Four ways computer vision will reshape urban transportation

Smart transportation is one of the basic components of smart cities. The integration of digital technology and physical transportation infrastructure will change the way people live, work and travel in cities. The use of self-driving cars, the Internet of Things, big data analytics and more technologies will make traveling safer, cheaper and faster for city dwellers.

Mobility and communication networks in urban space enable any city to run smoothly. Adding smart transportation elements to them will make cities more efficient, livable and sustainable. Computer vision is expected to play a key role in a variety of smart transportation applications, from autonomous vehicles and traffic flow analysis to parking space management and road condition monitoring.

Exploring the impact of computer vision in smart transportation

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

1. Improving road safety

According to statistics from the World Health Organization (WHO), about 1.3 million people die in road traffic accidents every year. Some of the main causes of traffic accidents are speeding, drunk driving, failure to wear safety equipment such as helmets and seat belts, distracted driving and failure to obey traffic rules. It can be seen that human error is the cause of most traffic accidents.

Self-driving cars can remove the human element from these situations, greatly reducing the chance of an accident. Self-driving cars will constantly collect information from a vast network of sensors and cameras on cars, roads and traffic lights. Computer vision algorithms will analyze this raw data to optimize road safety and generate insights about collision alerts and pedestrians on the road in real time.

A self-driving car can process data on the fly and detect how close it is to pedestrians, other vehicles, cyclists and potential hazards on the road before making accurate adjustments. Image processing algorithms will also enable self-driving cars to recognize moving objects in low-light areas and automatically trigger airbags and brake automatically in the event of a collision.

Other safety technologies in autonomous vehicles that will transform road safety include:

1. Blind Spot Safety Monitoring System
2. Intelligent Speed ​​Adaptation System
3. night Vision System
4. Road Sign Recognition
5. Lane Keeping System

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

2. Alleviating traffic congestion

Smart transportation involves not only self-driving cars, but also the optimization of road networks. Traffic congestion is the biggest reason for increased travel times in cities. It leads to higher fuel consumption and air pollution. Smart traffic monitoring and management can solve these problems by leveraging computer vision to reduce congestion and fuel consumption.

The first step in a smart traffic monitoring system is to collect data through aerial and ground cameras, GPS, GIS and radio frequency equipment. This data is fed into a computer vision algorithm that will detect vehicles on the road, calculate traffic density and communicate their status to the local traffic control center. Real-time road congestion data is further analyzed to redirect vehicles onto less congested roads. In this case, autonomous connected vehicles will also serve as a source of information for traffic detection systems, with their cameras sending real-time data to a control center.

Standing vehicles in traffic waste huge amounts of fuel, exacerbating already high air pollution levels. Therefore, computer vision in smart transportation can solve this problem through object detection and name recognition of such vehicles. Machine learning algorithms can identify vehicles and their approximate fuel consumption. This knowledge will help adjust the traffic lights at the next intersection accordingly to keep vehicles moving.

Researchers at Oak Ridge National Laboratory (ORNL) have used machine learning and computer vision to design a system that moves traffic efficiently through intersections and minimizes fuel waste.

3. Strengthen airport passenger safety

Air travel is also a prominent feature of urban transportation. Smart transportation applications at airports focus on passenger safety, airport personnel safety and customer experience. Airports see long lines at security checkpoints and check-in counters during the busy holiday season. Here, cameras equipped with computer vision can improve queue management. The cameras can continuously monitor user queues, and computer vision and deep learning algorithms will predict when customer service staff will be needed at a specific counter, or if another window needs to be opened. Monitoring data will also be used to analyze and calculate passenger wait times. These calculations will help reduce baggage and customer bottlenecks at security checks and waiting times during loading and unloading.

The algorithms are even capable of facial recognition to verify a passenger’s identity and authorize 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, resulting in faster response times and improved airport security.

For example, object recognition will be used to track suspicious devices or potentially harmful materials. Facial recognition algorithms will identify and track potential threats without contacting the person in question or disturbing other travelers.

4. Design better parking spaces

When there are no designated parking areas in the city, people park illegally on the road, reducing the available road space for vehicles and causing traffic congestion. People also spend a lot of time driving looking for a suitable parking space, wasting time and fuel. Smart transportation can solve this problem by collecting critical information about vehicle movements, parking locations, illegal parking spaces, dedicated delivery areas, ride-hailing areas, pedestrian traffic and periods of increased vehicle activity. Much of this data is in the form of images and videos, so computer vision algorithms are needed to process this data and provide insights for city planners to design parking policies.

Optimizing parking through smart transportation reduces traffic delays by reducing the time users spend looking for a parking space. Real-time monitoring of parking spaces can be used to guide drivers to open parking spaces. The real-time parking availability feature can help delivery fleets improve route efficiency because delivery partners do not have to park on the street. This app will save delivery companies the cost of paying roadside parking fines.

Without computer vision, artificial intelligence and the Internet of Things, it is impossible to build smart transportation systems and thus build smart cities. Computer vision-driven systems form the backbone of every application in smart city initiatives. Whether it's improving traffic conditions, curbing air pollution, safely transporting passengers around cities or helping design better urban spaces, computer vision in smart transportation will revolutionize the way people live, travel and work in cities.

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