Smart cities: The correlation of cause and effect

The concept of a ‘smart city’ can be seen as a cluster of city improvement plans that address multiple challenges. These can relate to transport, mobility, air quality, noise levels, water management, architectonic barriers, maintenance optimization, street and outdoor lighting, urban development, and other areas. These aspects are ultimately all related to each other – the amount of traffic, for example, has an effect on air quality and noise levels – but with many other factors also thrown in, understanding the exact correlation of cause and effect is a complex task.

Staying with our example, in order to effectively deal with pollution, city authorities are increasingly looking for technologies that help them not only manage traffic flow, but also gain a better understanding of the link between crowded city streets and air quality. When it comes to assessing anti-pollution policies, it is crucial that they can rely on accurate information. They need a clear picture of the problem and to this end, it is useful to measure which of a number of factors – traffic, industry and weather – has a bigger impact on the environment.

Surveillance cameras have some of the answers

Daily commuting routines and car accidents are among the main factors causing traffic jams. In some cities, network cameras are already used as intelligent sensors that collect real-time data about traffic, including vehicle counting, information on deviations or roadworks, accidents and congestion. Live video and automatic incident notifications can significantly help city officials with faster assessment of the traffic situation. Authorities can determine the police and rescue resources required and intervene promptly, which helps reduce congestion.

On top of this, as they are easier to install and more versatile than traditional vehicle counting devices placed under the road surface, network cameras are more economical to deploy and maintain. This makes it easy to build a denser network of sensors that gathers a larger amount of data.

The extensive traffic statistics from cameras can be combined with data from weather stations that measure air quality, recording real-time data on gases, particles, wind speed and direction. With all this input, big data applications can deliver a much better analysis of the correlation between air quality and pollution sources – and they can also help assess the real impact of any countermeasures the city has taken.

Many application areas

Thinking of all the other factors that influence daily city life, the concept above can be extended to other areas, too. For instance, software applications running on network cameras can detect when it is raining or snowing and measure its intensity. This greatly improves nowcasting, and once combined with other data sources, it also helps monitor the impact of rain and snowfall on pollution and traffic.

When it comes to measuring noise levels, IP cameras equipped with microphones and audio analytics software allow city authorities to measure and analyze noise pollution, again correlating it to traffic but also to crowd levels and any construction works going on in the vicinity.

Talking about crowds: understanding crowd behavior, using data collected by cameras about the number of people per square meter over time, can help authorities develop new concepts for urban planning that optimize architecture, traffic management, parking, malls, pathways, urban furniture and lighting.

Cameras can provide live information about the availability of parking spaces, which has a direct impact on traffic, air quality and the citizen experience. Increasing the safety of drivers, cyclists and pedestrians is another smart city objective that involves the correlation of traffic data, crowd levels and street lighting.

An open approach

The choice of an open, flexible platform that allows network cameras to act as intelligent sensors is now bringing tangible benefits to smart city projects. This approach can help provide valid and correlated information about the many different variables that make up day-to-day city life. For the first time, authorities have the opportunity to compare detailed, real-life data with recent data models, and to decide on policies that will actually make a difference.

Of course, cities’ needs will change and evolve over the years. With an open platform, network cameras can be used now as intelligent sensors for existing use cases, but they will also be easy to scale and integrate into future solutions according to priority, resources and new technical possibilities.

Find out more about the future of video in smart cities.