line on all attitudinal batteries of the survey. • Respondents were excluded if they selected an option in a stated choice exercise for which the chosen versus the cheapest option available in the exercise was in the 95.5 percentile of cost. In total, less than 10% of the sample were excluded based on the foregoing criteria, leaving a final sample size of 5,625, of which 513 respondents came from the Cascade Corridor and 5,112 from the NEC. 2.2.3 Additional Data Collection In addition to the previously mentioned sample, supplemental data were collected from 517 NEC respondents. These respondents were recruited from the NEC e-mail list and, strictly speaking, did not live in one of the eligible metropolitan areas (i.e., metropolitan areas of Boston, New York City, Philadelphia, and Washington, DC). For instance, these respondents might live in between two larger metropolitan areas (Plainsboro Township, New Jersey, located in between New York City and Philadelphia) or too far away from any of the metropolitan areas (e.g., Killingworth, Connecticut, or Perryville, Maryland). Of importance, these individuals did not complete the stated choice exercise but did complete the attitudinal statements and any questions not related to the stated choice exercise. Unless explicitly mentioned otherwise, analyses are based on the smaller sample of those 5,625 respondents who completed the entire survey, including the stated choice exercise. All 6,142 survey responses were used in the TPB model, whose data requirements did not include any data from the exercise. 2.2.4 Additional Surveys Used in the Project RSG conducted a 2014 survey on intercity travel behavior on behalf of the New England Trans- portation Institute (NETI) and the University of Vermont’s Transportation Research Center (UVM TRC). Respondents were recruited by a commercial provider and directed to RSG’s survey platform. After eliminating hasty responses (i.e., under 6 minutes) and respondents who straight- lined, a total of 2,560 valid responses were collected. Massachusetts had the highest number of respondents, followed by New Hampshire, Maine, and Vermont. In addition, the data analysis process in Chapter 3 utilized additional survey resources, including an 11,000-person survey undertaken by RSG, also in 2014, for TransitCenter, an advocacy group for better public transportation. A shortened description of the methods used in that survey can be found in TransitCenter and RSG Inc. (2014). The survey instruments used in both the UVM TRC and TransitCenter projects were designed by RSG to provide data largely compatible from one study to another.

22 Intercity Passenger Rail in the Context of Dynamic Travel Markets 2.2.5 Design of the Survey Instrument The survey instrument was created during a several-month process to ensure that it contained all of the data-gathering activity required for the development of the new ICLV models, and for the development of the new NCRRP long-distance attitudinal models. Examples of the kinds of data collected are shown in Figure 9. Data Needs for Activities in the Development of the Stated Choice Exercise The research team developed a data collection approach to gather cost and time sensitivities. These data were used in the development of the full ICLV model, and the reference or “base case” model without the attitude component. An image of what the survey respondent saw is shown here as Figure 10. Each survey respondent was asked to provide detailed information about one specific, eligible intercity trip. If the respondent reported taking several eligible trips, he or she was asked about the most recent. The programmed web survey defined a trip for the user, based on the home location of the traveler. For NEC respondents, distances from their home zip code to Boston, New York City, Philadelphia, and DC were computed, and the city that was located the shortest distance from the home zip code was assigned as the respondent’s “metro home area.” Similarly, Cascade par- ticipants were assigned to Portland, Oregon, as their “metro home area,” if their home zip code was one of the qualifying counties in Oregon (Benton, Clackamas, Columbia, Lane, Linn, Marion, Multnomah, Polk, Washington, Yamhill) or Clark County, Washington. Participants with zip codes in all other qualifying counties in Washington (King, Kitsap, Mason, Pierce, Skagit, Snohomish, Thurston, and Whatcom) and all qualifying counties in Canada (Greater/Metro Vancouver, Fraser Valley) were assigned to Seattle, Washington, as their metro home area. The results of this stated choice exercise are reported in Section 5.1 in a model with no reference to attitudes and values. Section 5.2 shows the use of the results of the model in the creation of the ICLV model. The Survey Instrument The research team developed a refined approach to the design of a flexible, innovative, and statistically adequate/efficient survey. This is a key prerequisite to enable advanced modeling A set of near-term attitudes including….A set of underlying values including…. Concern for the Environment Belief in Urbanism/ High-Density Life Value Privacy and Separation from Other People Orientation to the Private Auto Orientation toward Information Communications Technology Perception of relative times and costs Convenience/ Connectivity Perception of stress (vs. other modes) Perception of productivity of time Convenience/inflexibility of train schedules Intent to Choose Train for This Trip Demographics of the traveler Belief that others like you would approve Perception of safety of train trip environment Figure 9. The survey instrument gathered respondents’ values and attitudes.

Previous Research and the Collection of New Data 23 approaches that can accommodate attitudinal factors to better understand how those attitudes influence choice behavior beyond level-of-service variables. This is done using advanced models that can estimate latent factors and segments that are driving different choice behaviors. As can be seen in the set of questions presented in Section 2.3.3, the research team designed the survey to gather data on both level-of-service variables (e.g., times and costs) and attitudinal variables in order to gain a deeper understanding of intercity mode choice using advanced models. 2.3 Survey Respondent Sample Overview 2.3.1 Demographics The results confirm that the primary goal of obtaining a cross section of the population with good coverage in various demographic categories was achieved. For instance, Figure 11 shows the income distribution was close to normal, and respondents from different age groups were Figure 10. Example screenshot how data was collected in support of the stated choice modeling used in NCRRP. Figure 11. Household income distribution.

24 Intercity Passenger Rail in the Context of Dynamic Travel Markets Figure 12. Age distribution. Note: Bars total more than 100% due to multiple responses per record. Figure 13. Trip purpose of most recent trip. Figure 14. Party size of most recent trip. represented in almost equal proportions. Further, as would be expected, most respondents were employed full time; most identified as White though the sampling effort was also successful in recruiting a sizable number of respondents who identified as other than White (20%). The age distributions provide adequate samples for key age categories to be used in the project (Figure 12). 2.3.2 Characteristics of Recent Trip The most common recent trip that respondents reported was traveling with one additional travel companion to visit friends or relatives (see Figures 13 and 14).

Previous Research and the Collection of New Data 25 As might be expected, respondents decided to take different modes depending on how many other people were in the party, such that traveling with just one additional person increased the chance of traveling by car quite strongly. Specifically, Figure 15 shows that only 44% of those traveling alone took a personal car and 20% traveling alone went by train, making it the second most common travel mode among this group. However, having an additional travel companion increased the likelihood of taking a personal car to 74%. This percentage stayed relatively stable for party sizes of three and four people, but decreased again for groups with five or more people (58%). 2.3.3 Attitudinal Variables Table 5 presents the major attitudinal questions (sometimes shortened) included in the survey, followed by the mean value and the standard deviation. The first results from the attitudinal portion of the survey can be seen in Table 6. Table 6 presents a matrix of the correlations between and among the attitudinal variables, to which has been added one demographic variable: age. Being a millennial is given the value 0 and being older is given the value 1. The first column in the matrix shows the relationship between the row variable and age category: a positive correlation shows that, as age increases, the values of the column variable increases. For all correlations shown in Table 6, two asterisks (**) mean that the correlation is significant at the p < 0.01 level and one asterisk (*) means that the correlation is significant at the p < 0.05 level, showing a weaker statistical connection. No asterisk implies that there is no statistically significant relationship between the column variable and the row variable. Figure 15. Mode of recent trip by group size.

26 Intercity Passenger Rail in the Context of Dynamic Travel Markets Atudinal Variable Mean Std. Devia on I enjoy being out and about and observing people 5.53 1.27 I like a neighborhood where I can walk to a village center 5.32 1.57 If everyone works together, we could improve the environment 5.95 1.27 Value having a private home with separaon from others 5.00 1.62 Value living in a community with mix of people and backgrounds 5.05 1.49 I feel I am less dependent on cars than my parents 3.42 1.88 I need to drive a car to get where I need to go 5.18 1.73 I love the freedom and independence I get from car 5.73 1.44 It is important to me to control the radio and the air condioning 4.90 1.47 I would prefer to borrow, share, or rent a car just when I need it 2.56 1.70 I feel really stressed when driving congeson in big cies 4.64 1.80 With driverless cars I would be less likely to travel by rail or bus 3.95 1.65 On a train or a bus with people I do not know is uncomfortable 3.11 1.62 I don't mind traveling with people I do not know 4.64 1.59 The process of going through airport security is stressful 4.42 1.70 Important to me to receive email or text updates about trip 5.15 1.47 Using a laptop, tablet, or smartphone is important to me 5.28 1.43 I could deal with the schedules offered by the train 4.89 1.51 I would need the flexibility of a car at my desnaon 4.28 2.07 Geng to the train staon is inconvenient 4.07 1.87 Sharing a car with others for such a trip seems unpleasant 3.93 1.73 Esmate cost of train more than the cost by car 4.94 1.63 Esmate cost of bus more than the cost by car 3.67 1.77 Esmate cost of train more than the cost by bus 5.42 1.40 Worry about personal safety/disturbing behavior Bus 4.15 1.68 Worry about personal safety/disturbing behavior Train 3.50 1.61 Worry about personal safety/disturbing behavior Air 3.45 1.67 Worry about personal safety/disturbing behavior Car 2.75 1.60 Concerned about flexibility of schedules Air 4.85 1.60 Concerned about flexibility of schedules Bus 4.81 1.52 Concerned about flexibility of schedules Train 4.69 1.54 Compared to car, I would be less red and stressed by air 4.13 1.78 Compared to car, I would be less red and stressed by bus 3.93 1.68 Compared to car, I would be less red and stressed by train 4.94 1.61 Difficult for me to get from train staon to where I need to go 3.83 1.82 Worry about crime or unruly behavior at the train staon and train 3.17 1.59 I would feel uncomfortable being on the train with strangers 2.80 1.51 Having to be with people whose behavior I find unpleasant 3.86 1.57 It might be unsafe to make this trip by train 2.75 1.46 People whose opinion I value would approve of the train 5.06 1.42 My friends and coworkers usually take the train 3.80 1.62 My family would approve of my taking the train 5.10 1.51 Most people who are important in my life would take the train 3.44 1.93 I could easily take the train for this trip 5.40 1.87 How possible is it to take the train for this trip 5.75 1.66 How efficient to take the train for this trip 4.54 1.93 How pleasant to take the train for this trip 5.00 1.60 I would definitely consider taking the train for this trip 5.04 1.90 How likely for you to take the train for this trip 3.84 2.06 Table 5. Means and standard deviations of the 49 attitudinal variables from the survey (sample size  6,142).

(continued on next page) Table 6. Correlations between/among survey attitudinal variables (sample size  6,142). M i l l e n n i a l i s z e r o , o l d e r i s o n e I e n j o y b e i n g o u t a n d a b o u t a n d o b s e r v i n g p e o p l e I l i k e a n e i g h b o r h o o d w h e r e I c a n w a l k t o a v i l l a g e c e n t e r I f e v e r y o n e w o r k s t o g e t h e r , w e c o u l d i m p r o v e t h e e n v i r o n m e n t V a l u e h a v i n g a p r i v a t e h o m e w i t h s e p a r a  o n f r o m o t h e r s V a l u e l i v i n g i n c o m m u n i t y w i t h m i x o f p e o p l e I f e e l I a m l e s s d e p e n d e n t o n c a r s t h a n m y p a r e n t s I n e e d t o d r i v e a c a r t o g e t w h e r e I n e e d t o g o I l o v e t h e f r e e d o m a n d i n d e p e n d e n c e I g e t f r o m c a r I m p o r t a n t t o m e t o c o n t r o l t h e r a d i o a n d t h e a i r c o n d i  o n i n g I w o u l d p r e f e r t o b o r r o w , s h a r e , o r r e n t a c a r j u s t w h e n I n e e d i t I f e e l r e a l l y s t r e s s e d w h e n d r i v i n g c o n g e s  o n i n b i g c i  e s W i t h d r i v e r l e s s c a r s I w o u l d b e l e s s l i k e l y t o t r a v e l b y r a i l o r b u s O n a t r a i n o r a b u s w i t h p e o p l e I d o n o t k n o w i s u n c o m f o r t a b l e I d o n ' t m i n d t r a v e l i n g w i t h p e o p l e I d o n o t k n o w T h e p r o c e s s o f g o i n g t h r o u g h a i r p o r t s e c u r i t y i s s t r e s s f u l I m p o r t a n t t o m e t o r e c e i v e m a i l o r t e x t u p d a t e s a b o u t t r i p Millennial is zero, older is one 1 0.01 0.046** 0.004 0.022 0.019 0.155** 0.102** 0.085** 0.058** 0.122** 0.028* 0.077** 0.113** 0.014 0.014 0.054** I enjoy being out and about and observing people 0.01 1 0.232** 0.245** 0.008 0.252** 0.048** 0.038** 0.081** 0.054** 0.011 0.024 0.019 0.075** 0.148** 0.001 0.077** I like a neighborhood where I can walk to a village center 0.046** 0.232** 1 0.207** 0.049** 0.289** 0.167** 0.099** 0.021 0.069** 0.121** 0.072** 0.057** 0.037** 0.139** 0.052** 0.111** If everyone works together, we could improve the environment 0.004 0.245** 0.207** 1 0.066** 0.294** 0.053** 0.037** 0.070** 0.052** 0.016 0.090** 0.032* 0.036** 0.108** 0.048** 0.114** Value having a private home with separaon from others 0.022 0.008 0.049** 0.066** 1 0.058* 0.078** 0.146** 0.165** 0.090** 0.079** 0.042** 0.073** 0.108** 0.049** 0.051** 0.031* Value living in a community with mix of people and backgrounds 0.019 0.252** 0.289** 0.294** 0.058* 1 0.148** 0.060* 0.004 0.061* 0.114** 0.057* 0.011 0.022 0.205** 0.001 0.122** I feel I am less dependent on cars than my parents 0.155** 0.048** 0.167** 0.053** 0.078** 0.148** 1 0.301** 0.193** 0.002 0.305** 0.074** 0.074** 0.002 0.115** 0.053** 0.079** I need to drive a car to get where I need to go 0.102** 0.038** 0.099** 0.037** 0.146** 0.060* 0.301** 1 0.372** 0.183** 0.227** 0.045** 0.033* 0.117** 0.039** 0.046** 0.023 I love the freedom and independence I get from a car 0.085** 0.081** 0.021 0.070** 0.165** 0.004 0.193** 0.372** 1 0.173** 0.276** 0.021 0.023 0.079** 0.021 0.044** 0.063** It is important to me to control the radio and the air condioning 0.058** 0.054** 0.069** 0.052** 0.090** 0.061* 0.002 0.183** 0.173** 1 0.030* 0.116** 0.087** 0.125** 0.009 0.097** 0.138** I would prefer to borrow, share, or rent a car just when I need it 0.122** 0.011 0.121** 0.016 0.079** 0.114** 0.305** 0.227** 0.276** 0.030* 1 0.076** 0.124** 0.043** 0.118** 0.054** 0.059** I feel really stressed when driving congeson in big cies 0.028* 0.024 0.072** 0.090** 0.042** 0.057* 0.074** 0.045** 0.021 0.116** 0.076** 1 0.100** 0.090** 0.039** 0.217** 0.102** With driverless cars I would be less likely to travel by rail or bus 0.077** 0.019 0.057** 0.032* 0.073** 0.011 0.074** 0.033* 0.023 0.087** 0.124** 0.100** 1 0.134** 0.005 0.093** 0.102** On a train or a bus with people I do not know is uncomfortable 0.113** 0.075** 0.037** 0.036** 0.108** 0.022 0.002 0.117** 0.079** 0.125** 0.043** 0.090** 0.134** 1 0.262** 0.164** 0.042** I don't mind traveling with people I do not know 0.014 0.148** 0.139** 0.108** 0.049** 0.205** 0.115** 0.039** 0.021 0.009 0.118** 0.039** 0.005 0.262** 1 0 0.116** The process of going through airport security is stressful 0.014 0.001 0.052** 0.048** 0.051** 0.001 0.053** 0.046** 0.044** 0.097** 0.054** 0.217** 0.093** 0.164** 0 1 0.093** Important to me to receive email or text updates about trip 0.054** 0.077** 0.111** 0.114** 0.031* 0.122** 0.079** 0.023 0.063** 0.138** 0.059** 0.102** 0.102** 0.042** 0.116** 0.093** 1 Using a laptop, tablet, or smartphone is important to me 0.090** 0.108** 0.101** 0.110** 0.045** 0.088** 0.074** 0.032* 0.074** 0.139** 0.056** 0.113** 0.116** 0.059** 0.106** 0.086** 0.323** I could deal with the schedules offered by the train 0.027* 0.114** 0.156** 0.118** 0.015 0.174** 0.152** 0.081** 0.008 0.023 0.099** 0.086** 0.034** 0.109** 0.193** 0.054** 0.116** I would need the flexibility of a car at my desnaon 0.039** 0.033** 0.039** 0.014 0.092** 0.021 0.107** 0.188** 0.184** .041** 0.063** 0.052** 0.077** 0.162** 0.071** 0.069** 0.021 Geng to the train staon is inconvenient 0.054** 0.024 0.084** 0.022 0.111** 0.063* 0.124** 0.208** 0.155** 0.052** 0.060** 0.056** 0.058** 0.148** 0.062** 0.118** 0.002 Sharing a car with others for such a trip seems unpleasant 0.016 0.051** 0.015 0.029* .104** 0.034 0.002 0.095** 0.095** 0.138** 0.008 0.093** 0.044** 0.191** 0.117** 0.099** 0.030* Esmate cost of train more than the cost by car 0.027* 0.037** 0.045** 0.063** 0.047** 0.004 0.001 0.102** 0.107** 0.050** 0.035** 0.054** 0.036** 0.030* 0.039** 0.096** 0.067** Esmate cost of bus more than the cost by car 0.012 0.007 0 0.002 0.060** 0.028 0.043** 0.129** 0.100** 0.048** 0.001 0.043** 0.084** 0.141** 0.026* 0.071** 0.024 Esmate cost of train more than the cost by bus 0.004 0.053* 0.070** 0.116** 0.036 0.065** 0.069** 0.013 0.041 0.076** 0.035 0.101** 0.021 0.008 0.104** 0.067** 0.079** Worry about personal safety/disturbing behavior – Bus 0.080** 0.021 0.021 0.009 0.093** 0.097** 0.018 0.075** 0.072** 0.103** 0.001 0.103** 0.080** 0.257** 0.101** 0.151** 0.085** Worry about personal safety/disturbing behavior – Train 0.103** 0.046** 0.013 0.016 0.103** 0.097** 0.024 0.067** 0.069** 0.086** 0.018 0.067** 0.110** 0.326** 0.135** 0.114** 0.039** Worry about personal safety/disturbing behavior – Air 0.069** 0.048** 0 0.005 0.068** 0.055* 0.008 0.044** 0.028* 0.072** 0.048** 0.086** 0.084** 0.230** 0.077** 0.172** 0.050** Worry about personal safety/disturbing behavior – Car 0.136** 0.005 0.038** 0.002 0.013 0.03 0.131** 0.040** 0.040** 0.038** 0.174** 0.087** 0.109** 0.118** 0.026* 0.071** 0.047** Concerned about flexibility of schedules – Air 0.009 0.040** 0.037** 0.042** 0.069** 0.004 0.037** 0.099** 0.104** 0.078** 0.030* 0.070** 0.062** 0.077** 0.018 0.135** 0.073** Concerned about flexibility of schedules – Bus 0.005 0.023 0.019 0.037** 0.111** 0.012 0.093** 0.160** 0.146** 0.086** 0.077** 0.049** 0.085** 0.130** 0.024 0.091** 0.072** Concerned about flexibility of schedules – Train 0.021 0.012 0.008 0.033* 0.103** 0.023 0.078** 0.144** 0.135** 0.085** 0.057** 0.053** 0.066** 0.145** 0.036** 0.089** 0.054** Compared to car, I would be less red and stressed by air 0.059** 0.057** 0.044** 0.053** 0.037** 0.058* 0.068** 0.017 0.009 0.015 0.070** 0.063** 0.081** 0.018 0.063** 0.075** 0.071** Compared to car, I would be less red and stressed by bus 0.002 0.067** 0.092** 0.052** 0.003 0.085** 0.107** 0.062** 0.043** 0.01 0.145** 0.092** 0.087** 0.077** 0.145** 0.009 0.063** Compared to car, I would be less red and stressed by train 0.015 0.094** 0.130** 0.113** 0.004 0.182** 0.119** 0.052** 0.007 0.023 0.100** 0.149** 0.052** 0.123** 0.176** 0.053** 0.131** Difficult for me to get from train staon to where I need to go 0.027* 0.052** 0.050** 0.011 0.090** 0.091** 0.092** 0.172** 0.134** 0.047** 0.042** 0.014 0.074** 0.185** 0.075** 0.102** 0.02 Worry about crime or unruly behavior at the train staon and train 0.079** 0.044** 0.022 0.032* 0.109** 0.096** 0.013 0.076** 0.064** 0.089** 0.052** 0.045** 0.120** 0.338** 0.121** 0.105** 0.028* I would feel uncomfortable being on the train with strangers 0.090** 0.084** 0.040** 0.055** 0.098** 0.051* 0.030* 0.058** 0.037** 0.090** 0.064** 0.041** 0.111** 0.469** 0.199** 0.117** 0.012 Having to be with people whose behavior I find unpleasant 0.127** 0.036** 0.003 0.018 0.122** 0.059* 0.013 0.077** 0.057** 0.100** 0.037** 0.075** 0.106** 0.331** 0.136** 0.120** 0.031* It might be unsafe to make this trip by train 0.107** 0.069** 0.029* 0.038** 0.086** 0.095** 0.034** 0.058** 0.043** 0.070** 0.063** 0.041** 0.108** 0.334** 0.113** 0.089** 0.027* People whose opinion I value would approve of the train 0.034** 0.132** 0.121** 0.126** 0.007 0.156** 0.124** 0.029* 0.009 0.055** 0.078** 0.094** 0.018 0.105** 0.216** 0.063** 0.159** My friends and coworkers usually take the train 0.080** 0.059** 0.068** 0.048** 0.001 0.091** 0.158** 0.053** 0.021 0.038** 0.137** 0.075** 0.096** 0.01 0.088** 0.072** 0.104** My family would approve of my taking the train 0.014 0.127** 0.130** 0.124** 0.003 0.159** 0.113** 0.028* 0.008 0.055** 0.085** 0.090** 0.006 0.125** 0.211** 0.054** 0.152** Most people who are important in my life would take the train 0.072** 0.068** 0.088** 0.044** 0.015 0.098** 0.145** 0.049** 0.054** 0.039** 0.164** 0.064** 0.080** 0.012 0.095** 0.044** 0.083** I could easily take the train for this trip 0.025 0.084** 0.115** 0.074** 0.034** 0.133** 0.149** 0.097** 0.030* 0.011 0.069** 0.028* 00.018 0.087** 0.113** 0.014 0.086** How possible is it to take the train for this trip 0.021 0.092** 0.109** 0.083** 0.028* 0.135** 0.093** 0.044** 0.005 0.030* 0.038** 0.041** 0.009 0.089** 0.128** 0.007 0.091** How efficient to take the train for this trip 0.066** 0.078** 0.127** 0.073** 0.041** 0.148** 0.183** 0.122** 0.076** 0.02 0.145** 0.060** 0.039** 0.095** 0.137** 0.02 0.083** How pleasant to take the train for this trip 0.018 0.102** 0.136** 0.091** 0.062** 0.194** 0.165** 0.106** 0.077** 0.009 0.109** 0.056** 0.014 0.230** 0.228** 0.027* 0.093** I would definitely consider taking the train for this trip 0.016 0.121** 0.162** 0.105** 0.046** 0.174** 0.162** 0.108** 0.045** 0.015 0.102** 0.079** 0.021 0.169** 0.199** 0.002 0.119** How likely for you to take the train for this trip 0.058** 0.067** 0.093** 0.036** 0.036** 0.106** 0.180** 0.121** 0.087** 0.004 0.162** 0.061** 0.065** 0.085** 0.134** 0.030* 0.082**

Table 6. (Continued). U s i n g a l a p t o p , t a b l e t , o r s m a r t p h o n e i s i m p o r t a n t t o m e I c o u l d d e a l w i t h t h e s c h e d u l e s o ff e r e d b y t h e t r a i n I w o u l d n e e d t h e fl e x i b i l i t y o f a c a r a t m y d e s  n a  o n G e  n g t o t h e t r a i n s t a  o n i s i n c o n v e n i e n t S h a r i n g a c a r w i t h o t h e r s f o r s u c h a t r i p s e e m s u n p l e a s a n t E s  m a t e c o s t o f t r a i n m o r e t h a n t h e c o s t b y c a r E s  m a t e c o s t o f b u s m o r e t h a n t h e c o s t b y c a r E s  m a t e c o s t o f t r a i n m o r e t h a n t h e c o s t b y b u s . W o r r y a b o u t p e r s o n a l s a f e t y / d i s t u r b i n g b e h a v i o r – B u s W o r r y a b o u t p e r s o n a l s a f e t y / d i s t u r b i n g b e h a v i o r – T r a i n W o r r y a b o u t p e r s o n a l s a f e t y / d i s t u r b i n g b e h a v i o r – A i r W o r r y a b o u t p e r s o n a l s a f e t y / d i s t u r b i n g b e h a v i o r – C a r C o n c e r n e d a b o u t fl e x i b i l i t y o f s c h e d u l e s – A i r C o n c e r n e d a b o u t fl e x i b i l i t y o f s c h e d u l e s – B u s C o n c e r n e d a b o u t fl e x i b i l i t y o f s c h e d u l e s – T r a i n C o m p a r e d t o c a r , I w o u l d b e l e s s  r e d a n d s t r e s s e d b y a i r Millennial is zero, older is one 0.090** 0.027* 0.039** 0.054** 0.016 0.027* 0.012 0.004 0.080** 0.103** 0.069** 0.136** 0.009 0.005 0.021 0.059** I enjoy being out and about and observing people 0.108** 0.114** 0.033** 0.024 0.051** 0.037** 0.007 0.053* 0.021 0.046** 0.048** 0.005 0.040** 0.023 0.012 0.057** I like a neighborhood where I can walk to a village center 0.101** 0.156** 0.039** 0.084** 0.015 0.045** 0 0.070** 0.021 0.013 0 0.038** 0.037** 0.019 0.008 0.044** If everyone works together, we could improve the environment 0.110** 0.118** 0.014 0.022 0.029* 0.063** 0.002 0.116** 0.009 0.016 0.005 0.002 0.042** 0.037** 0.033* 0.053** Value having a private home with separaon from others 0.045** 0.015 0.092** 0.111** 0.104** 0.047** 0.060** 0.036 0.093** 0.103** 0.068** 0.013 0.069** 0.111** 0.103** 0.037** Value living in a community with mix of people and backgrounds 0.088** 0.174** 0.021 0.063* 0.034 0.004 0.028 0.065** 0.097** 0.097** 0.055* 0.03 0.004 0.012 0.023 0.058* I feel I am less dependent on cars than my parents 0.074** 0.152** 0.107** 0.124** 0.002 0.001 0.043** 0.069** 0.018 0.024 0.008 0.131** 0.037** 0.093** 0.078** 0.068** I need to drive a car to get where I need to go 0.032* 0.081** 0.188** 0.208** 0.095** 0.102** 0.129** 0.013 0.075** 0.067** 0.044** 0.040** 0.099** 0.160** 0.144** 0.017 I love the freedom and independence I get from a car 0.074** 0.008 0.184** 0.155** 0.095** 0.107** 0.100** 0.041 0.072** 0.069** 0.028* 0.040** 0.104** 0.146** 0.135** 0.009 It is important to me to control the radio and the air condioning 0.139** 0.023 0.041** 0.052** 0.138** 0.050** 0.048** 0.076** 0.103** 0.086** 0.072** 0.038** 0.078** 0.086** 0.085** 0.015 I would prefer to borrow, share, or rent a car just when I need it 0.056** 0.099** 0.063** 0.060** 0.008 0.035** 0.001 0.035 0.001 0.018 0.048** 0.174** 0.030* 0.077** 0.057** 0.070** I feel really stressed when driving congeson in big cies 0.113** 0.086** 0.052** 0.056** 0.093** 0.054** 0.043** 0.101** 0.103** 0.067** 0.086** 0.087** 0.070** 0.049** 0.053** 0.063** With driverless cars I would be less likely to travel by rail or bus 0.116** 0.034** 0.077** 0.058** 0.044** 0.036** 0.084** 0.021 0.080** 0.110** 0.084** 0.109** 0.062** 0.085** 0.066** 0.081** On a train or a bus with people I do not know is uncomfortable 0.059** 0.109** 0.162** 0.148** 0.191** 0.030* 0.141** 0.008 0.257** 0.326** 0.230** 0.118** 0.077** 0.130** 0.145** 0.018 I don't mind traveling with people I do not know 0.106** 0.193** 0.071** 0.062** 0.117** 0.039** 0.026* 0.104** 0.101** 0.135** 0.077** 0.026* 0.018 0.024 0.036** 0.063** The process of going through airport security is stressful 0.086** 0.054** 0.069** 0.118** 0.099** 0.096** 0.071** 0.067** 0.151** 0.114** 0.172** 0.071** 0.135** 0.091** 0.089** 0.075** Important to me to receive email or text updates about trip 0.323** 0.116** 0.021 0.002 0.030* 0.067** 0.024 0.079** 0.085** 0.039** 0.050** 0.047** 0.073** 0.072** 0.054** 0.071** Using a laptop, tablet, or smartphone is important to me 1 0.128** 0.009 0.024 0.042** 0.063** 0.013 0.051* 0.068** 0.065** 0.063** 0.051** 0.052** 0.060** 0.045** 0.092** I could deal with the schedules offered by the train 0.128** 1 0.168** 0.128** 0.011 0.031* 0.044** 0.153** 0.006 0.077** 0.015 0.061** 0.041** 0.104** 0.164** 0.102** I would need the flexibility of a car at my desnaon 0.009 0.168** 1 0.199** 0.075** 0.070** 0.174** 0 0.111** 0.170** 0.104** 0.01 0.142** 0.200** 0.242** 0.013 Geng to the train staon is inconvenient 0.024 0.128** 0.199** 1 0.127** 0.132** 0.143** 0.070** 0.101** 0.106** 0.038** 0.001 0.173** 0.205** 0.220** 0.037** Sharing a car with others for such a trip seems unpleasant 0.042** 0.011 0.075** 0.127** 1 0.070** 0.095** 0.071** 0.154** 0.109** 0.068** 0.091** 0.059** 0.087** 0.071** 0.016 Esmate cost of train more than the cost by car 0.063** 0.031* 0.070** 0.132** 0.070** 1 0.345** 0.351** 0.076** 0.036** 0.033** 0.01 0.132** 0.134** 0.148** 0.007 Esmate cost of bus more than the cost by car 0.013 0.044** 0.174** 0.143** 0.095** 0.345** 1 0.065** 0.080** 0.143** 0.107** 0.045** 0.127** 0.176** 0.178** 0.012 Esmate cost of train more than the cost by bus 0.051* 0.153** 0 0.070** 0.071** 0.351** 0.065** 1 0.024 0.063* 0.025 0.013 0.041 0.02 0.022 0.045 Worry about personal safety/disturbing behavior – Bus 0.068** 0.006 0.111** 0.101** 0.154** 0.076** 0.080** 0.024 1 0.587** 0.436** 0.194** 0.155** 0.208** 0.179** 0.030* Worry about personal safety/disturbing behavior – Train 0.065** 0.077** 0.170** 0.106** 0.109** 0.036** 0.143** 0.063* 0.587** 1 0.573** 0.237** 0.169** 0.206** 0.225** 0.060** Worry about personal safety/disturbing behavior – Air 0.063** 0.015 0.104** 0.038** 0.068** 0.033** 0.107** 0.025 0.436** 0.573** 1 0.285** 0.196** 0.149** 0.164** 0.003 Worry about personal safety/disturbing behavior – Car 0.051** 0.061** 0.01 0.001 0.091** 0.01 0.045** 0.013 0.194** 0.237** 0.285** 1 0.045** 0.024 0.030* 0.101** Concerned about flexibility of schedules – Air 0.052** 0.041** 0.142** 0.173** 0.059** 0.132** 0.127** 0.041 0.155** 0.169** 0.196** 0.045** 1 0.538** 0.570** 0.102** Concerned about flexibility of schedules – Bus 0.060** 0.104** 0.200** 0.205** 0.087** 0.134** 0.176** 0.02 0.208** 0.206** 0.149** 0.024 0.538** 1 0.728** 0.005 Concerned about flexibility of schedules – Train 0.045** 0.164** 0.242** 0.220** 0.071** 0.148** 0.178** 0.022 0.179** 0.225** 0.164** 0.030* 0.570** 0.728** 1 0.002 Compared to car, I would be less red and stressed by air 0.092** 0.102** 0.013 0.037** 0.016 0.007 0.012 0.045 0.030* 0.060** 0.003 0.101** 0.102** 0.005 0.002 1 Compared to car, I would be less red and stressed by bus 0.066** 0.170** 0.103** 0.058** 0.037** 0.018 0.016 0.042 0.119** 0.007 0.050** 0.135** 0.038** 0.038** 0.016 0.288** Compared to car, I would be less red and stressed by train 0.131** 0.304** 0.158** 0.075** 0.003 0.052** 0.042** 0.112** 0.023 0.068** 0 0.091** 0.028* 0.021 0.056** 0.337** Difficult for me to get from train staon to where I need to go 0.045** 0.193** 0.445** 0.330** 0.115** 0.111** 0.173** 0.025 0.122** 0.159** 0.086** 0.002 0.183** 0.251** 0.288** 0.035** Worry about crime or unruly behavior at the train staon and train 0.030* 0.093** 0.170** 0.124** 0.127** 0.017 0.150** 0.078** 0.321** 0.430** 0.308** 0.133** 0.114** 0.168** 0.166** 0.058** I would feel uncomfortable being on the train with strangers 0.031* 0.088** 0.145** 0.105** 0.150** 0.01 0.134** 0.023 0.238** 0.345** 0.246** 0.126** 0.077** 0.123** 0.130** 0.047** Having to be with people whose behavior I find unpleasant 0.057** 0.047** 0.119** 0.095** 0.137** 0.021 0.110** 0.018 0.319** 0.377** 0.275** 0.105** 0.128** 0.156** 0.171** 0.041** It might be unsafe to make this trip by train 0.021 0.084** 0.143** 0.115** 0.120** 0.015 0.150** 0.048 0.229** 0.353** 0.240** 0.138** 0.075** 0.104** 0.111** 0.046** People whose opinion I value would approve of the train 0.130** 0.320** 0.175** 0.084** 0.002 0.022 0.050** 0.086** 0.024 0.119** 0.039** 0.042** 0.004 0.063** 0.089** 0.080** My friends and coworkers usually take the train 0.097** 0.191** 0.150** 0.075** 0.022 0.030* 0.005 0.009 0.044** 0.024 0.072** 0.111** 0.018 0.062** 0.097** 0.049** My family would approve of my taking the train 0.124** 0.331** 0.189** 0.091** 0.004 0.043** 0.058** 0.118** 0.042** 0.140** 0.046** 0.048** 0.004 0.060** 0.095** 0.075** Most people who are important in my life would take the train 0.093** 0.222** 0.172** 0.106** 0.023 0.048** 0.005 0.009 0.005 0.021 0.032* 0.112** 0.017 0.092** 0.140** 0.064** I could easily take the train for this trip 0.088** 0.314** 0.135** 0.260** 0.02 0.017 0.067** 0.091** 0.018 0.068** 0.011 0.027* 0.063** 0.104** 0.151** 0.105** How possible is it to take the train for this trip 0.103** 0.258** 0.111** 0.152** 0.01 0.01 0.048** 0.092** 0.011 0.067** 0.028* 0.025 0.005 0.028* 0.065** 0.073** How efficient to take the train for this trip 0.110** 0.329** 0.225** 0.243** 0.028* 0.052** 0.086** 0.002 0.008 0.052** 0.027* 0.104** 0.057** 0.135** 0.191** 0.113** How pleasant to take the train for this trip 0.098** 0.338** 0.199** 0.153** 0.060** 0.005 0.068** 0.066** 0.138** 0.216** 0.095** 0.038** 0.038** 0.120** 0.145** 0.077** I would definitely consider taking the train for this trip 0.123** 0.391** 0.231** 0.180** 0.045** 0.009 0.084** 0.076** 0.065** 0.152** 0.060** 0.033* 0.022 0.094** 0.133** 0.106** How likely for you to take the train for this trip 0.107** 0.332** 0.244** 0.191** 0.016 0.083** 0.063** 0.014 0.01 0.053** 0.015 0.117** 0.041** 0.123** 0.177** 0.097**

Table 6. (Continued). C o m p a r e d t o c a r , I w o u l d b e l e s s  r e d a n d s t r e s s e d b y b u s C o m p a r e d t o c a r , I w o u l d b e l e s s  r e d a n d s t r e s s e d b y t r a i n D i ffi c u l t f o r m e t o g e t f r o m t r a i n s t a  o n t o w h e r e I n e e d t o g o W o r r y a b o u t c r i m e o r u n r u l y b e h a v i o r a t t h e t r a i n s t a  o n I w o u l d f e e l u n c o m f o r t a b l e b e i n g o n t h e t r a i n w i t h s t r a n g e r s H a v i n g t o b e w i t h p e o p l e w h o s e b e h a v i o r I fi n d u n p l e a s a n t I t m i g h t b e u n s a f e t o m a k e t h i s t r i p b y t r a i n P e o p l e w h o s e o p i n i o n I v a l u e w o u l d a p p r o v e o f t h e t r a i n M y f r i e n d s a n d c o w o r k e r s u s u a l l y t a k e t h e t r a i n M y f a m i l y w o u l d a p p r o v e o f m y t a k i n g t h e t r a i n M o s t p e o p l e w h o a r e i m p o r t a n t i n m y l i f e w o u l d t a k e t h e t r a i n I c o u l d e a s i l y t a k e t h e t r a i n f o r t h i s t r i p H o w p o s s i b l e i s i t t o t a k e t h e t r a i n f o r t h i s t r i p H o w e ffi c i e n t t o t a k e t h e t r a i n f o r t h i s t r i p H o w p l e a s a n t t o t a k e t h e t r a i n f o r t h i s t r i p I w o u l d d e f i n i t e l y c o n s i d e r t a k i n g t h e t r a i n f o r t h i s t r i p H o w l i k e l y f o r y o u t o t a k e t h e t r a i n f o r t h i s t r i p Millennial is zero, older is one 0.002 0.015 0.027* 0.079** 0.090** 0.127** 0.107** 0.034** 0.080** 0.014 0.072** 0.025 0.021 0.066** 0.018 0.016 0.058** I enjoy being out and about and observing people 0.067** 0.094** 0.052** 0.044** 0.084** 0.036** 0.069** 0.132** 0.059** 0.127** 0.068** 0.084** 0.092** 0.078** 0.102** 0.121** 0.067** I like a neighborhood where I can walk to a village center 0.092** 0.130** 0.050** 0.022 0.040** 0.003 0.029* 0.121** 0.068** 0.130** 0.088** 0.115** 0.109** 0.127** 0.136** 0.162** 0.093** If everyone works together, we could improve the environment 0.052** 0.113** 0.011 0.032* 0.055** 0.018 0.038** 0.126** 0.048** 0.124** 0.044** 0.074** 0.083** 0.073** 0.091** 0.105** 0.036** Value having a private home with separaon from others 0.003 0.004 0.090** 0.109** 0.098** 0.122** 0.086** 0.007 0.001 0.003 0.015 0.034** 0.028* 0.041** 0.062** 0.046** 0.036** Value living in a community with mix of people and backgrounds 0.085** 0.182** 0.091** 0.096** 0.051* 0.059* 0.095** 0.156** 0.091** 0.159** 0.098** 0.133** 0.135** 0.148** 0.194** 0.174** 0.106** I feel I am less dependent on cars than my parents 0.107** 0.119** 0.092** 0.013 0.030* 0.013 0.034** 0.124** 0.158** 0.113** 0.145** 0.149** 0.093** 0.183** 0.165** 0.162** 0.180** I need to drive a car to get where I need to go 0.062** 0.052** 0.172** 0.076** 0.058** 0.077** 0.058** 0.029* 0.053** 0.028* 0.049** 0.097** 0.044** 0.122** 0.106** 0.108** 0.121** I love the freedom and independence I get from a car 0.043** 0.007 0.134** 0.064** 0.037** 0.057** 0.043** 0.009 0.021 0.008 0.054** 0.030* 0.005 0.076** 0.077** 0.045** 0.087** It is important to me to control the radio and the air condioning 0.01 0.023 0.047** 0.089** 0.090** 0.100** 0.070** 0.055** 0.038** 0.055** 0.039** 0.011 0.030* 0.02 0.009 0.015 0.004 I would prefer to borrow, share, or rent a car just when I need it 0.145** 0.100** 0.042** 0.052** 0.064** 0.037** 0.063** 0.078** 0.137** 0.085** 0.164** 0.069** 0.038** 0.145** 0.109** 0.102** 0.162** I feel really stressed when driving congeson in big cies 0.092** 0.149** 0.014 0.045** 0.041** 0.075** 0.041** 0.094** 0.075** 0.090** 0.064** 0.028* 0.041** 0.060** 0.056** 0.079** 0.061** With driverless cars I would be less likely to travel by rail or bus 0.087** 0.052** 0.074** 0.120** 0.111** 0.106** 0.108** 0.018 0.096** 0.006 0.080** 0.018 0.009 0.039** 0.014 0.021 0.065** On a train or a bus with people I do not know is uncomfortable 0.077** 0.123** 0.185** 0.338** 0.469** 0.331** 0.334** 0.105** 0.01 0.125** 0.012 0.087** 0.089** 0.095** 0.230** 0.169** 0.085** I don't mind traveling with people I do not know 0.145** 0.176** 0.075** 0.121** 0.199** 0.136** 0.113** 0.216** 0.088** 0.211** 0.095** 0.113** 0.128** 0.137** 0.228** 0.199** 0.134** The process of going through airport security is stressful 0.009 0.053** 0.102** 0.105** 0.117** 0.120** 0.089** 0.063** 0.072** 0.054** 0.044** 0.014 0.007 0.02 0.027* 0.002 0.030* Important to me to receive email or text updates about trip 0.063** 0.131** 0.02 0.028* 0.012 0.031* 0.027* 0.159** 0.104** 0.152** 0.083** 0.086** 0.091** 0.083** 0.093** 0.119** 0.082** Using a laptop, tablet, or smartphone is important to me 0.066** 0.131** 0.045** 0.030* 0.031* 0.057** 0.021 0.130** 0.097** 0.124** 0.093** 0.088** 0.103** 0.110** 0.098** 0.123** 0.107** I could deal with the schedules offered by the train 0.170** 0.304** 0.193** 0.093** 0.088** 0.047** 0.084** 0.320** 0.191** 0.331** 0.222** 0.314** 0.258** 0.329** 0.338** 0.391** 0.332** I would need the flexibility of a car at my desnaon 0.103** 0.158** 0.445** 0.170** 0.145** 0.119** 0.143** 0.175** 0.150** 0.189** 0.172** 0.135** 0.111** 0.225** 0.199** 0.231** 0.244** Geng to the train staon is inconvenient 0.058** 0.075** 0.330** 0.124** 0.105** 0.095** 0.115** 0.084** 0.075** 0.091** 0.106** 0.260** 0.152** 0.243** 0.153** 0.180** 0.191** Sharing a car with others for such a trip seems unpleasant 0.037** 0.003 0.115** 0.127** 0.150** 0.137** 0.120** 0.002 0.022 0.004 0.023 0.02 0.01 0.028* 0.060** 0.045** 0.016 Esmate cost of train more than the cost by car 0.018 0.052** 0.111** 0.017 0.01 0.021 0.015 0.022 0.030* 0.043** 0.048** 0.017 0.01 0.052** 0.005 0.009 0.083** Esmate cost of bus more than the cost by car 0.016 0.042** 0.173** 0.150** 0.134** 0.110** 0.150** 0.050** 0.005 0.058** 0.005 0.067** 0.048** 0.086** 0.068** 0.084** 0.063** Esmate cost of train more than the cost by bus 0.042 0.112** 0.025 0.078** 0.023 0.018 0.048 0.086** 0.009 0.118** 0.009 0.091** 0.092** 0.002 0.066** 0.076** 0.014 Worry about personal safety/disturbing behavior – Bus 0.119** 0.023 0.122** 0.321** 0.238** 0.319** 0.229** 0.024 0.044** 0.042** 0.005 0.018 0.011 0.008 0.138** 0.065** 0.01 Worry about personal safety/disturbing behavior – Train 0.007 0.068** 0.159** 0.430** 0.345** 0.377** 0.353** 0.119** 0.024 0.140** 0.021 0.068** 0.067** 0.052** 0.216** 0.152** 0.053** Worry about personal safety/disturbing behavior – Air 0.050** 0 0.086** 0.308** 0.246** 0.275** 0.240** 0.039** 0.072** 0.046** 0.032* 0.011 0.028* 0.027* 0.095** 0.060** 0.015 Worry about personal safety/disturbing behavior – Car 0.135** 0.091** 0.002 0.133** 0.126** 0.105** 0.138** 0.042** 0.111** 0.048** 0.112** 0.027* 0.025 0.104** 0.038** 0.033* 0.117** Concerned about flexibility of schedules – Air 0.038** 0.028* 0.183** 0.114** 0.077** 0.128** 0.075** 0.004 0.018 0.004 0.017 0.063** 0.005 0.057** 0.038** 0.022 0.041** Concerned about flexibility of schedules – Bus 0.038** 0.021 0.251** 0.168** 0.123** 0.156** 0.104** 0.063** 0.062** 0.060** 0.092** 0.104** 0.028* 0.135** 0.120** 0.094** 0.123** Concerned about flexibility of schedules – Train 0.016 0.056** 0.288** 0.166** 0.130** 0.171** 0.111** 0.089** 0.097** 0.095** 0.140** 0.151** 0.065** 0.191** 0.145** 0.133** 0.177** Compared to car, I would be less red and stressed by air 0.288** 0.337** 0.035** 0.058** 0.047** 0.041** 0.046** 0.080** 0.049** 0.075** 0.064** 0.105** 0.073** 0.113** 0.077** 0.106** 0.097** Compared to car, I would be less red and stressed by bus 1 0.480** 0.079** 0.004 0.007 0.018 0.009 0.159** 0.144** 0.142** 0.167** 0.130** 0.086** 0.186** 0.204** 0.177** 0.163** Compared to car, I would be less red and stressed by train 0.480** 1 0.143** 0.094** 0.107** 0.056** 0.111** 0.301** 0.181** 0.304** 0.203** 0.234** 0.211** 0.289** 0.365** 0.359** 0.277** Difficult for me to get from train staon to where I need to go 0.079** 0.143** 1 0.235** 0.214** 0.184** 0.235** 0.160** 0.126** 0.195** 0.173** 0.239** 0.173** 0.282** 0.246** 0.256** 0.268** Worry about crime or unruly behavior at the train staon and train 0.004 0.094** 0.235** 1 0.463** 0.460** 0.531** 0.111** 0.044** 0.142** 0.015 0.090** 0.088** 0.056** 0.221** 0.157** 0.066** I would feel uncomfortable being on the train with strangers 0.007 0.107** 0.214** 0.463** 1 0.366** 0.500** 0.104** 0.037** 0.119** 0.01 0.070** 0.087** 0.058** 0.217** 0.175** 0.059** Having to be with people whose behavior I find unpleasant 0.018 0.056** 0.184** 0.460** 0.366** 1 0.366** 0.051** 0.028* 0.076** 0.028* 0.031* 0.021 0.059** 0.206** 0.119** 0.062** It might be unsafe to make this trip by train 0.009 0.111** 0.235** 0.531** 0.500** 0.366** 1 0.113** 0.066** 0.148** 0.01 0.065** 0.075** 0.045** 0.188** 0.170** 0.039** People whose opinion I value would approve of the train 0.159** 0.301** 0.160** 0.111** 0.104** 0.051** 0.113** 1 0.332** 0.695** 0.305** 0.284** 0.249** 0.310** 0.380** 0.406** 0.305** My friends and coworkers usually take the train 0.144** 0.181** 0.126** 0.044** 0.037** 0.028* 0.066** 0.332** 1 0.294** 0.457** 0.216** 0.153** 0.330** 0.221** 0.272** 0.384** My family would approve of my taking the train 0.142** 0.304** 0.195** 0.142** 0.119** 0.076** 0.148** 0.695** 0.294** 1 0.284** 0.282** 0.270** 0.314** 0.385** 0.436** 0.317** Most people who are important in my life would take the train 0.167** 0.203** 0.173** 0.015 0.01 0.028* 0.01 0.305** 0.457** 0.284** 1 0.227** 0.145** 0.352** 0.286** 0.313** 0.447** I could easily take the train for this trip 0.130** 0.234** 0.239** 0.090** 0.070** 0.031* 0.065** 0.284** 0.216** 0.282** 0.227** 1 0.487** 0.443** 0.371** 0.412** 0.344** How possible is it to take the train for this trip 0.086** 0.211** 0.173** 0.088** 0.087** 0.021 0.075** 0.249** 0.153** 0.270** 0.145** 0.487** 1 0.374** 0.352** 0.350** 0.248** How efficient to take the train for this trip 0.186** 0.289** 0.282** 0.056** 0.058** 0.059** 0.045** 0.310** 0.330** 0.314** 0.352** 0.443** 0.374** 1 0.436** 0.463** 0.525** How pleasant to take the train for this trip 0.204** 0.365** 0.246** 0.221** 0.217** 0.206** 0.188** 0.380** 0.221** 0.385** 0.286** 0.371** 0.352** 0.436** 1 0.589** 0.436** I would definitely consider taking the train for this trip 0.177** 0.359** 0.256** 0.157** 0.175** 0.119** 0.170** 0.406** 0.272** 0.436** 0.313** 0.412** 0.350** 0.463** 0.589** 1 0.520** How likely for you to take the train for this trip 0.163** 0.277** 0.268** 0.066** 0.059** 0.062** 0.039** 0.305** 0.384** 0.317** 0.447** 0.344** 0.248** 0.525** 0.436** 0.520** 1 * Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level (2-tailed).