This guide is developed for retail operators, analysts, business development specialists, and real estate professionals to navigate them through vehicular traffic data for business strategies.
After exploring this guide, you’ll be able to:
understand how to employ traffic data parameters with the maximum efficiency for:
• location potential assessment
• sales forecasting
• store performance management
• staff planning
assess the quality of traffic data provided to your business.
Definition: AADT indicates the average number of vehicles passing by your location daily over the course of a year.
Represents: characterizes a potential customer base.
Application: initial site assessment, pre-selection forming the shortlist of potential business locations.
How to apply:
To evaluate potential customer demand at the selected location, a comprehensive estimation of AADT across all relevant directions - not only for the immediate road segment (at both travel directions), but also for adjacent roadways and highway off ramps nearby, and other important routes contributing to the traffic volume feeding the site should be included.
Each road direction —a primary road, a secondary road or a crossing road—exhibits a distinct conversion rate from passing vehicles to customers.
Recommended values:
1. AADT above 10,000 is generally considered acceptable.
2. A projected daily customer volume below 500 is unlikely to achieve a high return on investment (ROI).
3. Average location requires traffic data for 4 to 10 traffic directions.
Note:
a) Individualized conversion rate is calculated for each direction based on road network specifics;
b) Ticon’s conversion rates are derived from research and extensive field experience informed by an analysis of over 5,000 commercial locations
c) AADT levels above 4,000 can be favorable in certain cases;
d) High AADT does not always guarantee high returns. In some cases locations with lower AADT may deliver a higher return on investment (ROI). It depends on traffic flow speed and volume patterns, as well as on opportunities to construct a so-called “destination place” .
Important (!):
1) In certain cases, the combined impact of secondary and crossing roads may surpass that of a primary road in terms of conversion efficiency.
2) AADT alone does not show how many vehicles are likely or able to stop at a location.
3) AADT does not account for hourly, daily, weekly, or seasonal fluctuations in traffic, which significantly affect:
• the conversion rate of passersby into actual visitors
• the average spending behavior of customers.
Recommendation: For optimal site selection, additional traffic parameters are recommended to be considered, and they are described in the subsequent sections.
AADT values can give appropriate results for site selection only if two fundamental principles are followed:
1.2.1. AADT must be estimated for the SPECIFIC street address.
AADT for road segments over 350 ft., TMZ or XD segments may not be suitable for location potential assessment.
Explanation: IIn long street DOT, TMZ, or XD segments, which may stretch for several miles, the data is collected at one specific point of the road. AADT volumes may differ by over 500% (Fig. 1) depending on the exact location within even a half-mile segment. Therefore, the financial forecast for a convenience store, gas station, car wash, or QSR could be off by as much as 5 times.
Note: 80% of TMZ and XD segments are 0.52 plus miles; the remaining 20% are up to several miles.
Fig. 1. Probe size analysis: vehicles per day across eleven segments of a 1.5-mile road
1.2.2. AADT estimation accuracy should be properly reported
Follow these steps to make sure you receive accurate traffic data:
• request field studies from a traffic data provider. Field study should compare blind estimation results with ground truth.
• prioritize providers who are capable of presenting studies based on at least 25 locations, demonstrating low discrepancies between their own estimates and measured values within defined 35–350 ft segments. The measurements should come from verified sources.
• while examining studies, pay attention not to the “average error”, but also to the “maximum error” and corresponding confidence levels, that should not exceed 85%
• errors exceeding 20% render AADT estimations are not recommended for business forecasting and strategic planning.
Example: This is a field study involving 50 locations. The observed error per measurement does not exceed 19.4% with confidence level of 90%, and shows an average percentage error (PE) of 8.31%.
Fig. 2. Percentage Error for Annual Average Daily Traffic (AADT) estimation obtained from the field testing
1.2.3. What to avoid:
Certain data sources, while widely used in site selection, are insufficient for precise location potential assessment:
a) Mobile data (aka LBS, foot traffic, cell phone data):
Despite claims of extensive tracking capabilities, its penetration rate does not exceed 1.38% within the recorded timeframe, as revealed in a study by Ticon.
b) DOT-derived AADT estimates:
First, they provide data for large-scale road sections spanning several miles rather than defined traffic segments.
Second, measurements are conducted infrequently—typically once every few years, over a period of several days. This means, total neglecting of seasonal traffic variations, which can vary by an order of magnitude (up to a 10× difference)
While these estimates suffice for infrastructure maintenance needs, they lack the precision required for business-oriented site selection.
Therefore, given that each data type reflects only a partial aspect of traffic patterns, a comprehensive, risk-mitigating location assessment cannot be achieved by relying solely on one or two data sources.
Definition: ATV reflects the average daily traffic volume for each designated period of time, e.g. month and day of the week.
Represents:
a) traffic fluctuations within a day, a week, a month, a year;
b) peak times and seasonality (e.g., Fig. 4)
Fig. 4. ATV by day of the week and month
Application:
To evaluate potential customer demand at the selected location, a comprehensive estimation of AADT across all relevant directions - not only for the immediate road segment (at both travel directions), but also for adjacent roadways and highway off ramps nearby, and other important routes contributing to the traffic volume feeding the site should be included.
Each road direction —a primary road, a secondary road or a crossing road—exhibits a distinct conversion rate from passing vehicles to customers.
a) staff planning and scheduling:
• recognizing seasonal traffic fluctuations
• identifying correlation of weekday versus weekend traffic
• pinpointing the busiest and least busy days across each month of the year
b) revenue control;
c) store performance optimization.
How to apply:
a) for optimal staff-planning
Integrate seasonal fluctuation data to:
• adjust workforce levels by month
• reduce staff during low-traffic periods
• schedule additional personnel during peak seasons and high-traffic days to efficiently handle the increased customer load during seasonal spikes, avoiding long wait times and enhancing customer satisfaction.
Important (!): Seasonal fluctuations in traffic can differ significantly across locations, both in terms of duration (ranging from 1 to 8 months) and intensity (ranging from 15% to 500%). Though, for exact location these fluctuations usually do not change much from year to year.
b) for revenue control and store performance optimization
By consistently analyzing historical traffic data, businesses can systematically monitor store performance and identify objective factors contributing to revenue fluctuations. If traffic volumes with optimal speed distribution increase without a corresponding rise in revenue, this may signal an underperformance at the managerial level.
To project expected store performance at a given location, apply:
• daily and monthly traffic fluctuation data
• information on traffic speed and volume distribution
• conversion rates.
Definition: speed and volume distributions show how many vehicles pass a location and how fast they’re travelling, captured every 15 minutes and grouped by speed ranges (e.g., 0–15 mph, 15–30 mph).
Represents: drivers’ likelihood and ability to stop at the selected site
Application: comprehensive sales projection
Note: In terms of driver behavior and the rate of traffic conversion to visitors, two distinct traffic patterns are distinguished:
• Transit traffic refers to vehicles moving at relatively high speed—typically as a result of road network configuration. The threshold for what constitutes 'high speed' varies by road type. These specifics generally prevent drivers from making stops, thereby limiting potential customer conversion.
• Non-transit traffic, by contrast, occurs where the road network enables slower vehicle movement, allowing for potential stops. This pattern is associated with a significantly higher driver-to-customer conversion.
How to apply:
In addition to conventional traffic indicators such as AADT and ATV, use speed/volume distributions for developing reliable financial forecasts. Traffic capture rates and average check values vary significantly across different times of day. Apply a corresponding average check and traffic-to-customer conversion rate to each time interval, based on the speed & volume distribution metrics.
Recommended values:
For the development of a robust financial forecasting model, over 35,000 AADT estimations are normally incorporated along with detailed traffic behavior data.
Note: This requires monitoring 96 distinct time intervals throughout each day, continuously over the course of 365 days per year. Naturally, each of these types exhibits a distinct conversion-to-customer rate.
Such an approach promises the most comprehensive and reliable sales forecast when selecting a potential location and is recommended for use as a benchmark by Ticon.
Concluding Observations
The ability to perform detailed, large-scale computations—once reserved for large enterprises with extensive analytics resources—is now within reach of small, family-owned businesses.
With Ticon’s advanced traffic data analytics, sales forecasting, and feasibility modeling tools, even small-format retailers can make successful, data-driven location decisions. This empowers business operators to identify sites with the highest revenue potential, where even a small difference in potential revenue can make a significant financial impact.
