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

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71 6.1 Introduction In the previous chapter was discussed the creation of a new ICLV model to better understand how demand for rail is influenced both by traditional times and costs and by cultural and psychological factors that are more difficult to quantify. Those trained in model building, and in economic analy- sis, will have no trouble interpreting the implications both from the level of detail in Chapter 5 and from the more complete set of tables presented in ICLV and Hybrid Model Development in NCRRP Web-Only Document 2. However, making that information directly applicable to the analysis of public policy is still a challenge for most transportation practitioners. For this reason, the research team has created a new analysis tool which serves to translate the vast amount of data created in the ICLV, into the form of possible scenarios for the future. Toward this end, the new mode choice models with latent attitudinal variables have been imple- mented in the form of a Microsoft® Excel™ workbook used by the research team analysts in order to provide results for user-defined scenario changes. The implementation uses the sample enumera- tion method. Under this method, the appropriate model parameters are applied for each member of a sample of individual travelers, and the predicted mode shares are expanded and added across the sample to arrive at a total predicted number of trips made using each mode alternative for each travel segment (trip purpose). The resulting predictions can then be compared against a base scenario to determine the change in mode usage that is expected due to the change in the scenario inputs. NCRRP has created a user-friendly version of the scenario testing tool, which will be available for downloading from the TRB website in June 2016 (by searching for “NCRRP Report 4”). 6.1.1 Chapter Structure Following upon this brief introduction, the chapter presents a description of the testing tool created in this NCRRP project. Then, (consistent with the content of Chapter 1) four illustrative scenarios are described, and the implications for rail ridership are discussed. The model is then applied to the question of demographic content of future scenarios, with five separate demo- graphic changes explored. This is followed first with an exploration of changes in traditional times and costs, and second with possible changes in underlying cultural beliefs and attitudes, keyed to levels experienced by existing demographic segments of the population. 6.1.2 What Does the Tool Look Like? The scenario testing tool created for NCRRP 03-02 takes the form of a series of interconnected Excel spreadsheets that make available to the analyst a wide variety of data and procedures needed in the application of quick-turn-around scenario testing. C H A P T E R 6 Model Application for Scenario Analysis

72 Intercity Passenger Rail in the Context of Dynamic Travel Markets The scenario testing tool builds directly on the results calculated in the NCRRP ICLV forecast- ing model. For each of the 5,625 persons analyzed in the stated choice exercise, over 40 separate coefficients are created to reflect mode share implications from various forms of travel times (in-vehicle, access, egress) for four trip purposes. Thus, the scenario spreadsheets provide over 230,000 coefficients as the tool user specifies various combinations of trip characteristics to be studied. Concerning the documentation of attitudes, 25 columns of attitudes stratified by several basic demographic groupings are arrayed against the 5,625 rows, representing respondents, with potentially 140,000 unique combinations of respondents and attitudes. An additional spread- sheet arrays the person rows against some 40 columns of (primarily) socioeconomic variables. The characteristics of networks (including the several forms of travel times) are included both in terms of the hypothetical trips that were displayed to the respondent in the stated choice exercise, and empirically calculated from national network data. Notwithstanding the scale of the information contained in this free-standing Excel program, the entire program is packaged in a 20-megabyte file, allowing it to be run on local computers, with no need for network connections. 6.2 The Four Illustrative Scenarios To help the reader understand the implications of the ICLV model presented in Chapter 5, four future scenarios for the market setting for rail were created in the project. While they were briefly summarized in Chapter 1 (shown again in Table 16), they are now presented here with more description and detail (Table 17). Four combinations of the attitudinal shifts above were run to represent four possible future scenarios. Only the trends with age are varied—the attitudinal differences related to gender, education, and employment are assumed not to change in these scenarios. The most optimistic scenario for rail is that people will keep their current attitudes toward auto orientation and tech- nology as they age (“Go with cohort” in Table 17) and that the next generation will have the same attitudes as current millennials, but that all age groups will adopt the current attitudes toward privacy of the current age 65+ group. The most pessimistic scenario for rail is that each age cohort will adopt the attitudes toward auto orientation and technology that the previous cohort had at that age (“Current trends with age” in Table 17): The next generation, Z, will not reflect the current Decreasing Role of Auto Orientaon in Future Decreasing Concern forPrivacy in Travelin Future Pessimisc for Rail Bad future for auto rejecon Bad future for privacy tolerance ICT need will decrease with age Rail decreases by 4% Mixed Scenario B Good future for auto rejecon Bad future for privacy tolerance ICT need will decrease with age Rail increases by 4% Mixed Scenario A Bad future for auto rejecon Good future for privacy tolerance ICT need will connue with age Rail increases by 10% Opmisc for Rail Good future for auto rejecon Good future for tolerance ICT need will connue with age Rail increases by 18% Table 16. Summary of the results from the four future scenarios (simplified).

Model Application for Scenario Analysis 73 millennials but will reflect the current post-millennial 35–44 age group, and the cohorts will keep their same attitudes toward privacy as they age. The mixed scenarios have different combinations of those assumptions. The results are that the pessimistic scenario results in a 4% drop in total rail trips, while the optimistic scenario results in an 18% increase (Table 18). Mixed Scenarios A and B result in a 10% and 4% increase in rail trips, respectively. The “other” trip-purpose segment, which is the smallest segment, is the most volatile, while the business (work), vacation, and VFR trip purposes all show fairly similar trends across the scenarios. 6.3 Sample Expansion and Initial Model Calibration Because the research team was using only a partial sample of trips in the NEC and Cascade Corridor, it was useful to first check the numbers of trips by mode and corridor against simi- lar numbers from other corridors. Two sources were used: the NEC Intercity Travel Summary Report (RSG 2015a), which has estimates of annual trips by mode and city pair in the NEC, and the output from the national long-distance passenger travel demand model (RSG 2015b), which has synthetic estimates of annual trips by mode, trip purpose, and city pair in both the NEC and Cascade Corridor. Because these two sources use different definitions of geographic areas, and because the survey for this project used fairly vague city area definitions, the numbers are not exactly comparable between any of the estimates. The purpose of the scenarios is not to provide Effect Change in Values Pessimisc for Rail Mixed Scenario A Mixed Scenario B Opmisc for Rail Age on auto orientaon Current trends with age Gen Z same as current 35–44 Current trends with age Gen Z same as current 35–44 Go with cohort Gen Z same as millennials Go with cohort Gen Z same as millennials Age on technology orientaon Current trend with age Go with cohort Current trend with age Go with cohort Age on privacy atude Go with cohort All adopt atude of 65+ Go with cohort All adopt atude of 65+ Gender, employment, educaon on all atudes Current trends Current trends Current trends Current trends Table 17. Definitions of four scenarios, relative to base scenario. Trip Purpose Change in Rail Trips Pessimisc for Rail Mixed Scenario A Mixed Scenario B Opmisc for Rail Business 5% 13% 3% 22% Vacaon 4% 8% 2% 14% Visit Friends/Relaves 2% 9% 4% 15% Other 11% 14% 9% 35% Total 4% 10% 4% 18% Table 18. Change in rail trips under the four scenarios, relative to base scenario.

74 Intercity Passenger Rail in the Context of Dynamic Travel Markets precise forecasts but to begin with a reasonable representation of the current traveling popula- tion, so an approximate overall sample expansion was deemed acceptable. Using an expansion factor of 6,000 annual trips per survey respondent gave a reasonable match against “observed” trips in total and by trip purpose. Compared to the observed data, the initial mode share was skewed a bit toward bus and away from rail, so slight changes were made to the mode constants in the models to better match the data. The resulting initial expanded trips for the “base scenario” (no user-supplied changes) are shown in Table 19. Note that the overall rail and bus mode shares are near 14%, which is higher than the actual share for the NEC in total because the NCRRP 03-02 sample and the resulting models focus on trips with both ends near the major cities (Boston, New York, Philadelphia, Washington, DC), while the entire corridor also contains areas that are relatively distant from those city centers and thus tend to be more car oriented. 6.3.1 Changing the Demographic Distributions One way that the user can define future scenarios is to adjust the population distribution by gender, age group, education level, employment status, and/or income level. Table 20 shows the base scenario distributions that result from the expanded sample of respondents. The user can change the percentages in any of the green-shaded rows, and the percentages in the “base” categories (white unshaded rows) automatically adjust to maintain the total at 100%. In the model calculations, the expansion factors for each respondent are adjusted using the new scenario percentage divided by the base scenario percentage for the relevant category for each demographic variable. (For example, if the gender balance for business trips were adjusted to be 50%/50%, then the expansion factors for business trips made by females would be multiplied by 0.500/0.411, while the expansion factors for business trips made by males would be multiplied by 0.500/0.589.) Table 21 shows the resulting changes in rail trips for five different scenarios changing the demographic distribution of the traveling population: (1) a shift toward more females, (2) a shift toward more senior citizens, (3) a shift toward fewer individuals who are not college graduates, (4) a shift toward fewer unemployed adults, and (5) a shift toward the extremes of the income range and away from the center. The shifts are all fairly modest, at 10% of the initial population share, and the resulting changes in rail trips in Table 21 are also modest, all below 1.5% change in total trips. (The scenarios assume no changes in attitudes within each demographic category.) Mode Base Scenario Business Vacaon VFR Other Total Predicted Trips (1,000/year) Car 6,114 19,891 19,765 7,641 53,411 Bus 1,812 6,130 2,900 697 11,539 Rail 3,290 4,260 3,551 1,189 12,290 Air 1,887 2,341 2,213 517 6,958 Total 13,104 32,622 28,428 10,044 84,198 Mode Share Car 46.7% 61.0% 69.5% 76.1% 63.4% Bus 13.8% 18.8% 10.2% 6.9% 13.7% Rail 25.1% 13.1% 12.5% 11.8% 14.6% Air 14.4% 7.2% 7.8% 5.2% 8.3% Table 19. Base scenario total trips by purpose and mode.

Model Application for Scenario Analysis 75 Demographics Base Scenario Business Vacaon VFR Other GENDER Female 41.1% 57.1% 55.4% 56.4% Male (base) 58.9% 42.9% 44.6% 43.6% Total 100.0% 100.0% 100.0% 100.0% AGE Under 35 20.7% 24.1% 21.3% 12.6% 35 44 (base) 22.0% 19.9% 16.4% 12.9% 45 54 24.0% 18.5% 17.2% 24.7% 55 64 21.7% 18.8% 21.7% 20.8% 65 and older 11.6% 18.7% 23.4% 29.0% Total 100.0% 100.0% 100.0% 100.0% EDUCATION Not a college graduate 22.8% 33.9% 28.1% 35.8% College graduate (base) 77.2% 66.1% 71.9% 64.2% Total 100.0% 100.0% 100.0% 100.0% EMPLOYMENT Not employed 14.5% 32.0% 34.2% 41.9% Employed (base) 85.5% 68.0% 65.8% 58.1% Total 100.0% 100.0% 100.0% 100.0% INCOME Under $25,000 5.6% 6.3% 6.9% 8.9% $25,000 to $49,999 10.4% 15.3% 14.0% 12.9% $50,000 to $74,999 12.3% 16.9% 16.2% 15.3% $75,000 to $99,999 19.0% 20.5% 20.5% 21.3% $100,000 to $149,999 (base) 23.7% 24.3% 22.3% 21.6% $150,000 to $199,999 12.7% 9.0% 9.3% 10.3% $200,000 and up 16.3% 7.7% 10.8% 9.7% Total 100.0% 100.0% 100.0% 100.0% Table 20. Base scenario demographic distributions. Demographic Shi Change in Rail Trips Business Vaca on VFR Other Total Female share up 10%, male share down to compensate 0.6% 1.2% 0.5% 2.2% 0.9% Age over 65 share up 10%, under 35 down 5% 0.1% 1.1% 0.1% 0.9% 0.1% Not college grad share down 10%, college grad share up to compensate 1.4% 1.0% 1.5% 1.9% 1.4% Unemployed share down 10%, employed share up to compensate 0.4% 0.6% 0.5% 1.5% 0.3% Income shares below $50k up 10%, income shares above $150k up 10%, incomes $50–100k down 10% 0.4% 0.5% 0.4% 0.7% 0.1% Table 21. Change in rail trips by purpose for selected changes in demographic distributions.

76 Intercity Passenger Rail in the Context of Dynamic Travel Markets 6.3.2 Changing Mode Travel Times and Costs Table 22 shows the travel times and costs that can be adjusted by the user for specific scenarios. The changes are quite general and are applied in the same percentage to each person in the sample (with the exception of cost changes, which can be specified separately for business trips and non- business trips to represent changes in business or economy fares separately). The base scenario values are given an index of 100, so entering a value of 110 would increase the value by 10% for every person, while entering a value of 90 would decrease the value by 10% for every person. Tests were done increasing each of the values in Table 23 by 10% (entering an index of 110, one cell at a time). The largest influence was seen from increasing the rail non-business fare by 10%; a change in number of trips of -8.2% resulted (a direct fare elasticity of -0.82). The second largest change was from increasing rail IVT, which caused a direct travel time elasticity of -0.64. The cross-mode effect for a 10% increase in car IVT was the third largest with an increase of 5.5% in rail trips (a cross-elasticity of 0.55). In general, the cross-elasticities for changes in car times and costs are larger than for the other modes because of the larger base shares for car. 6.3.3 Changing Attitudinal Variables The spreadsheet also allows the user to simulate predefined shifts in the four latent attitudinal variables, where one or more groups defined by age, gender, education, and/or employment status take on the attitudes of another group along that demographic dimension. Such a shift is simulated by entering a value greater than 0 in the cell for any of the four attitudes, as shown in Table 24. The results of the attitudinal change for each of the cells in Table 24, in terms of total rail trips, are shown in Table 25. The final column also shows the result if all four attitudinal variables are shifted at once. The age variables tend to have the largest effect. If all age groups were to adopt the current Times and Costs Base Scenario Indices Car Bus Rail Air Main mode travel me 100 100 100 100 Access travel me 100 100 100 Egress travel me 100 100 100 Service frequency 100 100 100 Business trip cost 100 100 100 100 Non business trip cost 100 100 100 100 Table 22. Base indices for user-adjustable travel times and costs. Times and Costs Change in Total Rail Trips Car Bus Rail Air Main mode travel me 5.5% 2.7% 6.4% 0.3% Access travel me 0.5% 2.8% 0.8% Egress travel me 0.3% 1.4% 0.4% Service frequency 0.4% 0.0% 0.2% Business trip cost 0.7% 0.2% 2.0% 0.4% Non business trip cost 3.1% 0.8% 8.2% 1.7% Table 23. Change in total rail trips when increasing each time and cost variable by 10%.

Model Application for Scenario Analysis 77 Shis in Atude Privacy Auto Oriented Urbanism Technology All ages to under 35 0 0 0 0 Under 35 to 35 44 0 0 0 0 All ages to over 65 0 0 0 0 All ages one group younger 0 0 0 0 Female to male 0 0 0 0 Male to female 0 0 0 0 No college to college 0 0 0 0 College to no college 0 0 0 0 Unemployed to employed 0 0 0 0 Employed to unemployed 0 0 0 0 Table 24. User-adjustable predefined shifts in attitudes. Shis in Atude Change in Total Rail Trips Privacy AutoOriented Urbanism Technology All at Once All ages to under 35 3.4% 17.9% 0.0% 2.5% 16.4% Under 35 to 35 44 0.0% 1.7% 0.0% 0.0% 1.7% All ages to over 65 10.4% 11.9% 0.0% 3.4% 5.7% All ages one group younger 2.5% 6.1% 0.0% 1.4% 4.9% Female to male 0.4% 2.3% 0.3% 0.4% 1.2% Male to female 0.4% 1.8% 0.2% 0.3% 1.0% No college to college 2.7% 1.2% 0.1% 0.1% 4.2% College to no college 7.5% 3.6% 0.2% 0.3% 11.4% Unemployed to employed 1.3% 0.6% 0.0% 0.2% 0.9% Employed to unemployed 2.5% 1.2% 0.0% 0.4% 1.7% Table 25. Change in total rail trips when shifting attitudes, one at a time and all at once. attitudes of the “millennials” (under 35), then rail trips would increase by 16%, with the largest effect from a shift away from the “car-oriented” attitude, plus a positive effect from the technology aspect of being able to use devices in the train, but an offsetting negative effect from the relative lack of privacy in the train. On the other hand, if all age groups were to adopt the attitudinal tendencies of the current age 65+ group, rail trips would decrease by almost 6%, due to opposite offsetting effects as described for the shift to “younger” attitudes. The most realistic future trend in attitudes by age may be the “shift all ages one group younger.” This would be the situation 10 to 15 years from now if attitudes for any given person stay the same and go together with the age cohorts, rather than changing with age. The result for that test is a 5% increase in total rail trips. Other than age, the largest effect would be if all persons adopted the “no college education” attitude, regardless of education level. This would result in an 11% decrease in rail trips, but seems like a very unlikely scenario.