The lack of evidential data makes it difficult to justify spending taxpayer money on green projects that may or may not work as intended or forecasted. And once a new walking trail, access road, or park-and-ride system is up and running, officials lack the means to measure its effects. As a result, many of those in local government are challenged to progress beyond baby steps toward achieving their sustainability goals.
New AI-based optical sensor systems are starting to change the picture. By collecting and analysing data linked to traffic and pedestrians, in combination with information from environmental sensors and other sources, officials can make evidence-based decisions supporting green projects. Once the new solutions are deployed, results can be monitored with outcomes influencing optimisation and change as towns grow and transform.
Smart Spaces Lower Energy Costs, Boost Safety Though IoT sensor technology is complex, it can be easily deployed on streetlight poles. SSE has been doing just that since 2010, developing remotely controlled streetlights for local authorities, including Hampshire County Council and Southampton City Council. SSE solutions have since been installed on more than 400,000 light poles in the UK and Ireland.
Sensors attached to the poles enable town administrators to remotely turn on, turn off, or dim lights, helping reduce energy costs and lower their carbon footprint. And if a nighttime incident occurs, authorities can literally “shed light” on the problem for arriving emergency crews.
The street lighting control deployment has been followed by the development of the SSE Sentinel optical sensor. Installed on light poles in Cornwall, Slough, and Pembrokeshire, and other locales), optical sensors capture in-depth information about street activity—giving town administrators the tool they need to advance greener planning.
Running on a lightweight AI edge gateway, Sentinel collects and processes detailed images of vehicle and pedestrian traffic. It securely sends select data over a cellular network to the SSE Smart City and Places platform, where it can be visualised and analysed for planning and evidential insights.
AI and city planning considerations use sensors mounted on streetlights collect pedestrian, vehicle, and traffic information.
Figure 1. SSE Sentinel optical sensor collects vehicle, pedestrian traffic, and other data that help design sustainable smart cities.
To process data-heavy images in near-real time, the solution uses high-performance Intel® processors. Algorithms deployed through the Intel® OpenVINO™ Toolkit can scrub private details such as facial features and license plate numbers, transmitting only information that towns and cities need.
Customers can combine the sensor data with information about season, time of day, weather, and critically, air quality.
As environmental and sustainability concerns grow, cities and communities are likely to extend their use of smart spaces, Mitchell believes. For example, sensors in water drainage systems could detect pipe blockages and inform maintenance crews. By combining this data with historical information about floods and weather conditions, city officials could predict the effects of an upcoming storm and prioritise fixes to minimise flood damage.
Predictive analytics and smart technologies could also help authorities roll out smart and sustainable initiatives more efficiently.