Explore how AI is being used to improve the metro passenger journey from entrance to exit by increasing efficiency and safety in urban transportation systems.
Metro systems are the heart of a city’s public transportation, and carry millions of passengers every day. Cities like New York and Tokyo see over 3 million and 6 million metro riders daily. As cities grow, making metro systems more efficient, safe, and pleasant for passengers becomes vital.
That's where Artificial Intelligence (AI) comes in, transforming everything from buying tickets to maintaining trains. In this article, we'll take a ride through a metro system, seeing how AI improves each step of the journey and the hidden AI technologies that keep everything running smoothly.
Usually, a journey in the metro starts with standing in line, buying a ticket, and going through turnstiles. AI actively streamlines this, making it faster and more convenient for passengers. For example, facial recognition technology can be used in metro ticketing systems. In busy cities, passengers can access services without physical tickets or smart cards. Instead, their faces are scanned at entry points, allowing for smooth access. In 2019, Beijing introduced a facial recognition system to handle overcrowding and cut down on long queues during rush hours.
A seamless integration of AI makes daily commutes easier and benefits millions of passengers every day. AI-powered ticketing systems not only improve the passenger experience but also boost security. By analyzing facial data, the system ensures that only authorized passengers enter the station. AI can quickly verify identities, stop unauthorized access, and spot potential threats. It can speed up the entry process, enhance security, and reduce fare evasion.
After entering the subway, finding your way through the station can be confusing, especially during rush hours. AI-based navigation systems can make moving through the metro easier by offering passengers real-time guidance. These systems use AI algorithms to analyze data from sources like GPS, sensors, and cameras to find the best routes and provide accurate, up-to-date directions. Passengers can use mobile apps, similar to Google Maps, to receive step-by-step directions within the station to quickly locate platforms, exits, and amenities. AI-based navigation can save time and reduce stress.
Other than navigation, the data from cameras and computer vision in a metro can be used to monitor crowd density in real-time. Cameras throughout the station can count passengers in specific areas to spot potential bottlenecks and overcrowded spots. These insights help transit authorities take proactive measures, such as adjusting train frequencies or sending staff to manage crowds. In fact, AI has been used experimentally in Dubai to reduce congestion by 40% to 60% and cut waiting times to 30 minutes during peak times and public events.
As passengers board the train, AI can improve their travel experience in various ways. Let's understand some of these applications:
Metro trains themselves can also be AI-powered. Fully autonomous trains can operate safely without human crew members. These AI-powered trains use advanced algorithms for real-time decision-making and sensors to detect obstacles and operate safely. AI-driven metro trains reduce staffing costs, offer greater flexibility to carry more passengers with better service quality, can be more consistently on time, and optimize acceleration and braking to reduce power consumption. For example, the Honolulu Rail Transit Project is the first fully autonomous system in the US. The system is expected to reduce traffic congestion and emissions by eliminating around 40,000 car trips per day.
Monitoring how many people exit through different stations is also essential for managing metro flow efficiently. AI models, like Ultralytics YOLOv8, play a key role in this process. YOLOv8 also supports object tracking and can be used to identify and follow multiple objects in real-time, making it perfect for busy metro systems. By analyzing video feeds from cameras placed throughout the stations, YOLOv8 can count passengers exiting, track their movements, and spot congestion patterns.
Beyond the metro's exit gates, AI can be integrated into last-mile connectivity solutions. AI-enabled bike-sharing stations, ride-hailing services, and shuttle services can make it easier for passengers to leave the station. By analyzing real-time data, AI can predict demand and when these services are available. This technology can also suggest the best routes and modes of transport based on current traffic conditions and individual preferences. As a result, passengers can quickly access the most suitable transportation options as they exit the station, reducing wait times and making their journey more convenient.
As passengers, we often overlook the complex processes happening behind our metro journeys. AI is crucial behind the scenes, especially in track inspection and maintenance. Systems like the Duos Technologies Railcar Inspection Portal (RIP) showcase this AI-driven approach. Using AI algorithms, the RIP captures and analyzes 360-degree images of every train car in seconds, even at speeds over 125 mph. These edge-based AI systems detect problems and alert railroad personnel of maintenance issues within 60 seconds. Constant monitoring makes it possible for potential issues to be identified and addressed swiftly.
Energy management and optimization are other applications of AI in a metro that are not commonly known. For example, Metro de Madrid uses an AI-based system to reduce energy costs for ventilation by 25% and cut CO2 emissions by 1,800 tons annually. Operating 891 ventilation fans that consume up to 80-gigawatt hours of energy annually, the system uses an optimization algorithm inspired by the foraging behavior of bee colonies. This algorithm analyzes vast amounts of data, considering factors like air temperature, station architecture, train frequency, passenger load, and electricity prices. It uses both historical and simulated data to predict and achieve the best balance for each station, improving over time through machine learning.
AI's integration into metro systems has brought numerous benefits. For instance, it improves efficiency by helping metro systems adhere to schedules, reduce delays, and optimize operations for smoother services. Safety is also increased through continuous monitoring, and predictive maintenance helps prevent accidents and malfunctions.
However, implementing AI in metro systems comes with challenges, such as:
Despite these challenges, the benefits make AI a transformative force in modern urban transportation systems like metros. The potential for increased efficiency, enhanced safety, and improved passenger experiences explains why the AI community is actively working to overcome these obstacles to fully realize the advantages AI offers. So, the next time you ride the metro, keep an eye out for the AI innovations that are a part of your journey.
From entering the station to navigating, riding, and exiting the metro, AI is transforming the entire journey. It optimizes operations, enhances safety, and delivers a seamless passenger experience. By improving track maintenance and energy management, AI guarantees efficient and cost-effective metro systems. Despite challenges like implementation costs and data privacy concerns, the benefits of AI in urban transportation systems like metros are undeniable. As cities continue to grow, AI will play an increasingly vital role in making metro travel smarter, safer, and more efficient.
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