Truck Trip Generation by Grocery Stores

Quantifying the relationship between the number and types of truck trips generated by different land uses provides information useful for traffic demand analyses, forecasting models, and a general understanding of the factors that affect truck mobility. This project evaluated data collection methodologies for determining truck trip generation rates by studying a specific kind of establishment. This effort focused on grocery stores and collected both interview and manual count data from eight supermarkets in the Puget Sound region.

We selected grocery stores for this project because they constitute a common land use that is present in almost every type of neighborhood in the metropolitan region. Grocery stores generate truck trips that have the potential to affect all levels of the transportation roadway network, from local roads in neighborhoods to highways. The eight stores in the Puget Sound region identified for this study were diverse and included both national and local chains. The stores ranged in size from 23,000 to 53,500 square feet and included a variety of urban and suburban locations.

Methodologies for gathering trip generation information were identified in the literature. Telephone interviews and manual counts, which are frequently used data collection methodologies, were explored in this project. The project started with telephone interviews of four distribution centers. This step helped to refine the interview approach and helped to determined that data from larger warehouses could not be easily used to develop information on the number of trips traveling to individual stores. A second round of interviews, lasting between 10 and 15 minutes, was then conducted with the managers or receivers of the nine grocery stores. In addition to the number of truck trips that the store generated, the interviews explored a range of topics related to the busiest days and their delivery windows. This information was used to set up manual, on-site truck counts at each of the grocery stores.

We concluded that a combination of telephone interviews and manual counts is a reasonable way to collect accurate truck trip generation rates. Telephone interviews were an important first step. They established contact with grocery stores, which then provided permission for on-site manual counts. Information elicited from store interviews also included the days and times when the viii truck deliveries occurred so that the manual counts could be scheduled to reflect optimal times. In addition, the interview conversations provided sometimes unanticipated but valuable information that was relevant to understanding truck trip-generation rates. Because it is cost prohibitive and inefficient to send manual counting teams to observe facilities for long shifts, information from store managers regarding their delivery windows and hours made the counts more feasible.

The Puget Sound grocery stores in the study (all of which were conventional supermarkets) generated an average of 18 truck trips per day on typical weekdays. These daily counts were probably low, as some of the stores accepted a few late deliveries outside of the receiving windows. Most of these truck arrivals occurred before noon, and the average delivery time was 27 minutes. Although peak days of the week varied across the sample set, all reported higher volumes during holidays.

The manual counts (15 site observations) provided more accurate truck trip generation rates than did telephone interviews. The interview responses indicated approximately ten to twelve trucks per day in comparison to the average of 18 trucks per day counted at each store by observers. The telephone interviewees at the grocery stores clearly underestimated the number of trucks and provided only minimal information on truck characteristics. Manual counts also provided more detailed information regarding truck type, delivery location (loading docks or front door), average delivery time, and product mix.

Few grocery store characteristics that could be directly related to truck trip generation rates were identified. The project team reviewed literature discussing both trip generation data collection and grocery store management and could not identify any specific characteristic that could be used to quantify the number of truck trips generated by different stores. While size or employment is often related to truck trips in the ITE Trip Generation Manual, this effort did not find any direct relationship with these variables, with a possible exception related to a store’s size. This finding, that smaller stores generated more trucks trips, suggests that one promising area to explore is the linkage between the level at which stores are served by regional warehouses or direct service delivery (DSD) and the number and type of truck trips. The manual counts indicated variability in the nature and size of the delivery trucks, which in turn related to ix whether the deliveries were at the front door (often small trucks and DSD) or loading dock (larger trucks from warehouses with consolidated loads). Smaller stores often use more DSD, which may result in more truck trips generated. It is also possible that smaller stores had smaller stock rooms, requiring more frequent deliveries. Other census-related variables such median household income, residential density and jobs-housing balance, were evaluated, but no significant relationships to truck trip rates were found.