Each year, 1.19 million people worldwide lose their lives in traffic crashes. Road incidents also stand as the primary cause of death among children and young adults, highlighting a critical issue. Traffic managers and road operators diligently strive to mitigate these risks. They employ various methods, from routine road upkeep and infrastructure upgrades to crafting effective policies and ensuring clear and effective signage and signals.
However, while most traffic-related fatalities are caused by factors like speeding, distracted or drunken driving, or not wearing seatbelts, thousands of lives could be saved if emergency services reached the crash site earlier. Research by The Lancet shows that improving emergency response services and trauma care in low- and middle-income countries could prevent an estimated 200,000 deaths each year.
Accessing modern traffic management solutions such as automatic incident detection (AID) systems allows traffic controllers to monitor road conditions in real time and respond swiftly to accidents. These tools continuously scan road activity, instantly flagging incidents like collisions or hazards. From a traffic safety perspective, they offer proactive monitoring, enabling quicker responses to emergencies and potentially reducing congestion-caused risks. All that’s possible thanks to artificial intelligence.
Exploring the tech behind real-time, automated traffic safety systems
The goal of automatic incident detection systems, or AID for short, is to identify and report incidents live without relying on human intervention. Thanks to automation, AID systems such as GoodVision Live Traffic enable timely incident detection and response to hazardous events, minimising their impact. Beyond their applications in mobility and transportation, AID solutions are employed across other sectors, e.g., security and industrial operations.
No AID without AI
Before automatic incident detection (AID) systems were developed, incident detection and response relied largely on human observation of camera feeds. While this approach had the benefit of high accuracy over early AI detection algorithms, there are only so many feeds one human controller can monitor at any time. This limitation forced controllers to prioritise events that seemed most serious and could even lead to some going entirely unnoticed.
With advances in AI, computer vision, and machine learning—enabled by increased computing power—modern approaches have become significantly more sophisticated, allowing for faster, more confident, and highly accurate detection of specific road events.
Based on these and other records, the algorithm can decide whether a road event should be classified as an incident. This ability makes AI the fundamental component of any automatic incident detection system, enabling AID to function independently and reliably.
How does real-time automatic incident detection work?
From data sourcing to processing, AID systems require a set of different technologies to function properly. Hence, the best way to understand how automatic incident detection works is to examine all its parts step by step.
- Data source: First, an AID system needs access to real-time traffic data. The source of these records can vary between solutions, but the two most common are traffic sensors and cameras. The real-time data will then be processed by AI.
Due to its visual nature, scalability, precision, and ease of deployment, we believe that video is the best traffic data format. Also, video-based AID systems like GoodVision Live Traffic allow traffic management centres to collect additional parameters such as licence plate numbers, vehicle classification, speed, traffic density, movement patterns, and even pedestrian activity—providing a comprehensive view that supports accurate, real-time traffic management and safety interventions.. - AI algorithm: Machine learning and deep learning algorithms trained on large datasets of real traffic videos accurately identify and distinguish vehicles and pedestrians. These algorithms continuously analyse the data collected by sensors and cameras in search of anomalies that may indicate an incident. A critical factor in achieving this accuracy is the quality of the dataset used to train the neural networks. Unlike publicly available datasets, which often lack the precision needed for real-world traffic management, proprietary datasets of high quality are essential for delivering the level of accuracy required for reliable incident detection.
In GoodVision Live Traffic, users can precisely specify the detection zone by drawing virtual lines on the road section scene view. The algorithm can be set up to capture only movements within these zones and selected parameters such as speed, vehicle class, movement type, and more. - Data processing unit: A computer the algorithm runs on. Processed data is instantly and continuously sent to the traffic control software.
Latency and processing speed are essential for real-time automatic incident detection, but video streams require high bandwidth to upload and send. GoodVision Live Traffic uses on-site edge processing units paired with each camera to minimise processing time. An Internet connection is required only to upload processed, lightweight data to the user interface, and all data are anonymised to ensure a high level of privacy for road users. - User interface: Many automatic incident detection platforms display the collected traffic data visually or keep users up to date through automated alert systems. This allows traffic controllers to easily stay on top of all road events without the need to monitor dozens of camera streams.
GoodVision Live Traffic prevents operator overload by providing customisable dashboards and alerts. Users can tailor their interface to focus on the specific parameters and scenarios they need to monitor, setting up alerts for key events. This information is displayed in clear, glanceable graphs, ensuring that critical insights are easily accessible without overwhelming operators.
In GoodVision Live Traffic, traffic controllers can set up the interface to display the real-time data they need the most.
Automatic Incident Detection in traffic safety: what are the benefits?
Artificial intelligence is a crucial improvement of automatic incident detection mainly because it offers benefits similar to those of other algorithm-based solutions: efficiency and accuracy. What tangible benefits do these features translate to when traffic safety is concerned?
- Real-time traffic monitoring: AI enables live traffic monitoring capabilities on a much larger scale without compromising on accuracy. Besides expediting incident detection and response, real-time capabilities help TMC operators predict and manage congestion and dangers faster and more confidently.
- Automated alerts: AID automatically sends alerts to traffic management centres as soon as an incident is detected, allowing for near-instant confirmation and response. All identified incidents can be reported to ensure that they are addressed in a timely manner.
- Faster response times: Since traffic data is collected, processed, and uploaded to the user interface in real time, traffic controllers can react to incidents almost instantly. With camera-based solutions, their decisions are also supported visually by the video material.
- Detection accuracy: Unlike human traffic controllers, AI doesn’t get tired, bored, or distracted and can operate around the clock with consistent accuracy. Thanks to that, humans can focus on more demanding or nuanced cases.
- Cost efficiency: The initial deployment investment bears the brunt of the costs related to AID implementation. In the long run, automatic incident detection reduces manual labour and boosts efficiency, allowing for wider-scale deployment and saving funds that can be spent on other traffic safety efforts.
- Scalability and coverage: AID systems can monitor multiple locations at once and easily integrate with existing infrastructure. This makes them perfect for large-scale deployments such as highway or city-wide traffic management projects.
- Synergy with smart city solutions: Real-time data collected by AID platforms can be used by smart traffic management systems and infrastructure such as connected traffic lights, toll gates and gantries, and parking systems. With access to continuously updated records, all smart city transportation solutions get more accurate, contributing to overall better traffic safety.
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Real-time traffic data collection and monitoring systems are a staple of smart city infrastructure.
All these benefits make AID solutions an essential tool in fighting and preventing traffic hazards. As such, implementing automatic incident detection systems is an important part of cities and communities' efforts to pursue commitments and initiatives like Vision Zero, which aims to completely eliminate traffic casualties.
Traffic safety in any road environment
The ability to distinguish between various vehicle classes and recognise their behaviour allows camera-based AID systems to be applied in many different road environments, including roads reserved for motorised traffic and multimodal road sections. No matter the context, automatic incident detection can identify dangerous events and notify relevant services.
Highways
Due to high traffic speeds, highways are among the most dangerous road environments. Additionally, the fast pace and large distances to monitor make tracking all events exceptionally challenging. In this context, AI-assisted real-time video analytics are particularly handy, automatically identifying anomalies in traffic patterns that may indicate accidents.
Another benefit of video-based automatic incident detection is its high scalability, allowing it to cover longer distances and more hazardous spots along the motorway. These factors contribute to shorter response times and improved highway traffic safety.
Cities
Urban areas pose a very different set of challenges than highways: traffic is slower but denser, making incidents more common, especially at and near intersections. Additionally, accidents often involve the most vulnerable road users, like pedestrians and cyclists. Visibility can also be an issue, especially in narrow streets or junctions where drivers may not see other vehicles or pedestrians approaching in time.
Last but not least, there’s congestion, which has a two-pronged effect on traffic safety. Firstly, the above-average car density during traffic jams increases incident rates. Secondly, congestion may slow down emergency services trying to reach the site.
Considering all these dangers and the number of potential incident hotspots to monitor, real-time automatic incident detection and response are necessary in large cities. AID is a cornerstone of smart city solutions, assisting urban planners and traffic managers in enhancing traffic safety.
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High concentration of intersections and dense, multimodal traffic make cities prime candidates for AID implementation.
Residential areas
Calmer, residential neighbourhoods, where traffic is usually less dense than in bustling city centres, are often overlooked when AID is concerned. Though less intense, traffic in residential zones is pedestrian-heavy and often includes particularly vulnerable road users like children playing near roads and driveways.
In residential zones, real-time video analytics can track dangerous activities such as speeding, supporting proactive law enforcement and contributing to higher traffic safety.
Commercial zones and transportation hubs
Shopping malls, airports, train stations, supermarkets, and other commercial and logistical hubs have one thing in common: they need large parking lots to accommodate thousands of cars transporting people to and from these facilities daily. This intensity makes minor collisions common; although parking lot traffic tends to be slower, it often involves pedestrians.
Implementing AI-based automatic incident detection systems in parking facilities has several benefits besides faster incident detection and response times. ANPR (automated number plate recognition) capabilities can help identify and track offenders and prevent hit-and-run accidents. AID can also manage traffic flow and predict when incidents are more likely to occur, supporting proactive traffic safety efforts.
Your AID in traffic safety
When human lives are at stake, every second between the incident and the arrival of emergency services can be critical. With near-instant reporting, real-time automatic incident detection systems help minimise this time as much as possible while maximising traffic control efficiency.
Fast, accurate, and easy to customise and integrate, Live Traffic is a complete real-time AID solution. Keen to find out more? Tell us about your traffic safety challenges and solve them with GoodVision.