Traffic Visualization and Congestion Control
As urbanization trend increases over years, traffic congestion and air pollution are one of the most commonly addressed issues by smart city management systems. Getting real time insights of traffic congestion requires wide range of sensors deployed in key areas of the city roads. These sensors include LPR (license plate recognition), video cameras with advanced analytics (like vehicle counter) and magnetic loops. In order to provide operators insights, they can react upon, the major roads of the city divided into segments. Each segment is a section bound by junctions with smart traffic sensors (and optionally an environmental sensor monitoring air pollution level) and presented on the GIS map with predefined colors reflecting the congestion calculated by real time analytics. In the screen shot below there 6 segments presented in 4 colors.
Green color represents smooth traffic, yellow – slight traffic congestion, orange – moderate congestion and red represents traffic jam.
The congestion level set for each segment is based on data received from multiple sensors and services, like Waze or Google Maps, and a scheduled data analytics runs constantly to provide near real time updates. Data fusion of physical sensors and social services provide accurate reflection of the traffic status.
As traffic congestion increases air pollution grows and requires immediate actions to be taken either automatically by predefined workflow or manually by operator. Such action might include switching traffic light operational mode or sending traffic police unit to balance the congestion. The operators can utilize Digital Message Signs to redirect drivers to less congested roads. On the roads where bi-directional lanes are used, operator can switch the digital sign and switch the traffic flow of the lane.
In addition to real time visualization the system provides anomaly detection by using historical data collected for the segment on short recurring time ranges. Such anomaly can point to a car accident or road malfunction not detected by cameras and other sensors.
This extensible combination of physical sensors and social services provides actionable information for different stakeholders within traffic management authorities to better utilize traffic flow, and quickly respond to hazards and maintain high service level.
