Headlines about Sunoco adding 36 stores to its U.S. network fit a broader pattern: portfolios of 10 to 50 sites are changing hands as operators push into priority corridors and prune underperformers. In a market where the total U.S. c-store count stands at 148,026 and declined 1.5 percent year over year, with single-store operators down 3.1 percent to 60.4 percent of all locations, competition for quality assets is intense. The question for buyers and sellers is not whether scale matters, but which specific assets will create accretive cash flows, and which will dilute them. That answer lives in traffic, speed and intent. C-Site brings empirical structure to pre-merger diligence and post-close optimization so expansion and closures are guided by evidence rather than averages. What to measure before an acquisition Average Annual Daily Traffic is a starting point, not a decision rule. Two sites with similar AADT can behave very differently once you account for directionality, speed distributions, approach geometry and access friction. C-Site Advanced provides directional AADTs for each road and ramp, plus 15-minute volumes and speeds across weekdays and weekends, monthly and day-of-week fluctuation curves, and congestion definitions rooted in observed travel behavior. For one Pennsylvania c-store example documented in our "New Metric in C-Site Selection Traffic Analytics" post, we paired traffic with a 15-minute accessibility population of 40,310 and generated stop propensity indicators from speed and maneuverability patterns. This allows an acquirer to rank a target list by real visit potential, not just passing counts. Accuracy is not optional in valuation Pre-M&A models are only as reliable as the inputs. In the research report Application of Cross-Verified Multisource Data to Remediation of Inaccurate Detector Measurements, Brodski, Stepanyan, Kozakevich and coauthors show how single-source counter data would have produced material errors at a Main St corridor: AADT East was assigned at 15,619 vehicles per day and AADT West at 17,764 vpd after consolidating GPS probe data, geospatial context and speed-density modeling, while the one-to-two detector estimate would have been off by 12.8 percent on the eastbound approach. On a westbound segment misattributed to a section behind a retail plaza, the single-source estimate would have been off by 56.9 percent, while the corrected estimate was 15,799 vpd. The error metric was calculated as E = |SSE − TE| ÷ (SSE + TE) × 100 percent, where SSE is the single-source estimate and TE is the Ticon estimate. These are the kinds of gaps that swing revenue projections and, ultimately, deal pricing. Cannibalization screening and the upside of clustering Overlap risk cannot be assessed with radius maps alone. C-Site's driver behavior analysis distinguishes local traffic from in-transit flows by profiling speed and acceleration distributions, lane availability and intersection control. In "High AADT but Low Sales?" we detail why a high count with commuter-speed flow is often less convertible than a lower count with maneuverable speeds and cleaner access. That lens makes cannibalization modeling materially sharper. It also prevents false negatives. Our "Competitor presence: a threat or a benefit" case study in North Carolina demonstrates that clustering can expand the market rather than split it. On an approach near a Walmart access road, traffic on the retail-bound side ran 178 percent higher than the opposite approach, and a new development produced measurable demand uplift at nearby operators. C-Site's Impact Analysis framework quantifies such exogenous effects and separates a true demand lift from short-term construction detours. In pre-merger diligence, this is the difference between closing a site you should keep and investing in a co-location that can compound visits. Who is in the flow matters In "More than AADT: In-Transit Demographics & Retail Site Selection", we outline why two corridors with equal counts can diverge economically depending on who is driving by. A site with 50,000 vehicles composed largely of commute trips can underperform a 30,000-vehicle corridor with a higher share of local shopping trips and middle-income households. C-Site's approach integrates origin-destination patterns, travel time distribution and multi-source traffic trends to infer the composition of pass-by demand with greater fidelity than static household demographics or narrow mobile panels. Operational variables that move the P&L post-close Integration is where many acquisitions miss their pro formas. C-Site's 15-minute traffic and speed profiles support labor scheduling and inventory normalization at newly acquired stores. In our "Traffic Monitoring as a Tool for Operation Excellence" work and C-Site product specifications, congested hours are defined as periods when median speed is below 80 percent of expected speed, and rush hours when median speed is below 50 percent and flow exceeds 60 percent of capacity. Aligning staffing, delivery windows and promotional timing to these precise windows reduces costs and captures peak conversion opportunities.