Traffic counts are counts of vehicular and pedestrian traffic, which is conducted along a particular road, path, or intersection. These are metrics that are essential to retailers and real estate professionals alike for their decision making as it helps to provide key information regarding the flow of traffic to, from, and nearby to a subject site.

There are two types of traffic counting methods that PiinPoint offers:

Traditionally, traffic and pedestrian counts have been sourced from municipalities and government organizations such as the Department of Transportation. While many larger municipalities and organizations conduct regular, statistically representative studies, other smaller municipalities and organizations conduct such studies infrequently or not at all.

Furthermore, many traffic studies are based on small sample periods such as a single day or maybe only part of a day and that data is extrapolated as if it were representative of typical traffic volumes of that area.

Nevertheless, this data is a valuable location analysis tool, particularly when assessing the busyness of two sites in the same city. To learn more about PiinPoint’s traditional traffic feature click here.

Mobile Location Data (MLD) is a game-changer when it comes to tracking daily traffic volumes. PiinPoint’s custom vehicle traffic query allows you to look at modelled AADT (Average Annual Daily Traffic) and pedestrian counts for any major road segment in Canada & the US.

PiinPoint has partnered with NEAR, and provides traffic counts by drawing insights from millions of mobile devices each month across North America. NEAR is a partner that upholds our values of transparency and accuracy, as well as positions us well for future product improvements and offerings within our MLD features.

Using advanced machine learning methodologies, PiinPoint is able to extrapolate on the sample of anonymous mobile traffic data to represent 100% of the North American population. PiinPoint takes into account demographics, population statistics, and observational data to ensure accuracy.

To learn more about MLD Traffic and watch it in action, click here.

Our traffic counts are distinguished by their respective travel velocity over a period of time. We have time stamps on each observation which allow us to make assumptions about the type of travel the mobile device was doing based on the interval between each ping. While it is far more nuanced, very simply put, higher velocity pings could be attributed to vehicle activity and lower velocity pings as pedestrian traffic.

Traditional municipal traffic counts are conducted by local municipalities and/or government organizations, and are often based on intersectional traffic data that is only conducted at their sole discretion. It often proves to be difficult to truly rely upon.

MLD Traffic counters these issues by looking at virtually any road segment, being updated annually, and taking into account directionality of the traffic flow. Using MLD for traffic analysis allows for a more apples-to-apples comparison, higher accuracy and focused traffic count analysis to take place.

Sometimes, the counts generated through MLD appear to be either higher or lower than those using the traditional municipal counts. This happens for multiple reasons such as directionality, how many roads are feeding into the count, how long of a road segment it is, one way vs. two way street, etc.

Given the different methodology of data collection and calculation in order to generate traffic counts, there may be certain discrepancies. However, rest assured that multiple concerns have been taken into account and PiinPoint is able to generate traffic counts using mobile location data with significant accuracy.

Sometimes, it happens that the road you are looking at has a median separating the road in two. Even though it might technically be the same road, PiinPoint’s road classification system treats roads with medians as two one-way road segments in order to increase accuracy and make it easier for the user to understand.

We cover the following road segments:

The different colors on the road segment indicate the different sides of the road and help you understand the directionality of the highlight road segment.

PiinPoint’s road network data is derived from the road network from Open Street Map (OSM). With this dataset, there are many attributes that describe each road such as speed, number of lanes, road hierarchy (highway, primary road, secondary, etc.), and more. A single road segment is typically from one intersection to the next intersection, with an intersection being when a road makes contact with another road (an overpass would not count as an intersection because the road goes under it). However, wherever there is a change to one of these attributes, the road is split where the change happens. This leads to the final road segment length, and why you may see many different road segments between intersections.

NEAR gets the data from a variety of sources, asking users to enable access to their location settings or make use of cookies. We choose to partner with NEAR because of their commitment to transparency and education in their approach to MLD which allows us to integrate the data into PiinPoint in an anonymous and safe way.

MLD is a dataset that is enabled only through consumers consenting to share their location. The general impression towards location-enablement is contentious, particularly regarding how much of an effort apps make to ensure the user understands what they are opting-in to. PiinPoint takes additional precaution in the delivery of MLD. First, the data is anonymized. PiinPoint ensures the data is de-identified, and where the observations have only a device ID, disconnected to any individual user. There is no information collected regarding the individual, contrary to popular assumption that the insights include information on the individual’s usage on the phone, what apps they have, or their contact information. Second, the data is aggregated for display or understanding in PiinPoint products and services, so there is no way you can look at one individual - even a de-identified one - and their unique activity. Data is aggregated to either a Dissemination Area (DA) or Road Segment.