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89 CHAPTER 9. AN ATTTITUDE-BASED MODEL OF AIR VS. AUTO 9(A) INTRODUCTION Our work plan called for the creation of three separate mathematical models to aid in the understanding of the choice of mode between the auto and the airplane in the long-distance trip. The first model is designed to emphasize the importance of âsoftâ variables, including values, preferences and attitudes in the selection of modes for the long-distance trip; by design, this model does not emphasize the trip-based times and costs. The second model is designed to emphasize the immediately relevant factors of travel time and costs, in addition to other traditional variables such as travel party size: by design, this model does not emphasize the âsofterâ factors including values, preferences and attitudes. The third model is designed to integrate all relevant factors into the prediction of long-distance travel mode. 9(B) STRUCTURE Technical Appendix Chapter 9 presents the new Structural Equations Model created in this project. Appendix Chapter 10 presents the new Multinomial Logit Model. Appendix Chapter 11 then presents the Hybrid Choice model prepared to be integrated in the larger nationwide model of long-distance trip-making in the United States, and to support the scenario testing process mandated in the Amplified Work Program. 9(C) THE STRUCTURAL EQUATIONS MODEL INCORPORATING VALUES, PREFERENCES, AND ATTITUDES DEFINING THE MODEL The Structural Equations Model of Long-Distance Mode Choice has four basic elements. It is hypothesized that some values which impact transportation behavior are long-term in nature and may influence the location of the traveler; they may influence the shorter-term attitudes and may directly influence the choice of long-distance mode. For example, some basic attitudes toward the ownership of a car might influence oneâs location decision, might influence short-term attitudes toward the specific trip, and influence the choice or rejection of the auto for the trip. Location is the second element of the SEM model. Higher levels of âurbanityâ of a location often imply that a major airport is close by; the âruralnessâ of a location is correlated with longer driving distance to a sizeable airport. In the third element of the model, shorter-term attitudes are formed at a time closer to making the modal decision. For a given trip, the idea of finding satisfactory en-route lodging for the multiday auto trip might be unpleasant for some, while others might not be bothered by it. Some people might feel that âpeople-like-themâ would always choose the air, while others feel the opposite. The fourth element of the model is the modal choice itself. Figure 9-1 presents a basic diagram of how the three explanatory elements are hypothesized to relate to the outcome factor, âPropensity to Choose Car Trip.â
90 FIGURE 9-1: CONCEPTUAL DIAGRAM OF STRUCTURAL EQUATIONS MODEL THE LATENT FACTORS DEVELOPED IN THE MODEL Using the application of factor analysis and the published literature in this area, four latent factors were developed to represent preferences on issues with a longer-term time frame than those concerning the evaluation of a transit trip and its attributes, shown in Figure 9-2. The three categories here have been developed and applied in other CRP studies including NCRRP Report 4: Intercity Passenger Rail in the Context of Dynamic Travel Markets.11 11 RSG, M. Coogan, AECOM, I. Ajzen, C. Bhat, B. Lee, M. Ryerson, and J. Schwieterman. 2016. NCRRP Report 4: Intercity Passenger Rail in the Context of Dynamic Travel Markets. Transportation Research Board, Washington, DC
91 FIGURE 9-2: RELATIONSHIP OF OBSERVED VARIABLES (RECTANGLES) TO LATENT FACTORS (OVALS) Longer-Term Values Values Urbanism. A latent factor representing preferences for the attributes of âvaluing urbanismâ in neighborhoods was created with the use of three questions about the importance of walking to a commercial district, being outside with people, and having a mix of people from different backgrounds. Auto Orientation. A latent auto factor was created representing hedonic considerations (e.g., love for the auto); an observed variable representing the desire to control oneâs own space in the car; and the statement that he/she needs a car to get where he/she needs to go. Values Information Technology. Finally, the importance of being productive, and staying connected all day was explored in one latent factor based on the two related attitudes, and the level of ownership of devices. Location A latent factor was created with the use of two observed variables, one reflecting the density of intersections (âdesignâ), one about the distance to the preferred airport.
92 FIGURE 9-3: SEVEN SHORTER-TERM ATTITUDES ABOUT THE TRIP AND THE MODES Shorter-Term Attitudes about the Trip Seven latent factors were created in the model to reflect shorter-term attitudes and trip conditions. A total of 19 variables were used in development of the seven latent factors. Cost. A latent factor dealing with the perceived additional cost of the air trip was created with three observed variables. The first sought agreement with the estimate that this trip would be more expensive by plane. The second two were based on a) the actual airfare per mile, and 2) the airfare per hour of driving. Stress due to air travel. Reported attitudes were used for seats on planes being too close, dealing with crowds at the airport, and stress from the airport security procedures, to create the latent factor. Stress due to driving. A latent factor was created from two concerns about roadway congestion. Safety and disturbing behavior was reflected in a latent factor, based on three survey questions, each of which was scaled so that higher values represent higher levels of concern with air travel. In addition, three more factors were created, which were influenced by (but do not exactly operationalize) the Theory of Planned Behavior, a theory commonly used in the adaptation of principles of social psychology to the subject area of transportation. Affective attitudes toward trip. The first represents the concept of whether this behavior (e.g. choosing the car) is in the interest of the participant and is seen as a pleasant experience. These
93 attitudinal questions are similar to how âAttitude Toward the Behaviorâ in the Theory of Planned Behavior is usually operationalized. This Latent Factor was created from three observed variables, two of which concerned the unpleasantness of the drive, and one about the level of uncertainly associated with it. Social support. The next latent factor was created to help explore the influence of normative factors, that is, to what extent important others would support the participantâs modal decision, and whether they travel by that mode. In the Theory of Planned Behavior this factor is called Subjective Norm (and sometimes, Social Norm). Control â perceived inconvenience of the trip. The final latent factor was created to reflect the concept that choosing to travel by air might be difficult or even impossible, that is, something that one would not have the control to do. For this latent factor, three observed variables were used which reflect the perceived difficulty or impossibility of going by air, including âthe airlines just do not go where I need to go.â Needing a car at the destination, and difficulty of getting to the airport are also included in the creation of the latent factor. In the Theory of Planned Behavior, the concept that one may not be able to carry out the behavior is called âPerceived Behavioral Control.â The Outcome Factor: Choice of Mode A latent factor was developed to reflect âThe Propensity to Choose Car Tripâ as shown in Figure 9-1, which was based on a combination of mode selection for the reference trip, for the short- distance trip, and for a summary of all trips mentioned by the survey participant. RUNNING THE MODEL The model was run as a SEM, using AMOS Version 22 software, which is part of the SPSS set of modeling software packages. The sample included 4,232 respondents. The final model shows the relationships between and among the 12 latent factors of the model, based on the application of the 33x observed variables portrayed as rectangles in the three figures above. The Long- Distance Mode Choice SEM which results has positive overall evaluative characteristics. The model has a Root Mean Square Error of Approximation of 0.04, where any value below 0.05 is considered a good fit. It has a comparative fit index and a Tucker Lewis Index of 0.95 and 0.93, where values above 0.90 are considered to reflect a good model fit. The model has 33 coefficients, whose unstandardized and standardized coefficients and p values are presented in Table 9-2. All the coefficients in the model are significant with p values of less than or equal to 0.01, except one, which is under 0.02. INTERPRETING THE RESULTS OF THE LONG-DISTANCE MODE CHOICE SEM The model is not primarily designed to predict behavior, but to contribute to understanding of the relationship between and among factors, given the relationships hypothesized in Figure 9-1. SEM models allow the ability to look at the combination of direct and indirect effects of one factor upon another, called the âStandardized Total Effectâ (STE). For example, to explain the meaning of the bottom left cell in Table 9-1, the AMOS software program states:
94 âThe standardized total (direct and indirect) effect of Auto Orientation on Car Chosen is - .449. That is, due to both direct (unmediated) and indirect (mediated) effects of Auto Orientation on Car Chosen, when Auto Orientation goes up by 1 standard deviation, Car Chosen goes down by 0.449 standard deviations.â Table 9-1 shows the total effect of each column factor in each row factor, expressed as the STE. Focusing on the effects of various factors on the modelâs key outcome, choice of mode, it becomes clear that the factor concerning the issue of the comparative âexpenseâ of the two modes has the highest STE (at .72). The results of the bottom line in Table 9-1 are expressed graphically in Figure 9-4 in vertical bar chart format. TABLE 9-1: STANDARDIZED TOTAL EFFECT, IMPACT OF COLUMNS ON ROWS AutoÂ OrientationÂ Urbanism ICT Location Expensive AirÂ TripÂ StressfulÂ CarÂ TripÂ StressfulÂ Crime AttitudeâÂ UnpleasantÂ SocialÂ SupportÂ Inconven ient Location 0.37 â0.31 â0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 AirÂ TripÂ Stressful 0.39 â0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 TripÂ Expensive 0.60 0.14 â0.21 0.10 0.00 0.21 0.00 0.00 0.00 0.00 0.00 Crime/DisturbingÂ BehaviorÂ 0.42 0.03 â0.07 0.03 0.34 0.62 0.00 0.00 0.00 0.00 0.00 CarÂ TripÂ StressfulÂ â0.06 0.06 0.40 â0.01 â0.09 â0.02 0.00 0.00 0.00 0.00 0.00 SocialÂ Support â0.14 0.18 0.26 â0.07 â0.70 â0.18 0.30 0.15 0.00 0.00 0.00 AttitudeâÂ Unpleasant â0.21 0.09 0.31 â0.02 â0.15 â0.10 0.73 0.00 0.00 0.00 0.00 ControlÂ âÂ Inconvenient 0.60 0.06 â0.20 0.14 0.66 0.43 0.00 0.30 0.00 0.00 0.00 OutocmeÂ âÂ CarÂ ChosenÂ 0.45 0.02 â0.26 0.08 0.72 0.22 â0.24 0.02 â0.26 â0.17 0.15
95 FIGURE 9-4: TOTAL EFFECT ON MODE CHOICE, RANK ORDERED BY THEIR ABSOLUTE VALUES Interpreting the SEM format The data in Table 9-1 can be interpreted in several ways. While the STE is usually expressed in the scale of a 100% increase in the independent factor, a more realistic interpretation can be stated in terms of a 10% increase in the independent factor. For example, the table shows that: ï· A 10% increase in the value of the factor âAuto Orientationâ would be associated with a 4.5% increase in the Outcome Factor, âCar Chosen.â ï· A 10% increase in the value of the factor â(Car) Attitude Unpleasantâ would be associated with a 2.6% decrease in the Outcome Factor, âCar Chosen.â ï· A 10% increase in the value of the factor âAir Trip Stressfulâ would be associated with 2.2% increase in the Outcome Factor, âCar Chosen.â Comparing the role of the factors While the dominance of expense of the modes (expressed in the question as the expense of air) is totally expected, the role of other factors is noteworthy. The long-term values held by the traveler concerning his/her love of and need for cars emerges as the second strongest predictor of mode choice. Beyond the concern for costs, attitudes about the car seem to be more powerful explainers of the outcome than are attitudes about the air tripâwith measurement of trips being unpleasant and stressful given higher ranking than equivalent questions about the air experience. In our interpretation, the SEM model shows that the dominant characteristic of air travel is its cost â attributes about stress at the airport, or poor coverage of destinations (âinconvenientâ) simply did not emerge as explanatory factors the way that price does; an increase in air price is strongly associated with an increase in auto mode share. Figure 9-5âs horizontal bar chart format (same data as above) helps to reveal the positive and negative characteristics of the STE data. It 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 St an da rd ize dÂ To ta lÂ E ffe ct Â
96 shows that attitudes toward the auto are more nuanced: survey responses about oneâs love for the freedom and independence from auto ownership emerge as strong factors in the explanation of variance in mode share. At the same time, statements that long trips are unpleasant are part of a factor with strong negative explanatory power. Attitudes toward the auto seems therefore to be ambivalent, simultaneously receiving both positive and negative evaluations. FIGURE 9-5: STANDARDIZED TOTAL EFFECT, EXPRESSED AS POSITIVE AND NEGATIVE VALUES â0.4 â0.2 0 0.2 0.4 0.6 0.8 AirÂ Expensive AutoÂ Orientation CarÂ Unpleasant LikesÂ ICT CarÂ TripÂ Stressful AirÂ TripÂ Stressful SocialÂ SupportÂ forÂ Air AirÂ Inconvenient LocationÂ moreÂ rural CrimeÂ onÂ airÂ trip ProÂ Urbanism ExpressedÂ asÂ StandarizedÂ TotalÂ Effect
97 TABLE 9-2: UNSTANDARDIZED AND STANDARDIZED COEFFICIENTS IN THE SEM StandardizedÂ Estimate S.E. P Estimate AirÂ TripÂ Stressful <âââ AutoÂ Orientation 0.493 0.035 *** 0.389 Location <âââ AutoÂ Orientation 0.088 0.009 *** 0.364 Location <âââ Urbanism â0.079 0.009 *** â0.303 Location <âââ ICT â0.018 0.007 0.008 â0.096 Expensive <âââ AutoÂ Orientation 0.586 0.052 *** 0.495 Expensive <âââ AirÂ TripÂ Stressful 0.197 0.025 *** 0.211 Expensive <âââ Urbanism 0.219 0.038 *** 0.17 Expensive <âââ ICT â0.204 0.029 *** â0.219 Expensive <âââ Location 0.463 0.156 0.003 0.094 CarÂ Stressul <âââ Expensive â0.11 0.028 *** â0.094 CarÂ Stressul <âââ ICT 0.413 0.03 *** 0.378 Crime/Disturbing <âââ AirÂ TripÂ Stressful 0.766 0.029 *** 0.549 Crime/Disturbing <âââ Expensive 0.504 0.034 *** 0.337 CarÂ Stressul <âââ Urbanism 0.12 0.039 0.002 0.08 Inconvenient <âââ Expensive 0.686 0.044 *** 0.591 Inconvenient <âââ AirÂ TripÂ Stressful 0.15 0.028 *** 0.138 AttitudeÂ âÂ Unpleasant <âââ AutoÂ Orientation â0.182 0.052 *** â0.094 AttitudeÂ âÂ Unpleasant <âââ AirÂ TripÂ Stressful â0.111 0.035 0.002 â0.073 Inconvenient <âââ Location 0.525 0.125 *** 0.092 AttitudeÂ âÂ Unpleasant <âââ Expensive â0.131 0.044 0.003 â0.08 AttitudeÂ âÂ Unpleasant <âââ CarÂ Stressul 1.011 0.042 *** 0.725 SocialÂ Support <âââ Crime/Disturbing 0.15 0.033 *** 0.149 SocialÂ Support <âââ Expensive â1.103 0.072 *** â0.732 SocialÂ Support <âââ AutoÂ Orientation 0.529 0.06 *** 0.297 AttitudeÂ âÂ Unpleasant <âââ Urbanism 0.117 0.042 0.005 0.056 SocialÂ Support <âââ CarÂ Stressul 0.382 0.032 *** 0.296 SocialÂ Support <âââ Urbanism 0.477 0.044 *** 0.246 SocialÂ Support <âââ AirÂ TripÂ Stressful â0.152 0.045 *** â0.108 Inconvenient <âââ Crime/Disturbing 0.224 0.025 *** 0.288 CarÂ Chosen <âââ Expensive 0.214 0.027 *** 0.494 CarÂ Chosen <âââ SocialÂ Support â0.049 0.007 *** â0.169 CarÂ Chosen <âââ AttitudeÂ âÂ Unpleasan â0.069 0.005 *** â0.258 CarÂ Chosen <âââ Inconvenient 0.047 0.019 0.015 0.125 Unstandardized