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Vehicle counts and classification data are powerful sources of traffic data. By understanding traffic volume, departments of transportation, public agencies, and private organizations can make effective changes to improve how our cities move.
From transportation planning and traffic management to infrastructure development, the practical applications of traffic counting span far and wide. Before we discuss them, however, we must understand the techniques, methods, and sources associated with collecting traffic count information. While there’s no right or wrong way to do it, those wishing to tap into this valuable data source should consider the benefits of each approach.
But first, the basics: what is traffic count?
Traffic count refers to data collected on the volume and type of vehicles on a given roadway. This is split into vehicle traffic count and classification data. The former tells us how many vehicles are using a roadway at a particular time. While the latter tells us what types of vehicles are present, such as passenger cars, trucks, and buses.
Researchers can then gain an understanding of daily traffic figures and annual average daily traffic (AADT) – which is the total volume of vehicle traffic on a highway or road per year, divided by 365 days. With this information, researchers and decision makers can assess transportation needs and evaluate traffic patterns to support data-driven decision making in urban planning.
Approaches range from traditional to technical. Although technologically advanced techniques often deliver more accurate data more efficiently, they tend to be costlier than manual methods. Let’s explore these further:
A human traffic counter physically records the number of vehicles and classifications they can see on the roadway. This could be done in real time on the roadway, or after the event by watching a video recording.
No installations or connectivity are required, making it appropriate for rural areas. Overall processing costs are cheaper, especially for low-volume roads.
On-site counting raises safety concerns and manual labor is high, which can be costly, prone to human error, and inefficient for high-volume roadways. The research will most likely lack speed data and more specific vehicle classification.
These are placed perpendicular to traffic on the road surface, so that when a vehicle drives over, a signal is sent to external software registering the count. Classification and speed data is collected by registering axle count and spacing through a second tube.
This method is trusted, easy to execute, cost effective, and in many cases, agencies will already possess the equipment.
Accuracy can be affected by high volumes, slow-moving traffic, poor weather, or parking areas. The equipment is prone to damage and degradation, and can be unsafe to deploy/retrieve.
An electromagnetic field is created by a loop wire installed into the pavement. This passes on a pulse to a controller to register a vehicle when it passes through. Double loops can classify vehicles and collect speed data too.
Inductive loops are trusted, likely to pre-exist with users, cost effective, and can be used in all lighting and weather conditions.
They’re prone to water and street maintenance damage, while installation/maintenance disrupts traffic and is potentially unsafe. Low-metal vehicles like motorcycles may be hard to classify.
Unlike manual count video footage, a recording device and computer will count and classify moving vehicles, while an external piece of software analyzes and processes the data. Headlight detection is used for traffic recording at night.
Little manual labor is required, traffic disruption and safety concerns are eliminated, and accuracy is high even on high-volume roadways.
This method is more expensive, while classification data can be compromised at night or by poor weather. Installation is less flexible and there are privacy concerns.
Side-firing and forward-firing radars – or automatic traffic counters – fire beams across or along the roadway to detect counts and classify moving vehicles based on vehicle length measurements.
Radars can count high volumes with high classification accuracy in all conditions. They use little power, require no manual labor and pose no safety concerns.
They’re expensive, less effective in congestion, and not as well-known as other methods.
The cost, convenience, and effectiveness of different techniques are key considerations when deciding how to get traffic counts. And for many organizations, limited resources and budgets can get in the way of ideal research scenarios.
Take VID for example. The installation location and weather conditions have to be optimal, as it requires clear visibility and the use of pre-existing infrastructure. But what if the specific research area is known for rainy conditions and lacks the right roadway fixtures? You may have to use less accurate techniques or relocate to a different area entirely.
This only scratches the surface of the considerations that government agencies, private companies, online databases, and other sources of traffic counts data must make.
With clear visibility on how traffic behaves, decision makers can greatly improve transportation planning, traffic signal optimization, road capacity analysis, and congestion management. This will ensure the movement of people and goods is safer and faster, as well as more comfortable, convenient, economical and sustainable. And that’s exactly why ETALYC used Wejo’s connected vehicle data…
Their AI-based technologies enable organizations and traffic managers to analyze and optimize traffic flow. By comparing Wejo’s real-time vehicle journey data with historical data on traffic trends, they identified poor-performing traffic signals. This enabled them to recommend adjustments to improve performance. As a result, they reduced rush-hour travel times and side-street delays, leading to cost savings of $2,317 per day.
By utilizing emerging AI-based technologies and machine learning, we can significantly streamline decision making with traffic data-driven intelligence. This software enables instant capturing, monitoring, and comparison of traffic and infrastructure data, which can then be translated into easy-to-digest and interactive displays that fast-track the data capture > analysis > action process. Now, what if that data could be even more granular and accurate? Enter: connected vehicle data.
Connected vehicles provide data straight from the source. With highly accurate traffic data available in real time and over time, we can gain instant access to the location, speed, and classification of vehicles in a given area. With the right solution, public and private organizations can select what they want to access in line with their requirements. And with no additional hardware, no field work, and no manual labor required – it’s cost effective and highly impactful.
It’s still early days for these techniques, meaning policymakers and funding distributors may need more convincing around areas like effectiveness and security. New methods mustn’t compromise privacy laws – something Wejo is right behind as we drive the smart mobility revolution forward with our award-winning traffic intelligence solutions.
Wejo is a SaaS provider that offers public and private organizations an up-to-the-minute view of traffic conditions and safety incidents through intelligent, aggregated data from millions of connected vehicles.
To discover how we can help you make a real-world impact on road safety and congestion within your community, get in touch today.