Today, with the growing adoption of the Internet of Things (IoT), digital data is plentiful, with technology can tracking and aggregating so many forms of data for various applications. Increasingly, cities are relying on data intelligence to become “Smart Cities.” That is, they gather data from multiple sources to increase efficiencies, improve residential quality of life, optimize municipal operations and, of course, improve public safety. One important aspect of local security is traffic management, and business intelligence has enabled many smart cities to analyze their current traffic patterns and plan for future needs. To do so, they gather statistics and data regarding both personal and public transportation, which includes not only trollies, buses or subways, but also vehicles, bicycles and, in some cities, electric scooters.
The challenge with business intelligence is that it can be overwhelming in its volume and variety unless it is presented in ways that are easy to understand. That’s where data visualization plays an important role. Data is aggregated and calculated by measures (such as # of Objects, # of People, Avg Duration, or Maximum Objects/Hour), and then coordinated with dimensions (such as Time, Date, Class, Color, Source, Path & Area). Data visualizations can merge different data points to convey deeper insights and trends. The power of some data (such as number or types of vehicles using a certain roadway) is good, but the power of combining data points (such as vehicle types and types of accidents on variety of particular roadways) is exponentially valuable.
One common technology used to collect transit / traffic data is video content analytics (VCA), which leverages the video produced by video camera surveillance networks. Although video surveillance data is commonly only thought of as a public safety driver, it can also be harnessed as business intelligence about traffic patterns, and it can display aggregated data that has been collected over time. Powered by Deep Learning and Artificial Intelligence, video content analysis can distinguish objects in video footage, such as cars, trucks, buses, motorcycles, bicycles, women, men, children, and animals. For example, how many vehicles cross a certain intersection or a bridge in a certain span of time? Which intersections are the most dangerous for pedestrians? Video analysis helps urban planners identify traffic hotspots and optimize traffic flows, and data visualization is a key part of delivering that business intelligence.
There are five common forms of data visualization that can be used for analyzing and comparing traffic over time, as explained below.
When individual graphs are displayed in dashboards alongside other business intelligence visualizations, the user can more effectively evaluate large sets to drive intelligent decision-making. By leveraging video content analytics and visualizing video data into dashboards, cities are empowered to monitor activity over the course of time, multiple cameras and various locations, and plan urban improvements and infrastructural changes based on actionable insights. Using data visualization to compare activity at a specific location across multiple days, cities can keep track of trends and anomalies and better prepare for expected and unexpected changes in traffic.
By aggregating data over time, planners gain quantitative insights about the efficiency of pedestrian paths or bike lanes, street or parking lot patterns, and where to add a new light or crosswalk. With data visualization, smart cities can unlock the data trends they have buried in their video surveillance data to optimize the safety and efficiency of their motorists, cyclists and pedestrians. When equipped with data that is highly relevant and easy to understand, urban planners, businesses, and municipal agencies can make better decisions about traffic management and improve public safety, public health and the overall quality of life for city residents, visitors and businesses.