Traffic Data Collection: Methods, Analysis, and Applications
Delve into the world of traffic data collection and explore various methods, analysis techniques, and practical applications. Learn how Wejo's expertise empowers urban planning and traffic management with data-driven insights.
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In 2022, the typical American driver lost 51 hours to congestion. Thatâs almost an hour a week. Reducing this figure and achieving efficient and effective traffic flow on our roadways requires readily available, up-to-the-minute information about traffic movement on freeways. This information is broadly known as traffic data, and the process of gathering it is called traffic data collection. But what is traffic data collection?Â

Here, weâll explore the various traffic data collection methods available to urban planning teams, how this data analysis is applied to real-world scenarios, and the advancements weâre seeing in road traffic data collection methods and applications. But first, letâs take a look at why traffic data collection is so importantâ¦

Defining Traffic Data Collection and its SignificanceÂ

Traffic data collection is a highly useful tool that offers valuable insights for traffic management and infrastructure developments. With the help of traffic data, traffic engineers can make informed decisions based on details such as traffic volume, types of vehicles or visitors on the road, the speeds those vehicles are traveling at, and more.

This data is imperative to traffic engineers for a number of reasons â including identifying transportation needs, evaluating the efficiency of traffic network operations, establishing traffic patterns, and driving data-driven decision making in urban planning.

Gathering the Insights: Traffic Data Collection TechniquesÂ

There are several road traffic data collection methods and applications, each with its own set of benefits and drawbacks. Below, we explain some of the most common traffic data collection methods, and how successful they tend to be.

Manual counting

Manual traffic counts are conducted by an individual positioned roadside, recording passing vehicles on a form. Its manual fashion distinguishes it from machines that record passing vehicles automatically â known as âautomatic traffic counts.â

PROS:

Simple set-up â with no installations required for on-site counting. Itâs also inexpensive to process low-volume roadways, and there are no network or connectivity concerns â especially in rural areas.

CONS:

High manual labor costs for employment of counters, plus concerns around the threat to countersâ personal safety. High-volume and multi-way roadways can also impact efficiency, while human error impacts accuracy.Â

Pneumatic tubes

For traffic data collection using pneumatic tubes, one or more rubber hoses are stretched across the road â with one end connected to a data logger, and the other sealed. Each time a pair of wheels makes contact with the tube, air pressure within the tube causes the data logger to activate â recording the time of the event.

PROS:

Easy to install and retrieve, as many agencies and departments already have pneumatic tubes on hand. Also a tried, tested, and trusted method thatâs inexpensive to run.

CONS:

Safety concerns for teams installing or retrieving tubes â plus restrictions on where tubes can be placed (not in parking zones). Also less effective in adverse weather conditions, and can quickly deteriorate.

Inductive loopsÂ

Inductive loops are installed beneath the surface of a roadway â typically in a circular, square, or rectangular shape. They measure changes in the field when objects interact with them. Each time a driver passes over a loop sensor, the loop field changes, indicating the presence of a vehicle.

PROS:

Another trusted method of traffic data collection. Also cost effective â in some cases, inductive loops can even be pre-existing. Not impacted by changes in lighting or weather conditions. Double loops can detect speed information too.

CONS:

Installation periods can disrupt traffic flow, and poor installation can lead to inaccuracies in the data. Water penetration and street maintenance can cause damage to the set-up. Some low metal vehicles â such as motorcycles â arenât always detected.

Video detection

Video detection combines one or multiple cameras, a microprocessor-based computer, and software to capture and analyze roadway video footage. The cameras differentiate between vehicles and provide classification, and the count and classification can then be analyzed by the software.

PROS:

No disruption to traffic caused, and no additional manual labor required following installation. Also a more effective option for high-volume or heavily congested roadways â plus results are much more accurate in comparison to other methods.

CONS:

Cameras can be expensive â especially when you account for monitoring and replacement. They also occupy a lot of space, so it may be difficult to set them up in tighter areas.Â

GPS tracking

Global Positioning System (GPS) tracking relies on a satellite-based radio navigation network that continually transmits time and location information. GPS data can be retrieved from a vehicle using a satellite navigation system or a mobile phone, alerting traffic management systems, in real time, of a carâs location, speed, and direction of travel.

PROS:

No special hardware or software is required, nor any specific infrastructure on the road, and itâs inexpensive as a result. Provides real-time monitoring of a vehicleâs movements.

CONS:

Signals can become blocked by trees, tunnels, and mountains, so theyâre not always reliable. Sophisticated algorithms are required to extract and treat high-quality data before it can be used.Â

From Data to Action: Applications of Road Traffic Data Collection

Below, weâve provided a brief overview of traffic data collection methods, along with real world applications.Â

Transportation planningÂ

When planning upgrades to the network, traffic engineers rely on traffic data collection and analysis to see exactly how the roads are being used and clearly map out more efficient road systems.

Real world application: National transportation planning and engineering firm DKS Associates was tasked with advising Orange County Transportation Authority on its most recent corridor evaluation. They leveraged Wejoâs smart mobility solutions to visualize every point of the corridor at any given time. Wejoâs data offered a more accurate and reliable picture than traditional methods, allowing them to complete a large-scale analysis on a short timeline.

Traffic signal optimization

Traffic signal timing is one of the fastest and most cost-effective ways to curb congestion, and traffic data collection equips traffic engineers with accurate data on the dynamic behavior of traffic streams to inform the best possible timings of a traffic signal.Â

Road capacity analysisÂ

Road capacity analysis â in other words, calculating the number of vehicles in the traffic network â is critical for developing successful congestion mitigation strategies. Traffic data collection provides a more holistic view of the urban transportation network.

Real world application: The Consumer Data Research Centre at the University of Leeds was working on a project to build and calibrate a baseline simulation of mobility and activity of the Northern UK region. Traditional methods meant they were routinely missing data, but with Wejoâs help, they analyzed thousands of vehicle movements over the course of a month â uncovering fresh insights to feed into their urban simulation.

Congestion management

Effective congestion management can only be achieved through understanding why congestion occurs. Traffic data collection and analysis can be used to calculate traffic volume at different periods of the day, identifying trends and facilitating demand management.

Making Sense of the Data: Traffic Data Analysis TechniquesÂ

Advanced technologies are increasingly bolstering traffic data collection methods and analysis â in particular, artificial intelligence (AI), machine learning, and connected vehicle data are revolutionizing the way traffic is managed in urban environments.

AI analyzes real-time data gathered from vehicles to understand traffic volume, behavior, and trends â allowing decision makers to optimize traffic flow and maximize safety.Â

Traffic flows can also be predicted using machine learning, drawing on different types of data such as mapping data, vehicle position and speed, weather conditions, plus additional information regarding road conditions â including sports events, organized protests, or road accidents.Â

Both methods require accurate, up-to-the-minute traffic data â in other words, connected vehicle data. This is information generated directly from vehicles in real time. Itâs one of the most reliable means of traffic data collection, and when paired with AI, itâs a highly effective way of understanding historical trends and predicting future driver behaviors to improve transport operations. This is just one of the many ways connected vehicle data is revolutionizing our understanding of the roads.