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4 Ways Computer Vision Will Reshape Urban Transportation

Smart transportation is one of the fundamental components of smart cities. The integration of digital technologies with physical transport infrastructure will change how people live, work and travel in cities. The use of autonomous vehicles, IoT, big data analytics and many other technologies will enable city residents to travel safer, cheaper and faster. Mobility and communication networks in urban spaces keep any city running smoothly. Adding elements of smart transportation to it will make cities more efficient, livable and sustainable. Computer vision is expected to play a key role in many intelligent transportation applications—from self-driving cars and traffic flow analysis, to parking lot management and road condition monitoring.

Assessing the Impact of Computer Vision on Smart Transportation

Smart transportation relies on digital systems that process large amounts of information in the form of images, videos, audio files, text-based information, GPS and GIS data, IoT sensor data and other forms of data. . Machine learning and computer vision algorithms are needed to process this raw information and transform it into actionable insights for urban planning bodies to formulate effective policies on smart city. These technologies are also the driving force behind complex applications such as self-driving cars, intelligent traffic management, intelligent airport video monitoring and automatic parking systems.

1. Improving Road Safety

According to the World Health Organization (WHO), approximately 1.3 million people die in road accidents every year. Some of the main causes of traffic accidents are speeding, driving under the influence of alcohol, avoiding safety equipment such as helmets and seatbelts, distracted driving and not obeying traffic rules. As you can see, human error is the cause of most traffic accidents.

Autonomous vehicles can remove the human element from this equation, thereby reducing the likelihood of crashes. A self-driving car will constantly collect information from a vast network of sensors and cameras on vehicles, roads and traffic signals. Computer vision algorithms will analyze this raw data to optimize road safety and generate insights on collision alerts and pedestrians on the road in real time. A self-driving car can process data dynamically and detect how close it is to pedestrians, other vehicles, cyclists and potential road hazards before making appropriate adjustments. Image processing algorithms also enable autonomous cars to pick out moving objects in low-light areas and automatically trigger airbags and automatic brakes in the event of a collision.

Other safety technologies inside an autonomous vehicle that could change road safety are:

● Lane centering systems

● Blind-spot safety monitoring systems

● Intelligent speed adaptation system

● Night-vision systems

● Road sign recognition

These applications rely on computer vision and machine learning algorithms to function properly. Recently, the University of Applied Sciences in Ulm and Heilbronn, Germany, collaborated to develop a self-learning road warning system, which uses sensor, radar and camera data to identify moving objects and warn the drivers to avoid accidents.

2. Eliminate Traffic Congestion

Smart transportation does not only involve self-driving cars but also the optimization of road networks. Traffic congestion is the biggest cause of increased travel time in cities. This contributes to higher fuel consumption and air pollution. Intelligent traffic monitoring and management can solve such issues by using computer vision to reduce congestion and fuel consumption.

The first step in smart traffic monitoring systems is data collection through overhead and ground-based cameras, GPS, GIS and radiofrequency devices. This data is fed to computer vision algorithms that detect vehicles on the road, calculate traffic density and relay their status to a local traffic control center. Real-time data on road congestion is further analyzed to divert vehicles to less congested roads. In this situation, autonomous, connected cars will also act as sources of information for traffic detection systems, with their cameras sending real-time data to control centers.

Cars idling in traffic waste a lot of fuel and add to already high levels of air pollution. So, computer vision in intelligent transportation can solve this by object recognition and name recognition for such vehicles. Machine learning algorithms can identify the car and the estimated fuel consumption. This knowledge helps regulate traffic lights at the next intersection to keep vehicles moving.

Researchers at Oak Ridge National Laboratory (ORNL) used machine learning and computer vision to design a system that can keep traffic moving efficiently at intersections and also reduce fuel consumption.

3. Improving Passenger Safety at Airports

Air travel is also a unique feature of urban transportation. Intelligent transportation applications in airports focus on passenger safety, airport staff safety and customer experience. During the busy holiday period, airports have very long queues at security checkpoints and check-in counters. Here, cameras with computer vision can improve queue management. Such cameras can continuously monitor queues of users, and computer vision and deep learning algorithms will predict when a customer service staff is needed at specific counters. , or if necessary open a separate window. Surveillance data will also be used to analyze and calculate passenger waiting times. These calculations help reduce baggage and customer bottlenecks at security screening and waiting times during loading and unloading.

Algorithms can even perform facial recognition to verify a passenger’s identity and allow them to proceed without human intervention. Typically, security personnel physically scan airport cameras to identify and track suspicious activity. Machine learning and computer vision will also automate this process, leading to faster response times and better airport safety.

For example, object detection is used to track suspicious devices or potentially harmful objects. Facial recognition algorithms can identify and track potential threats without having to interact with the person in question or affect other travelers.

4. Designing Better Parking Spaces

Without specific areas in the city reserved for parking, people park illegally on the street, reducing the available road space for cars and causing traffic congestion. People also spend a lot of time driving to find suitable parking spaces, resulting in wasted time and fuel. Smart transportation can address this by gathering vital information on vehicle movements, parking locations, illegal parking spots, dedicated delivery zones, ride-hailing areas, pedestrian traffic and times of increased vehicular activity. Much of this data is in the form of images and videos, so computer vision algorithms are needed to process this data and generate insights for urban planners to design parking policies.

The optimization of parking through intelligent transportation results in less time spent by users to find parking spaces, which causes a reduction in traffic delays. Real-time monitoring of parking spaces can be used to direct drivers to open parking spaces. The real-time parking availability feature helps delivery fleets improve route efficiency because delivery partners don’t have to park on the curb. This application will save money to delivery companies in paying fines for side parking.

Intelligent transport systems and thus smart cities cannot be built without computer vision, artificial intelligence and IoT. Computer vision-driven systems form the backbone of every application of smart city initiatives. Whether it’s improving traffic conditions, preventing air pollution, transporting passengers safely around the city or helping to design better urban spaces, the computing vision of intelligent transportation will change. how people live, travel and work in cities.

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