In 2026, major commercial real estate transactions and retail expansion strategies highlight the importance of evidence-based site selection. Examples like the $425 million acquisition of The Shops at Skyview in Queens and Aldi's plan for 180 new grocery stores in the US demonstrate the stakes of choosing optimal locations in transit-rich urban and suburban areas. Additionally, federal and industry focus on freight mobility and truck parking, such as the FHWA’s Jason’s Law Truck Parking Survey, reflect diverse mobility challenges for consumer and logistics networks.
Fine-grained mobility insights from comprehensive historical traffic data are essential. Ticon’s platform applies advanced methodologies for traffic data collection, monitoring, and analytics to enhance location-based decision making.
Traditional site selection relied on intuition or simple traffic counts, insufficient for today's dynamic consumer behavior. Retail chains now adopt AI and multi-source traffic analytics to overcome gaps like temporal limitations (traditional counts capture less than 2% of the year), spatial inaccuracies (averaging over long segments causes up to 91% error), and lack of behavioral segmentation (distinguishing transit vs. stopping behavior).
Ticon triangulates data from sensors, video, radar, GPS, and historical sources. Algorithms analyze variables including speed, acceleration, vehicle class, lane distribution, and weather to identify stop-prone traffic indicative of shopping behavior. This approach marks customer interception potential scientifically, benefiting operational excellence.
Empirical evidence shows sophisticated traffic analysis improves site viability, with a recommended minimum average daily traffic (ADT) of 25,000 vehicles coupled with stop behavior analytics reducing decision risk. For example, a convenience store chain used Ticon’s data plus demographics to select sites with high ADT and evening peak flows. Post-launch monitoring optimized labor and inventory, boosting sales beyond projections.
Historical and monitored traffic data also aid public infrastructure and Intelligent Transportation System (ITS) projects, enabling quantitative validation of interventions. Year-round multi-source data reduces sales forecast error by up to 40%. Well-maintained detector systems achieve ±2% to 7% ADT accuracy versus up to 40% deviation in short-duration counts. Public-private studies reveal private truck stops supply ~90% of parking capacity, underlining the need for corridor-specific traffic data for planning.
Ticon’s combination of historical records, real-time monitoring, and predictive analytics is transforming mobility improvement for businesses and agencies. Address-specific coverage and behavioral segmentation allow adaptable solutions optimizing labor, logistics, and infrastructure investment. The future competitiveness rests on precise, behaviorally informed, continuous traffic analytics enabling timely, smart decisions for retailers, investors, and urban planners.
Contact Ticon’s analytics team for detailed technical insights or customized analytics projects.
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