Trip Generation Characteristics of Super Convenience Market Gasoline Pump Stores

Transcription

1 Trip Generation Characteristics of Super Convenience Market Gasoline Pump Stores This article presents the findings of a study that investigated trip generation characteristics of a particular chain of convenience market gasoline pump stores. The study was conducted to determine if trip generation characteristics of these retail facilities differed from those contained in the Institute of Transportation Engineers Trip Generation, 8th Edition: An ITE Informational Report. The sites included in this study consist of convenience markets with a larger average size and a larger average number of vehicle fueling stations than those included in the ITE trip generation report. JINA MAHMOUDI, P.E. Introduction Estimating accurate trip generation rates for developments consisting of convenience markets and gasoline service pumps is a challenge faced by many transportation professionals. Uncertainties exist in finding the most appropriate trip generation rates. ITE s latest edition of Trip Generation, 8th Edition, informational report contains two categories of land uses for convenience markets and gasoline pumps. These land use categories are distinguished from one another based on the primary activity of business that occurs at the development. Land Use Code 853 (Convenience Market with Gasoline Pumps) contains sites where the primary activity of business is the selling of convenience items. Land Use Code 945 (Gasoline/Service Station with Convenience Market) provides trip generation data and rates for sites where the primary activity of business is the fueling of motor vehicles. Neither Trip Generation nor Trip Generation Handbook provide specific guidance on how to determine which activity is the primary activity of business. Therefore, this classification is left to the judgment of the individual conducting the survey. The 8th edition of Trip Generation provides trip generation rates for Land Use Codes (LUCs) 853 and 945 during the weekday AM and PM peak hours of the adjacent street traffic based on the following site characteristics: market; and at the gasoline station. Table 1 presents a summary of the trip generation rates for ITE LUCs 853 and 945. The table suggests a noticeable difference between trip generation rates of LUC 853 (where the primary activity of business is the selling of the convenience items) and LUC 945 (where the primary activity of business is the fueling of motor vehicles). Also, it is observed that LUC 853 generates more trips per vehicle fueling position during both AM and PM peak hours, but fewer trips per These observations can indicate that the independent variable associated with the primary business of the site (vehicle fuel- LUC 853) plays a more important role in determining the trip generation rates compared with the independent variable associated with the nonprimary activity of the site (vehicle fueling positions for LUC 1 In a 8 study, trip generation data and rates for three developments consisting of fueling pumps and a convenience market were analyzed and submitted to ITE. The study was conducted in New Jersey, USA. All three sites belonged to the same retail chain. The study concluded that even though the study sites fit the description contained in Trip Generation for LUC 853 (Convenience Market with Gasoline Pumps), the trip generation rates at these sites were significantly higher than Table 2 summarizes site characteristics and trip generation rates from the sites included in the New Jersey study and LUCs 853 and 945. As shown in Table 2, the average number of vehicle fueling positions and the 1. Another reason can be that none of these independent variables by itself is a suitable factor for trip generation analysis of these land uses, and a per site trip generation rate may be a better indicator of the trip generation rates. 16 ITE JOURNAL / JUNE 212

2 Table 1. ITE Trip Generation, 8th Edition, Volume 3 Weekday Trip Generation Average Rates Trips per Vehicle Fueling Position Trips per 1, Sq. Ft. Gross Floor Area Land Use Title Land Use Code Primary Activity of Business PM Peak Hour of Convenience Market with Gasoline Pumps Gasoline/Service Station with Convenience Market LUC 853 Selling of convenience items LUC 945 Fueling of motor vehicles Table 2. Weighted Average Trip Generation Rates Data Source Average Number of Vehicle Fueling Positions Trips per Fueling Position Average GFA (1, sq. ft.) Trips per 1, Sq. Ft. GFA New Jersey Sites , ITE LUC , ITE LUC , average convenience market size for the New Jersey study sites were considerably larger than those of the ITE land uses. Further, as indicated in the table, the weighted average rate for the New Jersey sites is more than 15 percent higher than the comparative trip generation rates of the ITE LUCs 853 and (except for the case of PM peak hour of LUC 945). The above observations suggest that the New Jersey sites have different trip generation characteristics from the study sites included in the ITE LUCs 853 and 945, and may need to be classified under a new land use code in Trip Generation. In an effort to collect additional data and further investigate the trip generation characteristics of the larger convenience store sites included in the New Jersey study, data for several additional sites in the same retail chain were collected in a different geographic location. Objectives The objectives of the study can be summarized as follows: additional similar sites of the same retail chain in the New Jersey study to enhance the existing database for 2. Refer to Trip Generation Handbook, Chapter 4. the Trip Generation report; of the larger sites by analyzing the available data; larger sites with those from similar ITE land uses; and eration rates of the sites compared with the ITE trip generation rates and, if applicable, recommend the addition of a new land use. Site Description and Data For this study, data were collected at eight sites in Maryland, USA. In addition, data from the three New Jersey sites were also utilized in the study. The stores considered for the study offered a large array of convenience items including freshly brewed coffee and coffee accessories, daily made donuts, muffins, bagels, cakes, hot and cold beverages, breakfast items, dairy items, fresh fruits, soups, light meals, ready-to-go and freshly made sandwiches and wraps, and ready-to-go salads. Each store also had automated teller machines (ATMs), self-service touch-screen computers for ordering food, and public restrooms. The gasoline stations commonly included 12 to 16 fueling positions; however, one site provided 2 fueling positions. The size of the convenience markets at the study sites ranged from 4,676 to 5,771 square feet of GFA. The primary business activity of the study sites could not be easily identified. Sales of convenience items and fuel seemed to occur in similar frequencies at the study sites. tering and exiting) were taken in 15-min. intervals during the traditional AM and PM peak hours of the adjacent street traffic. The peak hour of the adjacent street is the highest consecutive hour between 7: a.m. and 9: a.m. and between 4: p.m. and 6: p.m., during which the traffic volumes on the adjacent streets are the highest. The weekday peak hours of the adjacent street traffic were the only time periods during which trip generation data were collected and analyzed for this study. Therefore, the AM and PM peak hour of the adjacent street traffic will simply be referred to as the AM peak hour and the PM peak hour throughout the remainder of this article. Also, because the average values of the independent variables associated with the study sites are significantly larger than those of the ITE LUC 853 and LUC 945 database, the study sites are referred to as super convenience market gasoline pump stores in this article. For ITE JOURNAL / JUNE

3 Site Number Table 3. Study Site Characteristics and Trip Generation Data Number of Vehicle Fueling Positions 1, Sq. Ft. GFA Total Trips During Total Trips During Maryland Sites ,24 7:3 8:3 4:3 5: ,655 7: 8: 5: 6: ,74 7: 8: 4:15 5: ,771 7:15 8:15 4:15 5: ,676 7:15 8:15 4: 5: ,74 7:15 8:15 5: 6: ,687 7: 8: 4:3 5: ,655 7:3 8:3 5: 6: Average (Maryland) 15 5, New Jersey Sites ,676 7:15 8:15 4:45 5: ,587 7:45 8:45 4:3 5: ,676 7: 8: 5: 6: Average (New Jersey) 16 4, Average of All Sites 15 5, each of the eight Maryland sites, data were manually collected at the site driveways on a Friday 3 between July 9 and June 21. The three New Jersey sites were surveyed in December 5: two on a Tuesday and the other one on a Wednesday. For the sites that had multiple driveways, traffic was counted at each driveway and then combined for all driveways to obtain the total number of entering and exiting trips. All 11 sites were stand-alone facilities located in suburban settings. Table 3 summarizes the study site data. Methodology Separate regression equations were developed for the AM and PM peak hours. The dependent variable in each regression equation was the total number of trip ends with either the origin or the destination of the trip being the study site. Trips made by vehicles that did not utilize any of the services offered at the study site (i.e., purchasing convenience items, using the ATM 3. Friday is usually considered a peak day for retail stores while Tuesday and Wednesday can be considered more average days in terms of sales. However, owing to the study s time constraints, Friday was the only day in the week during which data collection was possible for the Maryland sites. inside the building, fueling the vehicle at the gasoline pumps) were excluded from the total number of trips. These trips included vehicles that cut through the study site to avoid traffic signals or congestion on adjacent streets, vehicles that made a U-turn at the parking lot of the study site, or vehicles that stopped at the site in order for the motorists to meet and talk with each other. Based on the site characteristics used to determine the trip generation rates for similar land uses in Trip Generation, two site characteristics were considered in this study as independent variables for the regression equations: 1. Size of the convenience market (square footage); and 2. Number of the vehicle fueling positions at the site. Analysis Single-variable regression analyses were performed to investigate the correlation between the total number of trips generated at the site and each of the site characteristics. The dependent variable (total site vehicle trip ends) was plotted separately as a function of each of the independent variables. Figures 1 through 4 provide scatter plots and the best fit regression curves and equations during the AM and PM peak hours for the study sites. One statistical measure of the goodness-of-fit of regression models is the value of the coefficient of determination, R 2. This measure indicates how well the regression line approximates real data points. The closer the value of R 2 to 1., the better the regression line fits the data points and, therefore, the stronger the correlation between the independent and dependant variables in the regression equation. Linear and logarithmic regression analyses were conducted. No significant improvement in the R 2 value was observed in the logarithmic regression equations; hence, only linear regression equations are presented on the plots. Table 4 shows the weighted average trip generation rates and results of the statistical analysis for the AM and PM peak hours of the study sites. Table 5 summarizes the trip generation rates for the related ITE land uses and the weighted average trip generation rates for all 11 sites analyzed in this study during the AM and PM peak hours. Results and Discussion As seen in Figures 1 and 2, the value of the coefficient of determination, R 2, for the 18 ITE JOURNAL / JUNE 212

4 Table 4. Weighted Average Trip Generation Rates and Statistical Analysis for Study Sites Weighted Average Rate Range of Rates Statistical Std. Dev. Weighted Average Std. Dev. Coefficients t-statistics Std. Error Variable AM PM AM PM AM PM AM PM AM PM AM PM AM PM Fueling Positions Size (sq. ft.) T =.7S R 2 =.1 4, 4, 5, 5, 6, T =.5S R 2 =. 4, 4, 5, 5, 6, S = Square Feet GFA S = Square Feet GFA Figure 1. Number of trips generated during the AM peak hour (based on GFA) Directional Distribution: 5% entering, 5% exiting Figure 2. Number of trips generated during the PM peak hour (based on GFA) Directional Distribution: 51% entering, 49% exiting T = 16.85FP R 2 = T = 2.413FP R 2 = FP = Number of Vehicle Fueling Positions FP = Number of Vehicle Fueling Positions Figure 3. Number of trips generated during the AM peak hour (based on number of vehicle fueling positions) Directional Distribution: 5% entering, 5% exiting Figure 4. Number of trips generated during the PM peak hour (based on number of vehicle fueling positions) Directional Distribution: 51% entering, 49% exiting single-variable regression analysis based on the size of the convenience market is very low for both the AM and PM peak hour equations provided on the plots. Thus, it can be inferred that the correlation between the size of the store (sq. ft. GFA) and the total number of the trips generated during AM or PM peak hours is very weak. Similarly, the R 2 s of the regression equations for the number of vehicle fueling positions for both AM and PM peak hours are very low, which suggests a weak correlation between the total number of trips generated and the number of vehicle fueling positions at the study sites (Figures 3 and 4). Another factor that might have contributed to the low R 2 s of the regression equations can be that the independent variables vary little compared with variation of number trips generated at the study sites. Further, the low t-statistic values for the coefficient of the size variable (sq. ft. GFA) during both AM and PM peak hours (.1 and.9 from Table 4) suggest that at any level of significance, the null hypothesis of the size variable not having a significant effect on the number of trips generated cannot be rejected. This means that in a single-variable regression model, the size of the convenience market does not exhibit a significant impact on the number of the trips generated at the study sites. Also, the t-statistic for the coefficients of the number of vehicle ITE JOURNAL / JUNE

5 Table 5. Comparison of ITE and Study Trip Generation Rates Site Characteristics Vehicle Fueling Positions 1, Sq. Feet GFA Primary Activity of Business at Study Site Site Average (Number) AM Peak Hour of PM Peak Hour of Average (Sq. Feet) AM Peak Hour of PM Peak Hour of Selling convenience items LUC 853 8* 16.57* 19.7* 3, Fueling of motor vehicles LUC ,* 79.3* 97.8* Fueling of motor vehicles/ Selling convenience items Study Sites , * The primary activity of business plays an important role in selecting the best suitable independent variable to estimate trip generation rates. Trip rates associated with the nonprimary independent variable (vehicle fueling positions for LUC 853 and GFA for LUC 945) are probably less suitable to be used. fueling positions variable during the AM and PM peak hours (1.24 and.21) suggests that at any level of significance, the null hypothesis of the number of vehicle fueling positions not having a significant impact on the number of the trips generated cannot be rejected. This implies that in a single-variable regression model, the number of vehicle fueling positions is not a significant factor in the trip generation estimation of the study sites during the AM and PM peak hours. From Table 5, it can be seen that the weighted average trip generation rates of the 11 study sites based on each of the independent variables are higher during the AM peak hour than the PM peak hour (35.86 in the morning vs in the afternoon for the number of vehicle fueling positions, and in the morning vs in the afternoon for the sq. ft. GFA). Because of time and budget limitations, a survey of the site patrons was not conducted to record the site usage and customer activity during the AM and PM peak hours. However, the higher trip generation rates at the study sites during the AM peak hour can indicate a high volume of coffee or other breakfast-related sales at these sites. These results differ from LUCs 853 and 945, which show higher trip generation rates for the PM peak hour than the AM peak hour. The higher trip generation rates during the AM peak hour suggest that unlike most land uses where the PM peak hour is considered to be the most critical time period in trip generation analysis, the AM peak hour may be the critical time period for trip generation analysis of super convenience market gasoline pump stores surveyed in this study. However, traffic volumes on the adjacent streets will also need to be considered for determining the critical time period of trip generation analysis. Those volumes were not counted in this study. In addition, a comparison of the trip generation rates of LUCs 853 and 945 and the weighted average rate of the 11 sites included in this study reveals that the weighted average trip generation rates of the study sites based on the total number of vehicle fueling positions (35.86 in the morning and 3.88 in the afternoon) are considerably higher than those of the Trip Generation land uses during both the AM and PM peak hours. A similar comparison of the convenience market size (sq. ft. GFA) reveals that the weighted average trip generation rate of the study site during the AM peak hour (11.22) is significantly higher than those for any of the relevant ITE land use codes. These results suggest that super convenience market gasoline pump stores have substantially different trip generation characteristics than Trip Generation, 8th edition, land use categories, which consist of a convenience market and gasoline pumps. Several factors may have contributed to this difference, including the larger average number of vehicle fueling positions at the study sites, the larger average size of the convenience market, the popularity of this particular brand of retail stores, additional services offered at the stores, and potential difference in site usage and customer activity compared with that of the LUCs 853 and 945. Conclusions and Future Research For super convenience market gasoline pump stores that were investigated in this study, the following conclusions can be drawn: 1. The size of the convenience market or the number of vehicle fueling positions alone are not significant trip generation variables when a single-variable regression analysis is performed. 2. Future trip generation analysis should include consideration of multivariable regression analysis of factors other than the size of the convenience market and the number of vehicle fueling positions. Examples of such factors are the traffic volumes on the adjacent streets, gasoline price at the study site, and/ or site location characteristics. 3. Additional data need to be collected from other sites to increase the sample size. 4. Considering the current information, the AM peak hour is the critical analysis period, as the weighted average trip generation rates during the AM peak hour are higher than those of the PM peak hour. 5. Trip generation characteristics of the study sites are significantly higher than those of similar land uses included in the Trip Generation report and may merit consideration as a new land use code in that report. 2 ITE JOURNAL / JUNE 212

6 6. For future studies, conducting a survey of the store patrons to record the site usage and customer activity will help to determine the primary activity of business at the sites. Acknowledgments The author would like to thank F. Maleki and A. Mahmoudi for their time and assistance in the data collection effort for this study. References Institute of Transportation Engineers. Trip Generation, 8th Edition: An ITE Informational Report. Washington, DC, USA: ITE, 8. Institute of Transportation Engineers. Trip Generation Handbook, 2nd Edition. Washington, DC, USA: ITE, 4. JINA MAHMOUDI, P.E., has been with the Institute of Transportation Engineers (ITE) since 7. She is currently serving as ITE s Planning and Engineering Projects Director. In her time with ITE, Ms. Mahmoudi has been actively involved in developing several engineering resources including the Trip Generation and Parking Generation informational reports. She received her bachelor of science degree in civil engineering from the Bahá í Institute for Higher Education, Tehran, Iran, and her master of science degree in transportation engineering from the University of Maryland, College Park, MD, USA. She is a member of ITE. Visit to download an order form today! Advertise your company products and services by placing a banner ad on the ITE Web site! Placing a banner ad is a great way to reach ITE s more than 16, members and other Web site guests. Target Your Market When you place a section banner ad on the ITE Web site, you choose where your advertisement is placed. If you are hiring a new employee you may want to place your banner ad within the Employment Center. Have a new product line you're looking to promote? Look no further than the Technical Information section. Increase Your Exposure Vertical banners are displayed on the left side of the screen within the navigation bar. This means your ad will be seen on almost every page of the ITE Web site! Only one advertiser uses the designated space at a time, and there are only two vertical spaces available. Ads are placed in the order that they are received. Please visit for ad specifications. For availability, please contact Christina Garneski, Marketing and Membership Services Senior Director, at ext. 128 or cgarneski@ite.org. ITE JOURNAL / JUNE