The importance of the railway industry, with its extensive network and intricate operations, is undeniable for many economies. In the face of technological advancements, Artificial Intelligence (AI) has surfaced as a revolutionary influence across a range of sectors, including the transportation sector. The railway industry is no exception, as the potential applications of AI promise to improve efficiency, safety, and customer service. However, the integration of AI into the railway industry is not without its difficulties. In this piece, we will explore the role of AI in the railway industry, address the obstacles encountered, and discuss approaches to overcome these challenges.
A recent publication titled 'Towards establishing AI-integrated rail firms', jointly produced by the International Union of Railways (UIC) and McKinsey, disclosed that roughly 35% of firms have one or two applications on a large scale, while other applications are still under trial. This suggests that the railway sector is in the early stages of AI integration, and as with any early stages, challenges are anticipated.
Leveraging AI for Enhanced Railway Operations
The phenomenon's prevalence can be attributed to the deeply ingrained customs within the industry under discussion. These customs have considerably influenced the practices and philosophies of those engaged in the industry, creating a challenge in introducing fresh perspectives or theories. Consequently, innovation is frequently suppressed, and novel problem-solving strategies are confronted with opposition. This deep-seated tradition can also pose barriers for newcomers, as the established entities of the industry are typically averse to transformation.
Moreover, this is an industry characterized by intricate operations where aspects like individuals, systems, efficiency, culture, and safety are vital considerations that can impede progress. Nonetheless, the potential of AI to boost productivity, safety, and security is vast. Particularly in those where unstructured data are concerned, such as images, videos, audio and conversation texts, where more advanced and language-oriented AI is required. Let's explore some use cases.
1. Visual Inspection and Defect Detection
Artificial intelligence-based vision systems evaluate pictures and films from cameras positioned on trains and along tracks to recognize defects like fractures, wear and tear, or obstructions. This automatic visual assessment assists in the early detection of problems, lessens the need for manual inspections and enhances safety.
For instance, a partnership between the Norfolk Southern Corporation and the Georgia Tech Research Institute (GTRI) resulted in the creation of digital train inspection gateways that employ computer vision to inspect trains traveling at a maximum speed of 60 miles per hour for potential mechanical defects.
2. Natural Language Processing for Customer Service
It may not be startling, but AI-powered chatbots and virtual assistants utilize natural language processing (NLP) to comprehend and answer passenger inquiries. They can manage a broad spectrum of questions related to railway services, ranging from ticket reservations to travel data, by examining unstructured text input from users, hence decreasing on-site lines and enhancing customer service.
3. Sentiment Analysis of Social Media and Feedback
AI algorithms analyze unstructured data from social media posts, reviews, and feedback forms to gauge passenger sentiment. This information helps railway operators understand public perception, identify areas for improvement, and respond to passenger concerns more effectively.
A recent prototype employed by the Indian Railway could automatic classify twitter posts with regards to its rail operations into feedback or emergency type of posts which potentially reduces manual work in reviewing post and increase response time towards taking action on emergencies.
4. Incident Reporting
AI technology scrutinizes incident reports, maintenance logs, and operational records in an unstructured textual format on the platform to identify patterns and pinpoint the root causes of issues. This analysis enables railway companies to improve their safety protocols and operational efficiency.
5. Platform Security and Surveillance
AI-driven surveillance systems use video analytics to monitor real-time footage from railway stations and trains. These systems can detect unusual activities, unattended luggage, and potential security threats, providing immediate alerts to security personnel and improving overall safety. Furthermore, computer vision can be used to track gantry and platform movement, ensuring an accurate count of people; failure to do this could result in security hazards.
According to documents procured by Wired, Amazon's Rekognition software has been under trial at eight of the busiest train stations in the U.K. over the last two years. The cameras played a significant role in detecting trespassing events on railway tracks and averting bicycle thefts.
Assessing an organisation's readiness for AI
AI benefits can be significant, but as with all technologies, its effective deployment requires readiness beyond software and hardware throughout the entire organization. To assess a railroad company's potential to integrate AI, there are several essential factors worth considering;
Technological Infrastructure: Assess current hardware, software, and network capabilities for AI integration.
Data Management Practices: Governance on data collection, storage, and processing
Workforce Capabilities: Evaluate and enhance employee skills in understanding and working with AI through training.
Organizational Culture: Foster a culture open to innovation and technological change.
Regulatory Compliance: Ensure AI initiatives comply with industry regulations and ethical standards.
Cost and ROI Analysis: Conduct cost-benefit analyses to evaluate potential ROI from AI projects.
Partnerships and Collaboration: Establish partnerships with technology providers and research institutions for AI expertise and best practices.
The Road Ahead for AI in Railways
As the railway industry pushes forward into the future, the integration of artificial intelligence (AI) will undoubtedly play an increasingly significant role. This evolving process is not without its difficulties, as various challenges may arise along the way. However, with careful strategic planning and a commitment to investment in new technologies, these obstacles can be effectively addressed and overcome. Embracing the potential of AI in the railway industry will undoubtedly lead to improvements in efficiency, safety, and overall performance, ultimately benefiting both industry professionals and passengers alike. We are excited to be at the forefront of this transformative journey and invite you to join us in shaping the future of rail transportation through business focused AI solutions.
