Land Use Impacts on Trip Generation Rates

Transcription

1 TRANSPORTATION RESEARCH RECORD Land Use Impacts on Trip Generation Rates REID EWING, MARYBETH DEANNA, AND SHI-CHIANG LI In the conventional four-step travel demand modeling process, the number of trips made by a household is modeled in terms of household size, income, and other sociodemographic variables; any effect of location, land use, or transportation service level is discounted. This is the same as discounting any effect of household accessibility to out-of-home activities as a factor in trip generation (accessibility depending on all three: location, land use, and transportation service level). In contrast to the practice of trip generation, theory tells us that trip rates must vary with accessibility, and some (not all) empirical studies have found that they do. In light of conflicting empirical studies, and the obvious need for more precise and policy-sensitive travel forecasts, this issue is revisited. The independent effects of land use and accessibility variables on household trip rates were tested for using data from Florida travel surveys. It was found that, after controlling for sociodemographic variables, residential density, mixed use, and accessibility do not have significant, independent effects on household trip rates. Conventional trip generation models, which generate person trips by vehicle (not by all modes) and do so without regard to residential location, may not be as bad as one would imagine a priori. In the conventional four-step travel demand modeling process, the number of trips made by households is modeled in terms of household size, income, and other sociodemographic variables; any effect of location, land use, or transportation service level is discounted (1 3). This is the same as discounting any effect of household accessibility to out-of-home activities as a factor in trip generation (accessibility depending on all three: location, land use, and transportation service level). The conventional modeling process also discounts any effect of location, land use, and transportation service level on the choice between vehicle and walk/bike modes. The latter are not even modeled in the conventional process; instead, total person trips by vehicle are generated on the basis of household sociodemographic characteristics, and the only travel choices modeled are between automobile and transit. Standard traffic impact analysis procedures based on ITE s Trip Generation manual discount these same variables (4). Although ITE suggests a downward adjustment in trip rates for single-family households living at densities above five units per acre, the adjustment is only 0.1 daily trips, a negligible number when the base rate is 9.55 daily vehicle trips (4,p.256). In contrast to the practice of trip generation, theory tells us that trip rates must vary with accessibility. The equilibrium point between travel supply and demand depends on the generalized cost or disutility of travel, which in turn depends on household accessibility to out-of-home activities. Households will make additional trips up to the point at which the utility (value) of the last out-ofhome activity just equals the disutility (generalized cost) of travel to R. Ewing and M. DeAnna, FAU/FIU Joint Center for Environmental and Urban Problems, 220 SE Second Avenue, Suite 709, Ft. Lauderdale, Fla S.-C. Li, Florida Department of Transportation, District 4, 3400 West Commercial Boulevard, Ft. Lauderdale, Fla it. If travel-activity demand is elastic, the increase in trip rates with improved accessibility will be large (Figure 1). If the demand is inelastic, the increase will be small (Figure 2). Even if total travel demand is inelastic, vehicle trip rates could vary with location, land use, and transportation service level. As activities become highly concentrated, walking and biking become viable options, and driving becomes less attractive because of congestion. The number of trips by vehicle should decline as walk/bike trips substitute for them. WHY RAISE THE ISSUE NOW? Why raise the issue after decades of treating travel demand as inelastic? One reason is that this issue was never resolved satisfactorily (the available empirical evidence being mixed). Treating trip rates as inelastic was more a matter of convenience than rational choice. Another reason is that the Clean Air Act Amendments of 1990 and the Intermodal Surface Transportation Efficiency Act of 1991 demand more precise regional travel forecasts. In particular, federal regulations require that congested travel times from traffic assignment be fed back to trip distribution and mode choice (per the solid lines in Figure 3). This is done to capture the effects of congestion on destination choice (in favor of closer destinations) and mode choice (in favor of alternatives to the automobile). By the same token, congestion may depress trip generation rates by increasing the disutility of travel. If so, the feedback loop should extend one step farther back to trip generation (per the dashed line in Figure 3). A final reason for raising the issue is that growth management demands more precise assessments of the effects of development. Go or no go decisions on developments, and the amount of mitigation required, depend on impact estimates. Some planners and developers claim that vehicle trip rates can be lowered by raising densities and mixing land uses in neotraditional developments. Yet the better accessibility that accompanies higher densities and mixed uses may have the opposite effect, raising vehicle trip rates rather than lowering them. It all depends on elasticities of total travel demand and of substitution between walk/bike and vehicle modes. PRIOR STUDIES Almost 40 years of land use and travel studies leave us with more questions than answers. In some studies, trip rates prove independent of land use and location variables. In other studies, rates rise or fall with these same variables. Why the different results? It may be because different studies measure different things. In some studies, the independent variable is accessibility (5 12). In others, it is density (13 18). In a few, it is a proxy for accessibility or density such as relative location in the metropolitan area (urban versus suburban, for example) (19 23).

2 2 TRANSPORTATION RESEARCH RECORD 1518 FIGURE 1 Elastic travel demand. FIGURE 4 Addition of walk/bike trips. FIGURE 2 Inelastic travel demand. Differences in study results could also be due to the presence or absence of control variables. Some studies of land use impacts on household trip rates control for sociodemographic differences among households. Others do not. If households with high activity demand (due to high income, large families, etc.) are concentrated in places of poor accessibility (the suburbs, for example), the simple correlation between accessibility and trip rates may be negative when the real relationship between the two, controlling for sociodemographic differences, is positive (Figure 5). Finally, differences in study results could be attributable to differences in study areas. That is, results for Cedar Rapids may vary from results for San Francisco simply because the two cities are so different. Accessibility in some areas may never be good enough to generate new trips or prompt significant shifts to walk/bike modes. COMPETING THEORIES One can find evidence in the empirical literature to support four different theories of trip generation. The four theories, which can be seen in Table 1, diverge in their assumptions about the elasticity of total travel demand and the elasticity of substitution between walk/ bike and vehicle modes. The theories provide researchers with hypotheses that can be tested. If total trip rates and vehicle trip rates prove independent of accessibility, it will support the first theory that travel demand is inelastic and mode substitution is minimal. This is the theory most consistent with conventional modeling practices. FIGURE 3 Travel model improvements. Accessibility, density, and various proxies are related, but they are not the same. Accessibility is usually measured in terms of travel time to activities across the region; it reflects regional land use patterns and transportation service levels. In contrast, density reflects only land use patterns in a localized area. Thus, at least theoretically, areas of high density could be less accessible than areas of low density, and total trip rates could fall as density rises. In some studies, the dependent variable is the number of trips by all modes. In others, it is the number of trips by vehicle. In still others, it is the number of trips for a specific purpose, such as shopping. Because of latent demand and mode shifts, total trip rates may rise with improved accessibility even as vehicle trip rates remain constant or fall (Figure 4). FIGURE 5 Influence of socioeconomic variables.

3 Ewing et al. 3 TABLE 1 Effects of Accessibility on Trip Rates The most interesting theory is the fourth: that travel demand is elastic and mode substitution significant. If total trip rates rise with improved accessibility and vehicle trip rates remain the same or fall, it will support this theory. The theories are not to be taken too literally. Of course, mode substitution occurs at some point if density is high enough and accessibility good enough: witness Manhattan. But that point may lie outside the range of conditions found in U.S. cities of the automobile age, including those in Florida. DEFINITIVE TEST A definitive test of these hypotheses (if there is such a thing as definitive ) would meet six criteria: 1. Use data for entire households. Because of trip trading and coordinated trip making, the household is the primary unit of travel decision making and the logical unit to study. 2. Control for sociodemographics of households. Travel patterns will vary with travel demand (sociodemographics) as well as travel supply (accessibility). Researchers cannot isolate the effects of one without controlling for the other. 3. Segment by trip purpose. If accessibility has any effect on trip rates, it will be most apparent for discretionary trips, that is, trips that can be postponed or not made at all. This effect may be lost in total trip rates that include travel to work and other obligatory activities. 4. Include all trip purposes. Discretionary activities may substitute for one another. An analysis limited to, say, shopping trips cannot detect any offsetting travel for social, recreation, or other purposes when shopping destinations are inaccessible. 5. Include nonmotorized trips. Nonmotorized trips may either substitute for or supplement motorized trips. 6. Be validated in different areas. Given the disparate findings reported in the literature, it would be hazardous to generalize about land use/travel relationships without confirming results in different areas. Travel Data Bases The authors have obtained travel survey data from several areas in Florida, including Palm Beach and Dade counties. The first survey was conducted to update a regional travel model, the second to better understand changing travel patterns after Hurricane Andrew. Both data bases were purged of households that had underreported trips (in which household members of reporting age did not all complete travel diaries). Both data bases were also edited extensively for data that were incorrectly or inconsistently coded or keyed in. The most common problems were departure or arrival times that were obviously miscoded; return trips that were not reported; trip purposes that made no sense given a household s composition; and sociodemographic data that disagreed with data collected in the earlier screener survey. It was usually possible to resolve inconsistencies and correct errors by studying a household s complete set of responses. Summary information for the two data bases is presented in Table 2. TABLE 2 Data Base Summaries

4 4 TRANSPORTATION RESEARCH RECORD 1518 TABLE 3 ANOVA Results for Palm Beach County, Household Trips by All Modes: Significance of F-Statistics TABLE 5 ANOVA Results for Dade County, Household Trips by All Modes: Significance of F-Statistics Trip Generation Models Tested To assess the independent effects of land use and accessibility variables on household trip rates, it is necessary to control for sociodemographic variables that affect trip rates. But which sociodemographic variables should be controlled? The authors chose to test the variables used in Florida s standard cross-classification model (part of the Florida Standard Urban Transportation Model Structure, or FSUTMS): household size (1 to 5 ), vehicle ownership (0 to 2 ), and dwelling type (single- and multifamily). Separate runs were done for home-based work trips and home-based other trips (including all other home-based trip purposes). In one set of runs, the dependent variable was total person trips by all modes. In another, it was total person trips by vehicle. Walk/bike trips represented only 3 percent of total trips in the Palm Beach County travel survey. Hence, the authors did not anticipate much difference in results between the two sets of runs; indeed, there was little. Only results for total person trips by all modes are presented in Tables 3 through 8. However, for Dade County, walk/bike trips represent a more substantial 7 percent of all trips. Here, the authors expected that results might differ between total person trips and total person trips by vehicle. These expectations proved wrong. To illustrate, results for both sets of runs are presented in Tables 5 through 8. Covariates Tested for their ability to explain variations in household trip rates left unexplained by the main variables; they are defined as follows: LOG-OVERDEN log 10 [(total population total employment)/ land area in square miles]. BAL-MIX 1 [absolute value (total employment 1.5 total housing units)/(total employment 1.5 total housing units)]. ACCESSIBILITY HBW accessibility index for home-based work trips. ACCESSIBILITY HBO accessibility index for home-based other trips. In the overall density measure for the zone of residence (LOG- OVERDEN), a logarithmic form was used to reduce the skewing effect of a few high-density zones. The jobs-housing balance measure for the zone of residence (BAL-MIX) ranges from 0 for zones with only jobs or housing, not both, to 1 for zones with a nominal balance of jobs and housing. The two accessibility measures for the zone of residence are both gravity-model formulations that give more weight to attractions that are nearby (in travel time). Beyond the sociodemographic variables used to cross-classify households, several land use and accessibility variables were tested TABLE 4 ANOVA Results for Palm Beach County, Household Trips by All Modes: Variance Explained and Unexplained TABLE 6 ANOVA Results for Dade County, Household Trips by All Modes: Variance Explained and Unexplained

5 Ewing et al. 5 TABLE 7 ANOVA Results for Dade County, Household Trips by Vehicles: Significance of F-Statistics In addition to these land use and accessibility measures, one other covariate was tested: WORKERS number of workers in household. This covariate was included because the Florida standard model does not explicitly account for labor force participation of household members. METHOD OF ANALYSIS The method used to analyze household trip rates was analysis of variance (ANOVA). This is a near-ideal application of ANOVA. Households can be divided into categories based on sociodemographic variables, and differences in trip rates can then be compared within and between categories. The larger the latter relative to the former, the more significant are the cross-classifying variables and the better is the model. In addition, ANOVA allows the measurement of the explanatory power of other variables (called covariates) beyond those used to cross-classify households. Covariates were processed last, after the main variables had explained all the variation in trip rates that they could. Any variance in household trip rates left unexplained was analyzed for relationships to land use and accessibility variables. TABLE 8 ANOVA Results for Dade County, Household Trips by Vehicles: Variance Explained and Unexplained RESULTS ANOVA results are presented in Tables 3 through 8: total trips by Palm Beach County households are analyzed in Tables 3 and 4; total trips and total trips by vehicle for Dade County households are analyzed in Tables 5 and 6, and 7 and 8, respectively. Tables 3, 5, and 7 show the significance levels of all variables tested. Tables 4, 6, and 8 divide the total variance in trip rates into three parts: percentage explained by all categorical (crossclassifying) variables taken together, percentage explained by all covariates taken together, and percentage left unexplained. The first percentage tells us how good the cross-classification model is. The second tells us how much better the model would be if other variables were introduced. Noteworthy results include the following: All cross-classifying variables are highly significant except one, dwelling type. After controlling for household size and vehicle ownership, dwelling type was significant only for home-based other trips in Dade County (and then only at the 0.01 level or thereabouts, not a high significance level for an ANOVA with so many cases). Conventional models can be improved by introducing other sociodemographic variables. In the Florida standard model, after controlling for household size and vehicle ownership, the number of working household members is positively related to the number of home-based work trips generated and negatively related to the number of home-based other trips. The reason for the former is obvious; as for the latter, working household members are not out making other trips during most of their waking hours, being otherwise occupied. Residential density, mixed use, and accessibility appear to have negligible effects on household trip rates. Where variables prove significant, levels of significance are low and no pattern is apparent. This is true for person trips by all modes as well as person trips by vehicle. The results tend to support the first theory in Table 1 that travel demand is inelastic and mode substitution is minimal. Conventional trip generation models, which generate person trips by vehicle (not by all modes) and do so without regard to residential location, may be better than one would imagine a priori. A qualifier applies. Land use and accessibility variables may still have some effect on household trip rates, indirectly through their effect on automobile ownership. Automobile ownership rates have been found to vary with residential density, albeit in studies that have not always controlled for sociodemographic differences among households (13,14,16,24 26). The authors plan to follow up with their own investigation of the effects of land use and accessibility on automobile ownership. REFERENCES 1. Harvey, G., and E. Deakin. A Manual of Regional Transportation Modeling Practice for Air Quality Analysis. National Association of Regional Councils, 1993, pp Stopher, P. Deficiencies of Travel-Forecasting Methods Relative to Mobile Emissions. Journal of Transportation Engineering, Vol. 119, 1993, pp Cambridge Systematics. Short Term Model Improvements Final Report. U.S. Department of Transportation, 1994, pp Trip Generation, 5th ed. Institute of Transportation Engineers, Washington, D.C., Marble, F. F. Transport Inputs at Urban Residential Sites. Papers of the Regional Science Association, Vol. 5, 1959, pp Dalvi, M. Q., and K. Martin. Estimate of Nonwork Trip Demand: A Disaggregated Approach. In Urban Transportation Planning: Current Problems and Future Prospects (P. Bonsall, M. Q. Dalvi, and P. J. Hills, eds.), Abacus Press, Tunbridge Wells, 1976.

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