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Analytics-driven solutions for smart infrastructure

As city streets and sidewalks become progressively more congested, city planners are facing new safety challenges, including distracted pedestrians, growing numbers of cyclists on the road, and an increase in public transportation. At the same time, smart and semi-autonomous vehicles that can communicate with each other and with roadside infrastructure are on the rise in cities and on highways.

The challenge for senior traffic engineers and planners is complex. From a broad spectrum of emerging technology coming from new and well-established industry players, how do you choose the right technology to improve mobility, safety, and efficient use of roadways now and into the future, and have a level of assurance that the technology selected is a sound and reliable investment?

IP cameras with onboard analytics processing capability operating as video sensors are a key component to intelligent transportation systems that help to keep roadways flowing safely and efficiently. In combination with other systems for city and traffic management, smart IP cameras enable detection and monitoring solutions that instantly alert the right people to safety risks and constantly gather information on roadway usage to provide better insights and information for data-driven decision-making. As a result, city planners and senior traffic engineers can create a smarter, safer and more sustainable transportation ecosystem.

Reliable data and detection

Video analytics technology can bring intelligence to infrastructure by delivering solutions for traffic flow, improved safety, smart parking, and data collection. Analytics built into IP cameras enables intelligent video devices that can alert to safety risks and deliver valuable data for highway and urban infrastructure planning.

True analytics at the edge, in the camera, eliminate the need for additional computers at the pole or expensive networks for cloud-based analytics, and allows continuous capture of high-quality data. The result is a distributed network of cameras acting as intelligent processing nodes with no single point of failure, delivering a cost-effective and reliable video-as-a-sensor solution.

With machine learning technology – the latest advancement in video analytics – the cameras can be taught to recognise user-defined object classifiers. For example, machine learning can be used to accurately count overlapping vehicles queued in front of traffic lights or in dense traffic jams. In many use cases, machine learning capability can improve detection accuracy to enable precise vehicle counts with minimal errors.

Machine learning also enables customised solutions to solve the specific needs of cities and transportation departments. For example, it can detect the formation of icicles or snow build-up on bridges, overpasses, and tunnel openings, where they can pose a significant safety risk to motorists or pedestrians passing underneath.

Enhance safety with video analytics

Intelligent IP cameras deliver automatic incident detection and verification for slow or stopped vehicles, queues of vehicles at exit ramps, vehicles travelling the wrong way, objects in the road – such as lost cargo – and other traffic events. Alerts can be sent to traffic management centres, or through integration with highway information solution providers, the IP cameras can trigger third-party systems to notify drivers on the road, improving situational awareness and enabling them to take action earlier.

With video analytics, the IP camera becomes an intelligent sensor that can classify objects as cars, trucks, bicycles, and pedestrians, and detect speed and trajectory. Object classification enables the cameras to recognise what they are viewing for gathering data.

Using video as a sensor and software tools for tapping into the camera generated metadata, the data can be extracted and stored in relational databases allowing city traffic planning directors and senior traffic engineers to continuously collect real-time data to analyse flow patterns on networks of roadways. The camera-generated data can be used for implementing new policies that result in safer and more efficient intersections. This data can also help them determine how pedestrians use certain locations to provide insights into possible safety improvements.

In busy cities, video analytics can also help monitor parking lot occupancy, curbside parking, and enforce no-parking zones. In lots, cameras can count the number of open parking spaces, specialised spots – such as those for handicap or electric vehicles – or track ingress and egress, and relay this data to the video and parking management systems. Sharing this information and alternative parking locations on a dynamic message sign can help drivers find open parking faster to get off the road and reduce traffic congestion and emissions.

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