BENEFIT AND SPATIAL TRANSFERABILITY OF LIFESTYLE MODELS FOR HOME-BASED WORK TRIPS CASE STUDIES OF THREE FLORIDA URBAN AREAS

Transcription

1 Zhao et al. BENEFIT AND SPATIAL TRANSFERABILITY OF LIFESTYLE MODELS FOR HOMEBASED WORK TRIPS CASE STUDIES OF THREE FLORIDA URBAN AREAS By Fang Zhao, Department of Civil and Environmental Engineering, Florida International University, Miami, FL 3399, (35) 34838, (35) 3488 (fax), LeeFang Chow, Department of Civil and Environmental Engineering, Florida International University, Miami, FL 3399, (35) , (35) 3488 (fax), MinTang Li, Department of Civil and Environmental Engineering, Florida International University, Miami, FL 3399, (35) , (35) 3488 (fax), Albert Gan, Department of Civil and Environmental Engineering, Florida International University, Miami, FL 3399, (35) 34836, (35) 3488 (fax), Imran Ghani, Florida Department of Transportation, District,, FL, (94) , (94) (fax), Words = 3,48 5 x Tables 5 x Figures = 6,98 Keywords: trip generation, lifestyle models, spatial transferability ABSTRACT Lifestyle models have been used to estimate trip generation rates based on lifestyle variables such as presence or number of workers/retirees or children in a household (HH) instead of the traditional variable set such as HH size and auto ownership. The benefits of lifestyle models have been inconclusive and different conclusions have been drawn by various studies. In this paper, the applications of two lifestyle models, which have been used in two major urban regions in Florida, to three Florida urban areas for homebased work (HBW) trips are described. The results showed that lifestyle models were beneficial to all areas, but the degree varies depending on the demographics of the population, mainly the retired population. The spatial transferability of trip generation rates was also studied, and the results suggested that trip rates were closer for areas that share similarities in demographics than those areas that have significantly different demographic characteristics. TRB 3 Annual Meeting CDROM

2 Zhao et al. INTRODUCTION Lifestyle variables may include numbers of adults, workers, retirees, children, seasonal residents, etc., in a household (HH). The usefulness of lifestyle variables in travel demand forecasting has been examined since the 96s. However, lifestyle variables have not been included in most travel demand studies, which usually forecast trips based on traditional variables such as HH size, auto ownership, and income level (). It is well known that models that do not consider lifestyle variables can produce unrealistic trip forecasts. For example, a model that crossclassifies trips by only HH size will predict the same number of work trips for a HH of two retirees and a HH of two workers, everything else being equal. Application of such a model to a zone where most HHs are retired or seasonal HHs, for example, will grossly overestimate the number of work trips for the zone. In this case, it is clear that the number of workers will be a better trip predictor. A number of urban areas in the U.S. have developed models that incorporate lifestyle variables. For example, San Francisco uses a model that crossclassifies work trips by number of workers and school trips by youthful age cohorts (). The Reading, Pennsylvania model crossclassifies trips by three types of HHs: retired, nonretired with children, and nonretired without children (3). Phoenix, Arizona uses the retirement status of an entire traffic zone as a stratifying variable. A zone is classified as a retirement zone if 5% or more of its population are retired (4). Portland, Oregon uses the age of the head of the HH and number of children for work and school trips (5). Several major urban regions in Florida have recently updated their trip production models to include lifestyle variables. These include the Tampa Bay Regional Planning Model, which crossclassifies trips by retired HHs, nonretired HHs with children, and nonretired HHs without children (6); the Southeast Florida Regional Planning Model (SERPM), which crossclassifies work trips by number of workers, auto ownership, and presence of children, and nonwork trips by auto ownership, presence of children, and HH size (7); and the Treasure Coast Regional Planning Model, which uses a similar crossclassification as that of SERPM. Lifestyle variables are not currently considered in the standard travel demand models for Florida urban areas, also known as the Florida Standard Urban Transportation Model Structure (FSUTMS) (8). As part of a statewide effort, the Florida Department of Transportation (FDOT) is sponsoring a research project to evaluate the needs for other Florida urban areas to adopt lifestyle models and the feasibility of developing a set of standard lifestyle trip production rates. As part of the research, a survey of the metropolitan planning organizations (MPOs) in Florida has been completed to identify the stateofthepractice in Florida in predicting trip productions, including data collection practice, socioeconomic data estimation and forecast methods, special characteristics of an area that affect trip productions, problems with their trip production models, and MPOs willingness to adopt lifestyle models. The survey results indicated that some of the MPOs were not interested in lifestyle models because they believed that the characteristics of their urban areas would not lend well to lifestyle models. Such beliefs lead to an important question: under what conditions will lifestyle models superior to the traditional models? TRB 3 Annual Meeting CDROM

3 Zhao et al. 3 There have been studies that evaluate lifestyle models in terms of their capability to improve the prediction of trip production. The lifestyle variables have been used successfully to discriminate between market segments when modeling the choice of mode and destination for shopping trips (9), and they have been shown to improve trip production forecasting through the inclusion of measures of HH structure and residential location in existing travel forecasting procedures (). Simonsen and Neveu () further compared the predictive capability between the traditional and lifestyle variables in trip generation analysis. The results indicated that the lifestyle classification scheme did not estimate trip production rates better than the traditional family size classification scheme. Chicoine and Boyle () also compared lifestyle models with HHsizebased models, and the results showed no evidence of improvement by using either of the schemes. However, when the predicted and the actual trip productions were compared, the lifestyle trip production procedure produced more accurate results. This paper summarizes a research effort to answer the following questions concerning homebased work (HBW) trips: () whether lifestyle models are better than the standard models; () what types of urban areas will benefit from lifestyle models; and (3) whether lifestyle models are transferable to other urban areas. Procedure and results of analyses of HH travel survey data from several Florida urban areas are described. The analyses involved calibrating trip production rates using two Florida lifestyle models and evaluating the performance of the lifestyle models. FLORIDA LIFESTYLE MODELS Three Florida urban regions have implemented lifestyle trip production models: Southeast Florida (MiamiDade, Broward, and Palm Beach counties), Treasure Coast (Indian River, St. Lucie, and Martin counties), and Tampa Bay (Hillsborough, Pinellas, Hernando, and Pasco Counties). The first two regional models share the same structure, which will be referred to as the Southeast Florida model thereafter. These models are briefly described below. Southeast Florida Lifestyle Trip Production Model Structure Two separate sets of variables are used in the lifestyle trip production crossclassification model in Southeast Florida. For HBW trips, the variables are number of workers, presence of children in HHs, and HH auto ownership. For other homebased trip purposes, HH size, presence of children, and auto ownership are used. Table illustrates the model structures for HBW trips. TABLE Southeast Florida Trip Production Model Structure for HBW Trips Presence of Children Auto Number of Workers In HH Without Children With Children 3 3 TRB 3 Annual Meeting CDROM

4 Zhao et al. 4 Tampa Bay Regional Model In 99, an extensive home interview origindestination survey was conducted in the Tampa Bay Region to identify factors influencing travel behavior (). Significant variations were found in trip making patterns among three major HH categories: HHs without children, HHs with children, and HHs with retirees. In 996 and 999, additional surveys targeting underrepresented HHs and seasonal HHs were undertaken. Table gives the Tampa Bay Region model structure. Retired HHs are defined as HHs with at least one retired HH member and no fulltime employed HH members; working HHs are HHs other than retired HHs; and seasonal HHs are those whose members live in the region more than one month but less than six months per year. TABLE Auto Ownership 3 Tampa Bay Region Trip Production Model Structure Retired Working HH with Working HH with HH No Children Children Seasonal HH Florida Standard Urban Transportation Model Structure (FSUTMS) In the standard FSUTMS trip production models, HHs are stratified by dwelling unit type, HH size, and auto ownership to account for variation in HH trip production rates. Table 3 gives the FSUTMS model structure. TABLE 3 FSUTMS Trip Production Model Structure for All Trip Purposes Auto Number of Persons in HH Dwelling Type Ownership Single Family MultiFamily STUDY URBAN AREAS Based on the availability of HH travel survey data, three MPOs/MSA were included in this study: Lee County MPO, Volusia County MPO, and MSA. Lee County is located on the west coast of Florida and south of Tampa Bay, which has a significant seasonal and retired population. Volusia County also has seasonal and retired populations that are above the state average. MSA is made up by four counties and has a significantly smaller population of seasonal and retired residents than the state average. MSA Metropolitan Statistical Area. TRB 3 Annual Meeting CDROM

5 Zhao et al. 5 CALIBRATION AND EVALUATION OF TRIP PRODUCTION MODELS Different methods, namely Multiple Classification Analysis (MCA) and adjusted MCA, were applied for trip rate calibration. The method with better performance was selected and the final results from the lifestyle models and FSUTMS standard model were compared. The comparison was made between the regionwide and survey districtwide HBW trips expanded from survey and the estimated HBW trips from the lifestyle models and the standard FSUTMS model. The following sections describe the procedures of the analysis in detail. Regionwide Mean Trip Rates Lee County MPO, Volusia County MPO, and MSA were divided into five, six and eight districts, respectively, for HH survey purpose. In this study, the HH samples were grouped into these survey districts according to their locations and the average HBW trip rates for each survey district as well as the entire MPO/MSA were calculated. When the difference between the average trip rate for a given district and the MPO/MSAwide mean trip rate was greater than percent, a KruskalWallis (KW) test was performed to examine if the trip rates sampled from different regions in a given urban area were significantly different. The results showed there was no significant difference in trip rates from survey districts in MSA and Lee County MPO. However, for Volusia County MPO, the mean trip rates were different between the costal area and the inland area. Therefore, two sets of HBW trip rates were calibrated for Volusia County MPO. In addition, District 4 in Volusia County MPO was combined with its adjacent district, i.e., District 5, since only HHs were sampled. Survey District and RegionWide Total HBW Trips For evaluation purposes, total HBW trips expanded from the survey were necessary to provide a benchmark against which different models can be compared. Trips expanded from the survey data were obtained simply by multiplying the total number of HHs by the average HBW trip rate in a given district. However, including HHs without workers in the calculation would result in underestimated HBW trip rate. Therefore, the expanded trips were obtained by multiplying the average HBW trip rate by the total number of HHs with workers in a given district. They were interpreted as the expected districtwide HBW trips and were used for comparison purpose. The 99 census special tabulate product (STP) 66 data were employed to estimate the total HBW trips in each district of the three urban regions since the STP data have not been released, yet. The trip expansion results are presented in Table 4. Calibration and Application of Trip Rates The procedure of applying lifestyle and FSUTMS models to the three MPOs/MSA involved two basic steps: () Calibration of trip production rates using HH survey data: (a) For each of the two lifestyle model structures and the FSUTMS model, determine the number of trips and the number of HHs from the survey samples for each crossclassification cell. TRB 3 Annual Meeting CDROM

6 Zhao et al. 6 (b) (c) Calibrate trip production rates using both MCA and adjusted MCA. Check for reasonableness of the trip rates for any possible adjustments. () Calculation of zonal HBW trips based on lifestyle and standard FSTUMS models trip rates. This was accomplished by multiplying the trip rates for each cell and the number of HHs for that cell. The HH information came from the 99 STP 66 data, which provided the number of different types of HH as classified in the Southeast Florida and Tampa Bay lifestyle models and standard FSUTMS model. TABLE 4 Statistics for Survey Districts for Three Urban Regions MPO/MSA District Total Sample Sampled Average HBW Sample Expanded HHs * HHs HBW Trips Trip Rates Variance Trips 6, ,8 4, ,75 Lee County 3 7, ,64 4 6, ,5 5 9, , Volusia County MSA Total 85, ,8 48, ,7, ,363 3, ,89 4&5, ,57 6 9, ,84 Total 9, ,36 4, ,48, , , ,93 4, ,63 5 9, ,39 6 8, ,68 7, ,9 8 6, ,7 Total 67,66,667 3,58 57,84 * Number of HHs with workers from 99 Census The estimated HBW trips based on the tip rates calibrated by MCA and adjusted MCA approaches for the lifestyle models and the FSUTMS model were compared with the expected district total. Trip rates calibrated by either MCA or adjusted MCA were selected for each model based on performance. For example, for Southeast Florida model, trip rates calibrated by MCA approach were selected because it provided closer estimation to the expected district total than the trips rates calibrated by adjusted MCA method. The trip rates are given in Tables 5 through 7 for the two lifestyle models and the FSUTMS model. TRB 3 Annual Meeting CDROM

7 Zhao et al. 7 TABLE 5 Lifestyle HBW Trip Rates Based on Southeast Florida Model Structure Workers MPO/MSA Presence of Children Auto.9.57 Without Children Lee County Volusia County (District &) Volusia County (Dsitrict 3,4,5,6) With Children Without Children With Children Without Children With Children Without Children With Children TRB 3 Annual Meeting CDROM

8 Zhao et al. 8 TABLE 6 Lifestyle HBW Trip Rates Based on Tampa Bay Model Structure Working HH with Working HH with MPO/MSA Auto Ownership Retired HH No Children Children Lee County Volusia County (Districts &) Volusia County (Dsitricts 3,4,5,6) TRB 3 Annual Meeting CDROM

9 Zhao et al. 9 TABLE 7 HBW Trip Rates Based on FSUTMS Model Structure Dwelling Number of Person in HH MPO/MSA Auto Type Single Family Lee County Volusia County (Districts &) Volusia County (Dsitricts 3,4,5,6) Multi Family Single Family Multi Family Single Family Multi Family Single Family Multi Family Comparisons of Model Estimates with Expected Number of Trips The HBW trips estimated from the lifestyle models and the FSUTMS model were compared with the expected district totals. Table 8 presents the comparisons. The absolute difference between the expected and estimated HBW trips for each district is shown in the Diff column. TRB 3 Annual Meeting CDROM

10 Zhao et al. TABLE 8 Comparison of Estimated HBW Trips for Three Urban Regions MPO/MSA District Expanded Southeast Trips Florida Diff Tampa Diff FSUTMS Diff 3,8 33,59,78 3,455,355 9,83,57 43,75 46,59,84 43, ,4 4,95 Lee County 3 7,64 33,9 6,758 3,59 4,47 5,5,38 4 7,5 33,38 5,87 9,564,6 7, , 9,7,8 7, , Total 48,79 66,587 8,38 5,455 8,83 38,54,5 7,7 73,9 3,9 7,47,697 6, 8,489 6,363 6, , ,55 79 Volusia 3 4,89 4, ,8 39 4, County 4&5,57, , , ,84 33,765,59 36,468,84 37,458,74 MSA Total 49,35 5,4 5,645 54,3 4,96 4,45,833 9,48, 864 9,8 33 9, ,39 3,948 5,89 8,66,467 3,844 4, ,93 4,55 8,359 37,495 3,698 37,673,5 4 4,63 45,49 4,86 4,74, 4, ,39 46,947 3,638 4,9,387 44,58,9 6 8,68 9, , , ,9 9,364,7 6, ,84,749 8,7 3,95 594,7 3, Total 57,84 7,6 3, ,65 3,465 DEMOGRAPHICS AND BENEFITS OF LIFESTYLE MODELS The results from table 8 indicated that for all three Florida urban regions, applying lifestyle models would improve the accuracy of trip production estimation, although depending on the lifestyle structures and to different degrees. Lee County and Volusia County MPOs would benefit using Tampa Bay Regional Model structure, while MSA would benefit using Southeast Florida model structure. One reason may be that MSA had a smaller retired population, as compared to Lee County and Volusia County MPOs (it also had a smaller seasonal population, but the effect of seasonal population has not been determined). Table 9 provides information on population, percentages of seasonal HHs, and percentages of population age groups based on the census for several Florida counties. By assuming the differences in demographic characteristics in each county between 99 and are negligible, it may be observed that MSA shared more similarity in demographics with Miami Dade County and Broward County, while the demographics of Lee and Volusia counties resembled more closely to those of the Tampa Bay Region. TRB 3 Annual Meeting CDROM

11 Zhao et al. TABLE 9 Demographics of Florida Counties Participating in the Study and Using Lifestyle Models Region County Population Seasonal Population HH HHs Under * Median Income (%) (%) (%) (%) ($) Lee Lee 44, ,39 Volusia Volusia 443, ,9 Clay 4, ,854 Duval 778, ,73 Southeast Florida Tampa Bay Note: Nassau 57, , St. Johns 3, ,99 Broward,63, ,69 Palm Beach,3, ,6 MiamiDade,53, ,966 Hernando 3, ,57 Hillsborough 998, ,663 Pasco 344, ,969 Pinellas 9, , Florida 5,98, ,89 Census data do not include information on retirees. Population of age 65 and older was considered retired population. It should be noted, however, that it is not just the percentage of retired population that makes a lifestyle model work better. The spatial distribution of this population is also important. In Broward and MiamiDade counties, while the percentage of the retiree population may be relatively low, their absolute numbers are large because of the large total populations. Uneven distribution of the retiree population can also result in significant errors in subareas. Figures and show the distribution of population of age 6 and over in terms of their percentages by census tract ( census). It may be seen that Broward County has many enclaves where the retired population congregates. In contrast, the distribution of the older population is more even in the counties in the MSA. TRB 3 Annual Meeting CDROM

12 Zhao et al. FIGURE Percentage of Population of Age 6 and Over by Census Tract in Miami Dade and Broward MPOs. FIGURE Percentage of Population of Age 6 and Over by Census Tract in MSA. TRB 3 Annual Meeting CDROM

13 Zhao et al. 3 SPATIAL TRANSFERABILITY OF TRIP RATES Many counties in Florida share similar demographics. However, not all will embrace lifestyle models for reasons such as lack of resources to collect lifestyle data. These counties will be able to benefit from lifestyle models if they can borrow trip rates from similar areas. For this purpose, the spatial transferability of the lifestyle models was tested by comparing the trip rates of Lee County MPO, MSA and Volusia County MPO. For Volusia County, however, only districts and were investigated since more than 65% of total regional HHs were located in these two districts. The tests were similar to those conducted by Chicoine and Boyle (), who derived a set of tables from the 973 Niagara Frontier Transportation Committee (NFTC) data and the 974 Genesee Transportation Council (GTC) data and compared the trip rates in each cell between the two regions. Only the cells with greater than a percent difference were tested. The results showed that six cells out of 5 had different trip rates at a significance level of.5. The authors concluded that the NFTC trip rates were generally replicable using the GTC data. The Wilcoxon RankSum test, also known as MannWhitney test and equivalent to Kruskal Wallis test for two samples, was used to test the difference between the trip rates for the three urban regions. Ten percent difference in the mean values of the samples was used as the criterion for overriding the results of the KruskalWallis test (3). The hypotheses for the Wilcoxon RankSum test are (4): H o : H a : The two population relative frequency distributions are identical. The two population relative frequency distributions are shifted with respect to their relative locations. Tables and provide the Pvalues of statistical tests on the trip rates from the two lifestyle models for the three urban regions. Table summarizes the results and shows the number of cells with significant differences in trip rates between the three urban regions. TRB 3 Annual Meeting CDROM

14 Zhao et al. 4 TABLE PValue of Wilcoxon RankSum Test on HBW Trip Rates Based on Southeast Florida Model Structure Workers MPO/MSA Presence of Children Auto Lee versus Volusia Lee versus Volusia versus Without Children With Children Without Children With Children Without Children With Children *.5*.78.7* = cells with less than a percent difference = cell count too small (under 8) to be tested * = cells found to have trip rates different at significance level of.5 in the two counties.75.3*.* *.9*.3.44* TRB 3 Annual Meeting CDROM

15 Zhao et al. 5 TABLE MPO/MSA Lee versus Volusia Lee versus Volusia versus PValue of Wilcoxon RankSum Test on HBW Trip Rates Based on Tampa Bay Model Structure Auto Ownership Retired HH.*. *.8 Working HH with No Children *.437. *. * Working HH with Children * = cells with less than a percent difference = cell count too small (under 8) to be tested * = cells found to have trip rates different at significance level of.5 in the two counties TABLE Model Southeast Florida Model Structure (6 cells) Tampa Bay Model Structure ( cells) Comparison of Trip Rates between the Three Urban Regions Number of Cells with Trip Rates Different (Significance Level =.5) Lee versus Volusia Lee versus Volusia versus out of 5 cells out of cells 6 out of 3 cells out of cells out of cells 4 out of cells Although the results in Table cannot be used to claim spatial transferability of the lifestyle models, they indicated replicability. The results also showed that the trip rates for Lee County and Volusia County MPOs were closer, probably due to their similarity in demographics. CONCLUSIONS In this paper, a methodology for evaluating lifestyle models for three Florida MPOs/MSA and the results are described. The methodology involved estimating HBW trips based on census demographics, using household survey data to calibrate trip production rates for lifestyle models, and comparing HBW trips predicted by the lifestyle and FSUTMS models against the expanded trips by geographic locations based on the survey data and census HHs. The results from this study indicated that lifestyle models could improve HBW trip production prediction for all three Florida urban regions to different degrees. To determine accurately the amount of improvement remains a challenge because of a lack of reliable data for model validation. Areas with more retired population were found to benefit more from lifestyle models than areas with a small TRB 3 Annual Meeting CDROM

16 Zhao et al. 6 retired population. However, the size of the retired population alone may not serve adequately as a a basis for rejecting lifestyle models, since the spatial distribution of retired population will also affect a model s ability to produce accurate results. Tests of spatial transferability offered evidence that trip rates might be applied to different urban areas if they shared similar demographics. However, other characteristics of population, such as the size of seasonal residents, also need to be accounted for. Studies of more urban areas will be needed to draw more definite conclusions. ACKNOWLEDGEMENTS This research was sponsored by a grant from the Research Office of the Florida Department of Transportation (FDOT). The opinions, findings and conclusions expressed in this publication are those of the authors and not necessarily those of the FDOT. REFRENCES. Chicoine, J.E., and D.K. Boyle. LifeCycle Concept: A Practical Application to Transportation Planning. Transportation Research Record 987, TRB, National Research Council, Washington, D.C., 984, pp. 7.. Metropolitan Transportation Commission. San Francisco By Area 99 Travel Model Improvement Project: Compilation of Technical Memoranda, Vol. and., Oakland, California, Allen, W.G., and J.D. Curley. Using Life Cycle in Trip Generation. Preprint. Presented at the 76th Annual Meeting of TRB, National Research Council, Washington, D.C., January Technical Memorandum No. : Development of the Trip Generation Models. Barton Aschman Associates for the Maricopa Association of Governments, Phoenix, Arizona, October Portland METRO. Phase III: Travel Demand Forecasting Model: A Summary of Inputs, Algorithms, and Coefficients. January, Tampa Bay Regional Transportation Analysis Phase III: Technical Report, Gannett Fleming, Prepared for the Florida Department of Transportation, March Enhanced FSUTMS Trip Generation Model. Final Report Submitted to Florida Department of Transportation, District 4. Carr Smith Corradino, August FSUTMS Trip Generation Model (GEN), Final Technical Report, No. 3. Florida Department of Transportation, Tallahassee, FL, June 997. TRB 3 Annual Meeting CDROM

17 Zhao et al Salomon, I. and M. BenAkiva, The Use of the LifeStyle Concept in Travel Demand Models, Environment and Planning A, Vol. 5, pp , Allaman, P.M., T.J. Tardiff, and F.C. Dunbar, New Approaches to Understanding Travel Behavior, NCHRP Report 5, Transportation Research Board, National Research Council, Washington, D.C., 98.. Simonsen, J. and A.J. Neveu, A Comparison of LifeCycle and Family Size Variables in Trip Generation Analysis to Assess Predictive Capability, Transportation Analysis Report, Planning Division, New York State Department of Transportation, January Lamb, D., D. Bredahl, T. Adler, and L. Rimmer, HH Survey for the Tampa Bay Regional Transportation Planning Model, Proceedings of the 4th National Conference on Transportation Planning Methods Applications, Daytona Beach, Florida, SchimpelerCorradino Associates, Urban Transportation Planning Model Update, Task B: REVIEW & REFINEMENT OF STANDARD TRIP GENERATION MODEL, Final Report, Florida Department of Transportation, June Hines, W.W. and D.C. Montgomery. Probability and Statistics in Engineering and Management Science, Third Edition. John Wiley & Sons, New York, 99. TRB 3 Annual Meeting CDROM