Traffic analysis is one of the most reliable ways to compare potential retail sites objectively, but it requires understanding both pedestrian and vehicle patterns relevant to how customers will actually reach your specific concept.
This article focuses on the methodology for gathering and interpreting traffic data, distinct from revenue projection which builds on top of this information.
Sourcing vehicle traffic count data
State and local departments of transportation typically publish annual average daily traffic counts for major roads, which are available through public data portals or by direct request. These figures give a baseline for comparing road segments objectively.
For roads without recent official counts, third-party traffic data providers offer more current or granular estimates, sometimes broken down by time of day, which can be valuable if your concept depends heavily on commuter or evening traffic patterns.
Evaluating pedestrian traffic in urban and mixed-use areas
In dense urban or mixed-use corridors, pedestrian counts matter alongside vehicle traffic, particularly for walk-up convenience formats near transit stops, office buildings, or residential density. Manual counts during representative time periods, or third-party foot-traffic data services, can supplement vehicle data.
Consider the walking distance and route quality from nearby residential or office density to your site — a location a short walk from a transit stop can outperform a slightly larger but less accessible site.
Interpreting the numbers relative to site accessibility
Raw traffic count means little without factoring in how easily passing traffic can actually access your site — a high-traffic road with difficult turning access, no dedicated turn lane, or a site set back from the road will convert far fewer passersby into customers than the count alone suggests.
Visit the site at different times of day and days of the week to observe actual traffic flow and access patterns firsthand, since published data reflects averages that can miss important local nuance like peak commute timing or weekend patterns.
Applying traffic data to compare multiple sites
When comparing multiple candidate sites, normalize traffic data by accessibility and visibility factors rather than ranking purely by raw count, since a moderately trafficked, easily accessible corner site often outperforms a high-count but hard-to-enter location.
This comparative approach is a useful complement when reviewing multiple city-specific listing options across different corridors within the same metro.
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