Scott Davis, VP UK & Ireland
As the demands on transport networks grow, so too does the need for smarter, proactive safety and security solutions. While dangerous driving offences in England and Wales slightly declined to 4,858 in 2024, incidents remain high, driven by factors such as speeding, distraction, and poor weather conditions. On the railways, trespassing, accidents, anti-social behaviour, and suicide attempts remain significant concerns for senior leaders. Advances in AI and intelligent video surveillance are coming to the fore as powerful tools to address the challenges faced by transport leaders. By leveraging AI, automation and advanced video analytics, leaders and operators benefit from enhanced situational awareness, rapid detection of high-risk behaviours such as loitering, proactive crime and accident prevention, and data-driven future planning.
AI capabilities are advancing
AI in video surveillance and traffic management have progressed rapidly in recent years, shifting from basic motion detection, with many false alarms, to highly sophisticated and accurate real-time analysis powered by deep learning. AI can now accurately identify different vehicle types (cars, trucks, buses, motorcycles, and bicycles), licence plates, plus attributes such as vehicle colour. Operators can monitor vehicle behaviour across multiple lanes, including detecting wrong-way driving or illegal turns. These real-time insights help operators respond swiftly to potentially dangerous incidents before they worsen. Likewise, in sensitive zones like bus stops or intersections, AI can flag stationary vehicles that pose a safety risk, allowing for rapid intervention before secondary accidents occur. Beyond detection, AI also supports intelligent traffic management, with features such as queue analysis, speed monitoring, and congestion alerts. These insights empower transport authorities to optimise signal timing, reroute traffic in real time, and plan infrastructure improvements based on data.
One major leap forward has been the integration of edge AI capabilities directly into cameras. This allows data to be processed locally, enabling faster responses, greater privacy, and reduced bandwidth and storage requirements, making edge devices particularly valuable for assets on the move, such as buses or trains, or remote locations such as level crossings.
Improving safety across transport
Transport networks and hubs are dynamic, complex, and high-pressure environments where safety and security must be addressed from multiple angles and risks. From entrances and terminals to restricted zones, logistics areas, on-board vehicles, and roads or railways, every element of the network requires constant monitoring. Security threats range from theft, vandalism, and anti-social behaviour to more serious risks like violent incidents, trespassing, and terrorism. Operators must also be prepared to respond to medical emergencies, locate missing individuals, manage large crowds, and minimise losses across freight and logistics operations. To meet these demands, organisations rely on a wide array of tools, including video surveillance, access and perimeter control, communication platforms, and connected sensors. Increasingly, AI is becoming the unifying force behind these technologies, enabling faster, smarter responses and greater operational efficiency. Here are five ways AI is already transforming safety and security across the transport sector.
1. Real-time monitoring and alerts
AI-powered video systems provide round-the-clock monitoring of transport environments, automatically identifying and alerting operators to unusual behaviour, safety incidents, or operational issues that require their input. These accurate AI algorithms reduce false alarms caused by irrelevant motion, such as wildlife or moving shadows. With AI working in the background to continuously scan for emergencies or unusual events, operators can focus on other activities and only respond to alerts that require their attention. This improves team efficiency and output, while also reducing operator fatigue from false alarms and continuous monitoring.
AI can improve response times, and is particularly valuable for serious risks such as an individual falling onto a railway track, trespassing on a level crossing, a fire starting in a bus stop, or a vehicle driving dangerously across lanes of traffic. Vandalism, such as graffiti tagging or the sound of windows smashing, can also be picked up by AI algorithms, allowing ground teams to apprehend vandals and prevent assets from being damaged. AI can also detect large crowd formations, which could signal anti-social behaviour or bottlenecks that will impact health and safety.
Dynamic privacy masking, when an object is dynamically masked with colour or mosaic overlay, ensures personally identifiable information on passengers is removed while retaining crucial information for operators on everything happening on the ground and in vehicles. Full video can be unlocked by authorised individuals for post-event investigations if needed.
2. Ramping up situational awareness
Transport operators must assess what’s happening across wide and often complex environments, and AI significantly sharpens this visibility. Cameras can be their crucial ‘eyes on the ground’ to identify objects of interest and their attributes, for example, a vehicle’s make, model and colour along with license plate details, which can help direct ground teams to the right suspects. It can also assist with finding missing people quickly, searching for someone based on their gender, colour clothing, or if they have glasses or a handbag.
3. Proactive city planning
By analysing trends in video footage over time, AI systems can provide valuable insights to inform city planning. AI insights into road or building usage with heat mapping alongside vehicle type, numbers and breakdowns can help with road layouts, signage, and resources such as ground patrols and maintenance. Security can be increased around areas where a lot of theft or trespassing is occurring, and routes can be increased or widened in response to traffic numbers. Visualisations, such as heatmaps and charts, can make this easy to understand for senior leaders to take proactive action. This data-driven approach enables more strategic allocation of resources and reinforces public safety without relying solely on reactive measures.
4. Connected systems
Connected AI-powered video surveillance and protocols like MQTT (Message Queuing Telemetry Transport) are transforming how operators manage safety, efficiency, and response in cities. Cameras no longer just observe, but interact with a wider ecosystem of sensors, lighting, signage, and access control through integrations that improve operator efficiency and streamline operations. For example, cameras can trigger lighting based on footfall or raise barriers for authorised vehicles identified through their license plates. If a trespasser is detected or a large crowd forms in a restricted area, audio alarms can be automatically sounded, telling people to move on. If a traffic incident occurs, digital signage can dynamically redirect vehicles to reduce congestion.
5. Post-event investigations
AI greatly accelerates investigations by allowing security teams to search footage using metadata such as a vehicle's make, the colour of a suspect's clothing, or licence plate numbers, rather than manually reviewing hours of video. This improves evidence gathering and supports faster coordination with law enforcement and other third-party agencies. Eyewitness accounts can also be highly inaccurate or incomplete, so having video data greatly supports investigations.
Video management systems (VMS) can consolidate data from multiple sources such as cameras, sensors, and access control into a single interface, making it easier to analyse trends, respond to incidents, and extract reliable evidence-grade footage. These platforms support long-term data storage and retrieval, enabling both real-time situational awareness and pattern insights for future planning.
Compliance management and reporting are simplified thanks to AI. Organisations can show proactive security and safety measures, plus increase the amount of detailed data available to auditors, regulators, and other stakeholders.
The road ahead for AI in video
AI-powered video is already transforming how city authorities and transport organisations approach safety, security, and operational efficiency, but we are only at the beginning of what’s possible. As AI continues to evolve, its ability to interpret complex scenarios and drive intelligent responses will grow exponentially. At the same time, the hardware it relies on (cameras, sensors, edge devices) will become more powerful, more versatile, and more deeply integrated into daily operations.
We’ll see an increasing use of advanced technologies, from panoramic and multi-sensor cameras to drones and body-worn video, all working in concert to provide complete visibility across transport networks. But with this growing ecosystem comes the need for intelligent consolidation. Centralising data from all sources into a single, unified platform will be critical for managing complexity and making fast, informed decisions.
For transport operators, the message is clear: investing in AI today is not just a step forward, it's a strategic move for the future. The organisations that begin now will be the ones best positioned to benefit as the technology matures. Now is the ideal time to get on board with AI.
First published in the August edition of Security Journal UK (SJUK).