Recent travel stop openings, remodels, and technology investments in convenience and fuel retail highlight growing operational pressures on freight corridors, interstate junctions, exit ramps, and urban arterials. While new travel stops can add truck services, they can also alter turning demand, increase truck volume at ramps, and reveal signal timing weaknesses. Retailers adopt AI for customer movement insights, and cities need similar empirical traffic network analyses. Urban mobility challenges are not only about high traffic volumes but about identifying where delay occurs, why it persists, and prioritizing interventions. Congested corridors often consist of localized bottlenecks such as oversaturated turns, poor signals, ramp junctions, truck-heavy approaches, or seasonal spikes. Ticon’s traffic analytics platform quantifies these distinctions. Volume alone is insufficient for bottleneck identification. Ticon's study analyzing over 196 million data points across 126 road sections found that reductions in traffic volume often yield disproportionately small reductions in delay, especially on signalized urban roads. Persistent delays despite lower volumes indicate issues like signal timing, lane assignments, intersection geometry, or turning imbalances. Cities should assess traffic control quality before implementing restrictive or large-scale solutions. Ticon’s approach utilizes multiple data sources—GIS, connected vehicle data, GPS, events, detectors, demographics—to achieve over 97% road coverage and 100% time coverage, with high spatial resolution down to 35 feet segments and time intervals as short as 5 minutes or 15 seconds. Accuracy is critical for reliable bottleneck ranking. Ticon's AADT estimations have a median average percentage error of 4.78% and maintain errors within 20% at 90% confidence, validated with over 1,200 directional counts across multiple states. Intraday traffic volume estimations have typical errors between 5-20%, with 15-minute flow volume estimates showing median errors around 11.24%, comparable to pre-calibrated video detectors. Bottleneck diagnosis requires more than identifying low speeds. Low speeds may result from various factors such as traffic calming, signal progression, incidents, pedestrian activity, or intersection saturation. Ticon examines volume, speed, delay, turning demand, traffic composition, and temporal fluctuations comprehensively. For instance, a new travel stop near an interstate may increase truck turning movements, alter queues, affect left-turn phases, and shift traffic balances. Bottlenecks arise when actual turning demand mismatches existing timing plans designed for older patterns. Ticon Turns estimates turning movements every 15 minutes over 24 hours, which can be aggregated by day, week, month, or peak periods, allowing targeted interventions such as timing adjustments instead of costly widenings. Vehicle classification is important; truck-heavy approaches differ from car-dominant ones as trucks need longer clearances and turning space. Separating truck and car patterns is essential near interstates, distribution centers, ports, and industrial areas for accurate capacity assessments.