Computer Vision Applications for Traffic & Parking Management

5 min read

Urban areas are plagued by a number of problems related to traffic congestion and both on-street and garage parking. Parking management companies lose money and have more accidents due to drivers wasting time looking for available parking spaces. Government agencies have a tough time reducing air pollution from wasted fuel and improving road safety due to drivers getting stuck idling at traffic lights. 

Computer vision is an advanced technology that can effectively solve these problems. This field of computer vision employs artificial intelligence to allow computers to derive meaningful data from digital image processing and segmentation. 

Computer vision is helping to create smart transportation systems in urban areas to make moving around a city easier, safer, and more cost-efficient. It is a key player in transportation applications including traffic flow analysis, parking space management, self-driving cars, and road condition monitoring. This guide is for government agencies that want to improve road safety, reduce traffic congestion, and lower fuel consumption; as well as urban planners and parking companies that want to improve parking space design and streamline parking management.

Computer Vision in Traffic Management

Whether it’s a government agency seeking opportunities to adjust signage to make the road a safer place, or vehicles that are leveraging computer vision to drive themselves - computer vision abounds in the world of traffic management. Here are some of the ways the technology is being used today. 

Improving Road Safety

The World Health Organization reported in 2022 that 1.3 million people die in road traffic crashes every year. The US Department of Transportation reported that more than 30,000 people died on roadways in the US through the first nine months of 2021, the highest transportation-related fatality rate since 2006. Computer vision can help bring these staggering numbers down.

Computer vision can recognize situations that commonly lead to accidents, like traffic violations. Some examples include speeding, blocking the pedestrian lane, not obeying traffic lights, and bike riders not wearing helmets. 

Computer vision can generate data that quantifies dangerous situations like hazardous drivers and near collisions. The data can help identify road infrastructure problems and estimate roadways' safety levels.  This information can then be used to estimate accident risks, plan for roadway repairs and improvements, and evaluate their impact on traffic and congestion. 

As a result of these analyses, computer vision can help governments and infrastructure companies reduce spending on road construction and reduce spending on police forces to enforce traffic violation penalties.

Computer vision used for traffic management can make roadways and intersections easier to navigate and safer for vehicles, cyclists, and pedestrians. It can detect the number of vehicles on a roadway at any given time, and predict upcoming traffic based on weather conditions. CV technology can quantify vehicle wait times, traffic flow patterns, and pedestrian traffic, to help facilitate optimal traffic flows to make roadways safer and more efficient for all.

Reduced Traffic Congestion and Fuel Consumption

Traffic congestion leads to longer travel times and more roadway accidents, but also to higher fuel consumption and more pollution. Smart transportation management uses computer vision to generate datasets which detect the types of vehicles (make and model) on a roadway, estimate their fuel consumption and gas mileage, and determine traffic density. This information is sent to a traffic control center that can reroute vehicles to reduce overall traffic congestion. 

The control center can use computer vision to determine the optimal usage and timing intervals for red and green lights on specific days and times to keep traffic moving through intersections. Traffic lights can be programmed to let vehicles with high fuel consumption pass through intersections more quickly, reducing idling time. This helps reduce fuel consumption and air pollution, and is particularly important for large vehicles like trucks and tractor trailers that lose fuel when having to stop or speed up. The US Department of Energy estimates that over 6 billion gallons of gasoline and diesel are lost every year to idling.

Computer vision is also being used as part of an initiative for better public transportation. Municipal transit agencies use computer vision to help increase rider occupancy, improve fuel efficiency, and ensure passenger safety. For example, video data processed in real time is used to help bus drivers avoid accidents. Buses can be re-routed to reduce instances of empty buses. Computer vision can gather data like the number of people waiting at bus stops or getting on and off of buses. These datasets can be used to help create smart bus stops with ideal passenger flow, bus routing and passenger capacity. Railways use computer vision to monitor trains and railway stations to increase efficiency and revenue.

Self-driving Vehicles and Driver Assistance Technology

Autonomous vehicles are still in development as their ability to navigate the complexities of roadways has proven to be very challenging. The aim of autonomous vehicles is to use a combination of artificial intelligence with data labeling, sensors and radar to operate without human intervention. Despite advancements in technology and progress in the development of self-driving cars, potential issues still remain in regards to safety. 

Computer vision and pattern recognition technology are being incorporated to make self-driving vehicles safe and easy to operate. Computer vision uses real-time pixel-based image processing of objects on the road to create a 3-dimensional map. These objects include other vehicles, pedestrians, road signs, barriers, and traffic lights. Image processing is used to determine driving space and predict accidents to eliminate risks. Self-driving vehicles use computer vision to determine traffic density and facilitate decision making at intersections which maximizes vehicle optimization and roadway safety. Computer vision can also predict behavioral patterns of other drivers, identify driving lane lines and detect upcoming changes in road conditions or terrain.

Although autonomous vehicles are still a work in progress, some driver assistance CV technology is already available in most vehicles, like acceleration, braking, and steering control, while drivers remain fully engaged. 

Computer Vision for Parking Management

Parking lot and garage managers have a lot to gain from various computer vision applications. Safety can be improved, but so can operational burden and space utilization. Here are some ways parking managers are taking advantage of computer vision.

Convenience and Cost Effectiveness

Computer vision can be used to detect and monitor available car parking spots, which is much more cost-effective than traditional camera detection systems. CV monitoring systems reduce driver frustration by saving them the time of searching for a parking space, and making both on street and garage parking easier. Drivers can request a specific on-street parking space in advance or be assigned a space as they enter a parking garage. Parking garage owners can use computer vision to effectively route drivers to avoid congestion and automatically detect space availability.

Computer vision can also be used to capture license plate information. The plate images are instantly recognized and used to determine for how long cars are parked, and if a vehicle is stored in a customer database. The technology records arrival and departure times, and streamlines parking lot management by eliminating the need for parking tickets or keycodes. Computer vision can also help manage car parking capacity, and use plate number recognition to provide automated payment processing and help drivers locate their parked car. 

Driver Safety and City Benefits

The time that drivers spend looking for available parking spaces is a common cause of increased traffic congestion and car accidents. The National Safety Council reported that distracted driving in parking lots causes tens of thousands of accidents annually in both parking lots and garages. Computer vision can identify vehicle features (size, make and model) to help drivers locate an ideal parking lot space as efficiently as possible, ensuring their safety and making optimal use of parking resources. 

Cities benefit from using computer vision for parking management by reducing traffic congestion and environmental pollution from emissions, and increasing revenue with fees collected through smart parking systems.

Improved Parking Space Design

Computer vision can collect data like vehicle movement, parking space locations, delivery areas, bus routes, and pedestrian traffic. Parking lots and garages can use data to identify bottlenecks where vehicles have problems pulling in and out of parking spaces. Smart transportation systems use this information to identify locations that need more parking spaces and determine optimal parking space designs. This helps avoid problems like illegal parking, parking lot and garage congestion, and traffic jams. Real time parking lot occupancy detection can help direct delivery drivers to available parking spots to avoid curbside parking. Computer vision gathers information that is critical for city planners and parking garage owners to make infrastructure upgrades to better manage parking demand. 


Computer vision has become an advantageous tool in the transportation industry since it has multiple problem-solving applications and provides large-scale benefits. Computer vision is being used by state and local government agencies, urban planners, and infrastructure companies for cutting-edge traffic and parking management. 

Computer vision can enable traffic and parking companies to become more competitive and increase profits through better design, monitoring and management of parking space, including the analysis and improvement of traffic flow. One of Safari AI’s many applications is to help parking lot managers quantify parking lot occupancy. But if you have other computer vision use cases for your business in mind, reach out and contact us to discuss further.

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