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4. ANALYSIS RESULTS Developing a method of estimating future design-day parking demand entailed conducting several analyses using the data collected. First, the consistency of the data collected from each site was compared. Second, possible relationships between parking demand, sales activity levels (sales and transactions), square footage, and other store characteristics were examined to determine the best possible predictor of parking demand. Third, the study day parking demand values were adjusted to the peak hour of the 5`h busiest day. Fourth, a range of parking demand rates was determined based on the results of the parking study. Data Comparison Several components of the data collected were compared between the study sites to assist in determining whether the data was biased or unreasonable. These components included the number of transactions, sales totals, visiting customers, parking demand patterns, and peak hours of activity. Comparisons of several of these components have already been described in the previous chapter. Specifically, both study stores had roughly the same building configuration, experienced the same patterns of parking lot use, and experienced similar peak hours of customer and parking activity. Surprisingly, both stores also had essentially the same number of visiting potential customers. However, as shown in Table 4, each store experienced different transaction-based characteristics. While not directly comparable due to their different study dates, the Montebello store brought in 76% more in sales on October I than the Pomona store brought in on October 8. Not only did the Montebello store have more transactions, but the average transaction amount was 42% higher than that of the Pomona store ($142 versus $100). These results are partially due to the fact that the Montebello store is about 16% larger than the Pomona store, but could also be partly due to their different location types (industrial versus retail areas). For example, the Pomona store, which is located in a retail shopping center, had a higher percentage of visiting customers who did not make a purchase. Table 4 - Employees, Customers, and Transactions on the Studv Dav Store Average Transactions Visiting Store # Store Name Size Employees Transaction Total During Customers* (sq.ft.? Amount Transactions 8 Hours of During 8 Hours Stud of Stud 703 MONTEBELLO 75,618 46 $142.58 970 708 804 705 POMONA 65,367 33 $100.63 753 613 799 *Excludes children. Couples were counted as "l customer." These comparisons reveal that the two stores exhibit similar customer and parking activity patterns during the course of the day, but that the magnitude of this activity differs between the stores, likely due to store size and location type. A larger set of study stores would be necessary to determine whether these relationships are statistically significant. Parking Study of Two Contractor s Warehouse Stores - Final Report Page 9 31 Aa45