Bankable Feasibility in a Market Where Traffic Patterns No Longer Sit Still

Casey’s recent leadership reshuffle and a plan to add 400 convenience stores highlight that growth in traffic-dependent retail involves more than just real estate. Changes in roads, flood protection, energy reliability, and public investment affect traffic flows and site economics.

For c-store operators, fuel retailers, QSR brands, shopping center investors, and lenders, it is essential to assess if traffic at a site converts into sales over time across various revenue streams. C-Site Insight transforms feasibility studies into empirical assessments of location risk and revenue potential rather than static market summaries.

Why a feasibility study has to go beyond AADT

Average Annual Daily Traffic (AADT) is useful but insufficient. High AADT sites may underperform if traffic is transit-only, too fast to stop, peak times mismatch business hours, or competition is stronger nearby.

C-Site’s methodology includes trade area insights, supply analysis, competitive landscape assessment, directional traffic analysis, customer profiles, market demand, customer estimates, site ranking, and 5-year projections across product categories.

Lenders and investors focus on the probability of durable cash flow, not just traffic counts.

From passing vehicles to forecastable revenue

C-Site Sales Projection reports estimate potential sales for fuel, in-store, and car wash based on traffic, demand, competition, and community profiles. Reports include site features, ranking, market demand, 5-year projections, competition analysis with Level of Service ratings, directional AADT, and demographics.

The approach links movement patterns to purchase potential considering directional AADT, intraday traffic patterns, speed, congestion, and seasonality to evaluate demand stability and volatility.

For chains expanding rapidly, each site must answer how much observed traffic can realistically become customer demand.

What research says about pass-by demand

A study titled Convenience Store Trip Generation developed models from data of 26 convenience stores in Knox County, Tennessee, using 24-hour traffic counts at store driveways and adjacent streets.

The models had high R2 values (0.84 weekday trip model; 0.75 for peak hours). Adjacent street traffic volume and market area accessibility significantly affected store trips. Adjacent street traffic had the greatest influence on trips generated.

The pass-by trip share averaged 72%. The pass-by trip model correlated positively with adjacent street ADT with an R2 of 0.75.

This emphasizes that c-store and fuel site revenue potential heavily depends on quality, direction, timing, and stopping behavior of nearby traffic rather than just population or building size.

Precision at the exact address can change the deal

C-Site focuses on exact-location traffic for feasibility. A validation case in southwestern Ohio compared traffic estimates to detector readings and included demographics and local business points to verify accuracy, illustrating the critical importance of precision in site evaluation.