Trip Generation Characteristics of Free- Standing Discount Stores: A Case Study

Trip Generation Characteristics of Free- Standing Discount Stores: A Case Study THE RETAIL CHAIN THE INSTITUTE OF TRANSPORTAtion Engineers (ITE) recently published CHOSEN FOR THIS STUDY the sixth edition of Trip Generation. 1 Data from 750 new studies were added to the WAS FAIRLY DISTINCT IN existing database for a combined total of more than 3,750 individual trip generation studies. Data collection and statistical TERMS OF ANNUAL SALES, analysis efforts resulted in the addition of EXPANSION OF EXISTING 19 new land uses. A review of the data for the Free-Standing Discount Store (FSDS) in the ITE STORES, SIGNIFICANT manual indicates that for some time periods, the number of studies reported is very INCREASE IN NEW STORES small as few as three. In those cases, no AND VARIETY OF regression equations are developed. The ITE manual classifies an FSDS as MERCHANDISE SOLD. a free-standing store with off-street parking. These stores offer a variety of customer services, centralized cashiering and THE AUTHORS PRESENT a wide range of products. They typically THE COLLECTED AND maintain long store hours seven days a week. The stores included in the study are ANALYZED DATA FROM often the only ones on the site, but they also can be found in mutual operation 18 MARYLAND STORES. with a related or unrelated garden center or service center. The manual further illustrates that the FSDS are sometimes found as separate parcels within a retail complex with their own dedicated parking. Based on the ITE definition, the retail chain chosen for the study may be classified as FSDS. This is consistent with a study by TRC Raymond Keyes Associates (RKA). 2 These stores offer a wide variety of merchandise, maintain long store hours and are normally open seven days a week. A survey of sites in Maryland, USA, shows that they are normally the only store at the BY MANOJ K. JHA AND DAVID J. LOVELL site, and they have their own dedicated parking. A single retail chain was chosen for the purposes of this case study because a significant amount of information was readily available for these stores and because the focus on one particular retailer should help reduce the effect on trip generation of various dissimilarities between competing retailers. BACKGROUND INFORMATION In the past decade, the number of new retail stores in the United States has grown rapidly. These stores can be classified into at least three different categories: discount stores, supercenters and membership warehouse clubs. The growth trend for the particular retail chain in this study can be seen in Figure 1. While size may be considered as one of the factors that differentiate between the conventional discount stores and the supercenters, the biggest difference between them is the verities of merchandise they sell. The supercenters sell far more varieties of merchandise than the discount stores. The stores that were used in the present study would all be considered discount stores and were very similar in nature, even though their sizes ranged from 92,000 square feet (sq. ft.) to 156,500 sq. ft. Such a significant growth of FSDS is of interest to city, county and state traffic engineers because of the significant amount of traffic generated by these stores, which further taxes the ability of existing roads and streets to serve traffic. The significant number of trips generated by such stores may not be accurately predicted by the equations for FSDS obtained from Trip Generation. In the current study, a separate regression equation has been developed for this retail franchise using actual data from 18 stores in Maryland. LITERATURE REVIEW In recent years, there have been several studies regarding trip generation for some land uses for which adequate data are not available in the ITE manual. Datta et al. 3 developed trip generation models for multiuse highway commercial ITE JOURNAL ON THE WEB / MAY 1999 85

Number of Stores 2500 2000 1500 1000 500 0 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 Year Discount Stores Supercenters Warehouse Clubs Average Size ( 1000 sq. ft.) Number of Associates Note: In 1997, some of the discount stores were converted to supercenters. Therefore, number of discount stores in 1997 is less than in 1996. Figure 1. Relevant data for the retailer. developments. Patel et al. 4 provided trip generation characteristics of economy motels. A similar study was done by Slipp and Hummer, 5 which provided a trip generation rate update for public high schools. The study reported that the small number of studies in the ITE manual associated with public high schools, in conjunction with their age, warranted further study in the area. The study by Peyrebrune 6 investigated the trip generation characteristics of shopping centers. The study was done for the ITE manual and investigated the following: The relationship between trip generation and a combination of several independent variables; The definition and classification of shopping centers used by ITE; The effects of the age of the data in the ITE trip generation database; and The relationship between pass-by trips and a combination of several independent variables. The conclusions of the study were summarized as follows: Additional data should be collected to further expand the ITE database; Consideration should be given to collecting data for additional independent variables for both trip generation and pass-by trips; and Consideration should be given to applying the methodology and procedures developed for this analysis to other land uses that may benefit from multivariable analysis. RKA conducted a study for five retail stores located throughout New Jersey, USA, to determine trip generation and pass-by information. The Peyrebrune study clearly indicated a need to update the ITE database and that information on other significant variables must be explored. The sixth edition of the ITE manual certainly provides better information on several new land uses and has a richer database. However, the database for some land uses is still poor, including FSDS. STUDY OBJECTIVE This study investigates the trip generation characteristics of a major retail chain, which may fall in the category of FSDS. The objectives of this study can be summarized as follows: Discuss the correlation between several independent variables using actual data from existing stores; Analyze the efficacy of several independent predictors of trip generation by estimating the coefficient of determination, R 2, for single variable regression; Develop a separate multivariate regression model and compare it with the best single-variable model; and Provide a comparison between the true and estimated values and values obtained by using the ITE data for FSDS. The following independent variables were chosen for the study: size of the store (sq. ft.), parking, annual average daily traffic (AADT) of the adjacent street, number of employees, population of the market area, population density (population/unit square mile) and the catchment area (square miles). Catchment area was defined as the area of the region from which shoppers would normally be attracted to a particular store, estimated qualitatively using a circle of large enough radius to capture the entirety of the nearby city or municipality. Admittedly, this process is subject to gross error, but more accurate estimates would not be possible without the use of detailed market research, including perhaps surveys of existing or potential patrons. The population density was computed by dividing the population of the market area by the catchment area. STUDY METHODOLOGY The study was done using data obtained from 18 stores in Maryland. For an FSDS, separate time periods were analyzed for weekdays and weekends in the ITE manual. Separate regression models were developed for two independent variables: gross floor area (GFA) of the store and number of employees. However, the number of studies reported using the number of employees is very limited as few as three. The Peyrebrune study indicates that for shopping center developments, the average weekday evening peak hour is the most critical time period. The same argument may be valid for FSDS, assuming similar trip-making tendencies. The study further illustrates that for shopping centers, GFA does not necessarily explain all of the variability in trip generation rate. Therefore, consideration should be given to collecting additional data for 86 ITE JOURNAL ON THE WEB / MAY 1999

various independent variables such as adjacent street traffic, population, household income, shopping opportunities and other socioeconomic variables. Data Collection The following data were obtained for the analysis: Trips in and out of the store during the p.m. peak hour of the adjacent street on an average weekday; Size of the stores in sq. ft. (GFA); Parking spaces available at each store; p.m. peak hour traffic of the adjacent street based on AADT; Number of employees working in each store; The population of the market area; and The catchment area for the store. An inspection of the monthly traffic variation 7 reflected that July was the peak traffic month. Additionally, the garden centers that were part of the stores were normally open only during spring and summer months. Therefore, the month of July was chosen for the analysis. While July is the highest trip-making month, it is not the highest retail shopping month. A monthly factor for the retail shopping variation is not provided for the FSDS (Land Use Code 815) in the ITE manual. For shopping centers (Land Use Code 820), however, a monthly retail shopping variation is provided in the manual. This variation may be used for the discount stores since the tendency for retail shopping may be the same for both shopping centers and discount stores. Thus even though the analysis included data for July, appropriate factors may be used to translate the trip-making tendency for other months using the variation table. The trips in and out were obtained by actual counts at each of the sites during July 1997. Although in most cases the retail franchise was the only store at the site, in some cases there were other stores in the vicinity. However, every effort was made to count only the traffic that used the retail store s parking lot and was destined specifically for the retailer. In addition, in some cases there were multiple access and egress points. In those cases, the traffic was counted at each access point and then combined to give the total number of in and out trips. Information on AADT was obtained from the files of the Maryland State Highway Administration (SHA). 7 Information on the population of the market area was obtained by discussion with local authorities. The complete data for the stores is shown in Table 1. Statistical Analysis After collecting the data, statistical analyses were performed, which included an examination of the correlation matrix, and single and multivariate regressions. The possible regression models applicable to the present study may be linear, logarithmic, inverse, linear-logarithmic, or logarithmic-linear. For a single-variable regression, the decision of which explanatory variable to use was based on maximizing the coefficient of determination R 2, which is equivalent to maximizing the correlation (in absolute value) between the independent and dependent variables. Departures from a strictly linear model would have been considered only if they Table 1. Key data for the retail stores in Maryland. Pop. Catchment Trips Trips Total trips= Size density area Radius Store # (in) (out) ins+outs (sq. ft.) Parking AADT Employees Population (pop./area) (sq. miles) (miles) 1 320 350 670 95,000 430 24,000 200 35,000 227.36 153.94 7 2 435 415 850 155,000 820 20,000 225 20,000 63.66 314.16 10 3 437 443 880 156,500 600 33,375 230 25,000 35.37 706.86 15 4 400 380 780 110,000 550 8,000 207 17,000 18.72 907.92 17 5 405 415 820 155,000 500 53,050 235 45,000 143.24 314.16 10 6 330 320 650 92,000 600 7,000 170 25,000 19.89 1,256.64 20 7 295 305 600 110,000 560 20,000 215 20,000 32.48 615.75 14 8 360 340 700 95,000 600 31,225 180 30,000 33.04 907.92 17 9 420 450 870 156,000 800 18,000 235 45,000 63.66 706.86 15 10 389 414 803 120,000 300 29,150 210 40,000 259.85 153.94 7 11 368 320 688 95,000 800 21,850 190 35,000 445.63 78.54 5 12 430 470 900 155,000 760 6,250 240 38,000 189.00 201.06 8 13 420 440 860 145,000 700 7,979 208 20,000 32.48 615.75 14 14 387 462 849 92,000 510 26,875 178 22,000 48.63 452.39 12 15 309 331 640 110,000 450 29,775 211 35,000 65.92 530.93 13 16 380 370 750 124,300 625 30,000 224 30,000 95.49 314.16 10 17 365 335 700 120,000 500 7,000 210 22,000 41.44 530.93 13 18 480 440 920 145,000 600 42,000 240 40,000 157.19 254.47 9 ITE JOURNAL ON THE WEB / MAY 1999 87

offered significant improvement in the R 2 values. No such improvements were noted; hence only strictly linear-in-parameters models are included here. The correlation matrix indicates the relative significance of the independent variables, as shown in Table 2. The first column of this matrix shows the correlation coefficients between the dependent variable (trips generated) and each of the Table 2. Correlation matrix obtained from the regression analysis. Popu- Pop. Catchment Trips Size Parking AADT Employee lation density area R 2 Trips 1.00 Size 0.75 1.00 0.56 Parking 0.33 0.40 1.00 0.11 AADT 0.16 0.17-0.31 1.00 0.03 Employee 0.57 0.88 0.22 0.27 1.00 0.32 Population 0.22 0.27-0.06 0.52 0.40 1.00 0.05 Pop. density -0.02-0.12 0.04 0.20 0.03 0.54 1.00 0.00 Catchment area -0.24-0.21 0.04-0.35-0.41-0.45-0.74 1.00 0.06 Table 3. Results of regression analysis. Type of Regression: Single Variable Regression Statistics R 2 0.56 Standard error 68.87 Observations 18 Coefficients Standard error t-stat P-value Intercept 406.66 82.85 4.91 0.00016 Size 2.96E-3 6.56E-4 4.52 0.00035 Resulting Regression Equation: T = 406.66 + (2.96 10-3 ) (Size) Type of Regression: Multivariate Regression Statistics R 2 0.64 Standard Error 78.68 Observations 18 Coefficients Standard error t-stat P-value Intercept 871.95 366.20 2.38 0.04 Size 0.0049 0.0019 2.60 0.03 Parking 0.0070 0.1788 0.04 0.97 AADT 0.0002 0.0019 0.11 0.92 Employee -3.2186 2.3008-1.40 0.19 Population 0.0007 0.0034 0.21 0.84 Pop. density -0.1059 0.3851-0.27 0.79 Catchment area -0.0970 0.1261-0.77 0.46 Resulting Regression Equation: T = 871.95 + (4.9 10-3 )(Size) + (7.0 10-3 )(Parking) + (2.0 10-4 )(AADT) (3.2)(Employees) + (7.1 10-4 )(Population) (1.1 10-1 )(Density) (9.7 10-2 )(Catchment) independent variables. The squares of these correlation coefficients are the R 2 values that would have resulted from single-variable regressions on each of these variables; hence the variable with the highest correlation coefficient (in absolute value) is the best choice for a single predictor, in the sense of maximizing the R 2 value. Because size has the highest correlation with trips, it was used as the independent variable for the single variable analysis. The multivariate regression was performed using the independent variables: size, parking, AADT of the adjacent street, employee, population, catchment area and population density. The t-statistic and P-values were obtained for each of the independent variables in order to assess their significance. The results of these regressions are shown in Table 3. The true and predicted values for single and multivariate regression as well as the single variable regression using ITE values are shown in Figure 2. Note that because Figure 2 is a line plot, multiple sites with the same size are considered separately on the abscissa. RESULTS AND DISCUSSION The correlation matrix indicates that the size of the store has a very high correlation (~0.75) with the number of trips generated. The next highest correlation is observed between number of employees and trips (~0.57) and then between parking and trips (~0.33). Because there is a very high correlation observed between number of employees and size (~0.88), this suggests that the number of employees is not a good supplemental predictor of trips, as corroborated by the multivariate analysis. The other variables have negligible impact on trip-making tendency. The multivariate analysis results in an R 2 value of 0.64. This is slightly higher than that obtained by a single variable analysis for size. The t-test shows that the null hypothesis can be rejected for the size at the 95 percent level of significance. Therefore, size is significant. This is also obvious by looking at the P-value, which, when subtracted from 1.0, yields the highest level 88 ITE JOURNAL ON THE WEB / MAY 1999

of significance at which the null hypothesis can safely be rejected. The t- statistics and P-values for the other variables show that the null hypothesis cannot be rejected at the 95 percent level of significance for these variables. Size is therefore the only statistically significant variable that contributes to the trip-making tendency. A comparison of predicted values for the single and multivariate regressions reveals little significant difference between the models, as suggested by the only slight improvement in R 2 (Figure 2). Additionally, Figure 2 shows that in most (but not all) cases, multivariate analysis is a better predictor than single variable analysis. Qualitatively, this observation is consistent with the slight improvement in the R 2 value offered by the multivariate model. The plot of true values, predicted values and the ITE values for single variable regression indicates that the ITE data clearly underestimates the trips (Figure 2). p.m. Peak Hour Trips (Ins + Outs) 1000 900 800 700 600 500 400 CONCLUSIONS Based on the study, the following conclusions may be drawn: 1. For the FSDS investigated in this study, size is the most significant trip predictor, which accounts for 56 percent of the variance in trip-making tendency. In fact, the significance tests show that size is the only statistically significant variable in the analysis. 2. The other variables that somewhat contribute to the trip-making tendency are number of employees and parking. However, because number of employees is highly correlated to the size, it offers little additional explanation for variance. The effect of parking is much less than size, when considered separately, and offers little marginal improvement, when the two are considered jointly. 3. The AADT, population, population density and the catchment area had the very least impact on the trip-making tendency and, therefore, may be ignored in the trip generation analysis. 4. The predicted values for multivariate analysis are closer to the true values than the predicted values for single variable analysis. Therefore, the multivariate analysis gives a better fit of the data. If information on other variables such as employees, parking and population is known, a multivariate analysis should be performed. Otherwise a single variable analysis for size may be performed, with little sacrifice in statistically defensible accuracy. 5. The equation developed in the ITE manual for FSDS is not a good predictor of trips for FSDS. The ITE equation normally underestimates the number of new trips generated. 6. The data used in this study may be added to the ITE database for FSDS. References 1. ITE. Trip Generation, 6th ed. Washington D.C., USA, 1997. 2. RKA. Trip Generation Study. New Jersey Wal-Mart Stores. December 1995. 3. Datta, T.K., S. Datta and P. Nannapaneni. Trip Generation Models for Multiuse Highway Commercial Developments. ITE Journal (February 1998): 24 30. 4. Patel, M.I., F.J. Wegmann and A. Chatterjee. Trip Generation Characteristics of Economy Motels. ITE Journal (May 1996): 21 26. 5. Slipp, P.R.M., and J.E. Hummer. Trip Generation Rate Update for Public High Schools. ITE Journal (June 1996): 34 40. 6. Peyrebrune, J.C. Trip Generation Characteristics of Shopping Centers. ITE Journal (June 1996): 46 50. 7. Maryland Department of Transportation. Traffic Trends 1995. State Highway Administration, 1995. 300 90000 100000 110000 120000 130000 140000 150000 160000 Size (sq. ft.) TRUE Predicted (Single Variable Regression) Predicted (Multivariate Regression) ITE Figure 2. Plot of true, predicted and ITE values. MANOJ K. JHA, P.E., is a Transportation Engineer for the SHA in Baltimore, Md. He also is working toward his Ph.D. in transportation engineering at the University of Maryland, College Park. Jha holds a B.E. in mechanical engineering from Regional Engineering College, Durgapur, India, and an M.S. in mechanical engineering from Old Dominion University, Norfolk, Va., USA. He is an Associate Member of ITE. DAVID J. LOVELL is an Assistant Professor in the Department of Civil Engineering at the University of Maryland, College Park. He holds a B.A. in mathematics from Portland State University and an M.S. and a Ph.D. in civil engineering from the University of California, Berkeley. Lovell is an Associate Member of ITE. ITE JOURNAL ON THE WEB / MAY 1999 89