Institutional capital continues to reshape convenience retail. Car wash networks are expanding inside c‑store portfolios, and multi‑hundred‑site c‑store deals coexist with regional transactions like 20 to 30 store packages. The strategic question is no longer whether consolidation will continue. It is how operators and investors quantify site‑level demand, prioritize expansion versus closure, and size post‑deal upside with empirical traffic data rather than assumptions. Many M&A models treat traffic as a single scalar, which can misprice assets and misallocate capital. C‑Site changes the unit of analysis from a store list to the exact road approach at a given address, measured continuously and broken into drivers, time windows, and conditions that move revenue. C‑Site measures demand precisely by reporting directional AADT for primary and secondary roads and adjacent highways; it provides continuous 24/7/365 data current within about a week; it has proven accuracy averaging 91.79% with an AADT error of 8.31%; it analyzes traffic patterns in intraday 15-minute bins, weekday vs weekend profiles, monthly seasonality, and driver behavior including speed and congestion; it distinguishes between local and transit traffic, essential for evaluating convenience retail, foodservice, and subscription car wash potential; it integrates trade area demographics and reach for practical proxies of addressable customers. C‑Site also supports financial models with feasibility studies and sales projections including market demand, competitive saturation at multiple radii, and 5-year revenue projections across fuel, in-store, and car wash categories. The US convenience store market is consolidating: there are about 148,026 c‑stores, declining 1.5% year over year; single-store operators hold 60.4%. Consolidation is natural given density of one c‑store per 2,245 people. Successful value creation depends on site-specific strategies for growth, reformat, or exit. Incomplete traffic data inflates risks; C‑Site mitigates this with multi-source data fusion, AI, and traffic engineer validation. A practical framework includes: 1) Establish directional AADT, intraday peaks, weekday/weekend, and seasonality per address. 2) Quantify stop-likelihood using speed distributions and congestion analyses. 3) Separate local traffic from transit demand to forecast fuel, food, and subscription rates. 4) Size car wash opportunity with directional volumes, peak timing, and queue dynamics. 5) Screen expansions and closures with evidence-based, uniform rules considering traffic quality and trade area depth. 6) Build bankable 5-year revenue projections and valuations with competitive saturation data. Applying the framework: - Car wash inside c-stores relies on peak timing and queue geometry for express exteriors, favoring sites with strong local traffic and deep trade area accessibility. - Multi-region c-store M&A benefits from site-level traffic variance insights revealing clusters for tailored foodservice and staffing plans. - Regional realignments target closures where traffic and stop-likelihood are low and favor format changes where stop-likelihood and local share remain strong. Why choose C‑Site now? - Right resolution: precise directional AADT, 15-minute intervals, monthly seasonality, and driver behavior data at exact addresses. - Right coverage: continuous observation year-round. - Right validation: field tested accuracy of 91.79%. - Right outputs: local vs transit shares, congestion windows, and a feasibility study with 5-year projections and competitive saturation. As the market consolidates and c-store car washes grow strategically, operators and investors applying empirical traffic analytics with C‑Site will better decide sites to buy, expand, and close. Start with a C‑Site portfolio screen and feasibility study to convert news into timing advantage.