We conducted a traffic (jargon – trip) generation study of existing CVS Pharmacies in the Minneapolis/St. Paul metropolitan area (Download Trip Generation Study for MN CVS Pharmacies – for NEC York & 69th in Edina MN). While we were on site collecting manual peak period trip generation data to calibrate our mechanical tube counts with, we collected parking occupancy and drive through queue data during the weekday p.m. peak period. Six suburban sites were surveyed from November 3, 2009 through November 7, 2009. Each store had two drive through lanes.
Our traffic generation results are shown in Table 1, which also shows the average rates from the Trip Generation report for comparison. During the p.m. peak period, we observed a maximum queue of three total cars waiting in the drive through lanes. The maximum parking occupancy during the p.m. peak period was 33 vehicles (2.54 parked stalls per 1,000s of building square feet). Based on the data for each peak period, we expect the parking lot and drive through data to represent the peak usage.
Given all the scatter in a typical trip generation graph, would you think a multi-variable regression would be more accurate than the usual “trips per 1000 sf” generation rates? For example, could we get more accuracy if we include store size and some measure of exposure, such as AADT or, for walking trips, population within, say, half a mile of the store?
I’ve only used the trip gen books. I’d be interested in the opinion of someone that has collected some data for them.
Having a low to zero r-squared value always makes me wonder if the data has any validity. Unfortunately, its the best we have.
CVS gets fresh ADT counts with every site they are considering (this also gives an indirect measure of population). They have thresholds they need to meet for a site to be considered viable.
The sites included were all suburban with low pedestrian activity. I think peds could matter in their urban core stores, but those are a different animal all together.
With pharmacies, I think the variable that would give the highest corollary would be the number of existing pharmacies within a one mile radius. I don’t think our profession is excited about going that route though because it would be difficult to use.