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.
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.
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.
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.
In GoodVision Live Traffic, traffic controllers can set up the interface to display the real-time data they need the most.
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 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.
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.
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.
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.
High concentration of intersections and dense, multimodal traffic make cities prime candidates for AID implementation.
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.
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.
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.