Smart transportation is one of the fundamental components of smart cities. The integration of digital technologies with physical transport infrastructure will transform how people live, work and travel in cities. The use of autonomous vehicles, IoT, big data analytics and many more technologies will enable urban residents to travel safer, cheaper and faster. The mobility and communication networks in urban spaces keep any city running smoothly. Adding elements of smart transportation to these will make cities more efficient, livable and sustainable. Computer vision is expected to play a key role in several smart transportation applications—from self-driving cars 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 digital systems that process a huge amount 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 convert it into actionable insights for urban planning bodies to formulate effective policies in smart cities. These technologies are also the driving force behind complicated applications like self-driving cars, intelligent traffic management, smart airport video surveillance and automated parking systems.

1. Improving Road Safety

According to the World Health Organization (WHO), approximately 1.3 million people die in road crashes every year. Some of the leading causes of traffic accidents are speeding, driving under the influence of alcohol, avoidance of safety gear like helmets and seatbelts, distracted driving and non-adherence to traffic rules. As you can see, human error is the reason for most traffic accidents.

Autonomous vehicles can remove the human element from this equation, thereby drastically reducing the chances of a crash. A self-driving car will constantly collect information from the vast network of sensors and cameras on motor 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 hazards on the road before making accurate adjustments. Image processing algorithms will also enable autonomous vehicles 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 within an autonomous vehicle that will transform road safety are:

● Lane centering systems

● Blind-spot safety monitoring systems

● Intelligent speed adaptation systems

● Night-vision systems

● Road sign recognition

These applications rely on computer vision and machine learning algorithms to function correctly. Recently, the Universities of Applied Sciences of Ulm and Heilbronn, Germany, have collaboratively created a self-learning road warning system, which leverages sensor, radar and camera data to identify moving objects and warn drivers to prevent accidents.


2. Eliminating Traffic Congestion

Smart transportation doesn’t just involve self-driving cars but also the optimization of road networks. Traffic congestion is the biggest reason for increased travel time in cities. It contributes to higher fuel consumption and air pollution. Intelligent traffic monitoring and management can address such issues by leveraging computer vision to reduce congestion and fuel consumption.

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

Vehicles at rest in traffic waste a lot of fuel and compound the already-high air pollution levels. So, computer vision in smart transportation can resolve this through object detection and name recognition for such vehicles. The machine learning algorithms can identify the vehicle and its approximate fuel consumption. This knowledge will help in regulating the next intersection’s traffic lights accordingly to keep the 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 through intersections and also minimize fuel wastage.

3. Enhancing Passenger Safety at Airports

Air travel is also a distinctive feature of urban transportation. Smart transportation applications at airports focus on passenger safety, airport personnel safety and customer experience. During busy holiday periods, airports have very long queues at security checkpoints and check-in counters. Here, cameras equipped with computer vision can improve queue management. Such cameras can continuously monitor the user queues, and the computer vision and deep learning algorithms will predict when a customer service personnel will be needed at specific counters, or if there is a need to open another window. The surveillance data will also be used to analyze and calculate passenger wait times. These computations will help reduce luggage and customer bottlenecks in security screening and wait times during loading and unloading.

The algorithms will even be able to conduct facial recognition to verify a passenger’s identity and authorize them to move ahead without human intervention. Usually, security personnel physically scan the airport cameras to identify and track suspicious activities. Machine learning and computer vision will also automate this process, leading to faster response times and better airport safety.

For instance, object recognition will be used to track suspicious devices or potentially harmful substances. Facial recognition algorithms will identify and track potential threats without needing to engage the person in question or impacting other travelers.

4. Designing Better Parking Spaces

When there aren’t specific areas in the city reserved for parking, people illegally park on the road, reducing available road space for vehicles and causing traffic jams. People also spend a lot of time driving to find appropriate parking spots, resulting in wasted time and fuel. Smart transportation can address this by gathering crucial information on vehicle movements, parking locations, illegal parking spots, dedicated delivery zones, ride-hailing areas, pedestrian traffic and periods of increased vehicular activity. Most 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 in designing parking policies.

Optimizing parking through smart transportation results in less time spent by users to find parking spaces, leading to reduced traffic delays. The real-time monitoring of parking spaces can be used to direct drivers to open parking spots. The real-time parking availability feature can help delivery fleets improve route efficiency as delivery partners will not have to park curbside. This application will save delivery companies money on paying fines on curbside parking.

Smart transportation systems and thereby smart cities cannot be built without computer vision, artificial intelligence and IoT. Computer vision-powered systems form the backbone of every application of smart city initiatives. Whether it’s improving traffic conditions, curbing air pollution, transporting passengers safely around the city or helping design better urban spaces, computer vision in smart transportation will revolutionize how people live, travel and work in cities.


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