Who we are and why we exist.

Egis is a leading global architecture, consulting, construction engineering and operating firm. We work side by side with clients to build a more balanced, sustainable and resilient world.

About egis

Our purpose, our values

Our leadership team

Our awards

Vision

Our strategic vision

Our brands and acquisitions

Digital transformation

About our CSR

Corporate Social Responsibility

Ethics and Compliance

Diversity and gender equality

Policies and charters

Egis Foundation

Our activities

We work alongside our clients on every step of their journey. Across every aspect of transport, infrastructure and the built environment. All around the globe.

Transport

Maritime and waterway

Major structures

Geotechnical engineering

City

Buildings

Cities

Industrial & Process Engineering

Sports & Events

Sustainable resources

From idea to operation.

Combining deep technical expertise with close collaboration, we help design and operate smart, sustainable transport, cities and utilities — unlocking success for our clients.

Advisory & Delivery

Consultancy and advisory

Design and engineering

Programme and project management

Training and certification

Managing & operating

Infrastructure management

Mobility and urban services

Road users services

On street parking management

Transformation

Global footprint. Local perspective.

With a network that spans over 100 countries in Europe, the Middle East, India, Latin America, Africa and Asia-Pacific, we can resource projects in every region.

Europe & Central Asia

Middle East

Africa

Asia

Australia & New Zealand

Latin America

North America

Discover what’s next.

Explore emerging trends and discover in-depth reports on issues affecting the built environment, future transport and sustainable infrastructure, along with breaking company news.

News

All news

Press & media

Events

Insights & White papers

Publications

All insights •Applications of Artificial Intelligence in Railroa...

Applications of Artificial Intelligence in Railroads

Published 10.02.25

5 mins read

Artificial Intelligence (AI) is a branch of computer science that enables machines to perform tasks that traditionally require human intelligence. These tasks include learning, decision making, problem solving and pattern recognition. Artificial Intelligence (AI) has emerged as a crucial tool for modernization in different systems and/or applications that supports different types of users to perform different tasks; for example, to detect some kind of disease based on images or simply to make a presentation.

In railway systems, being a sector characterized by its complexity and the need to optimize operational efficiency, AI would be an innovative solution that would improve safety, reduce costs and increase sustainability. The following is a description of the possible applications of Artificial Intelligence that would support Railway systems.

1 APPLICATIONS.

1.1 PREDICTIVE MAINTENANCE

One of the most prominent uses for AI in railroads would be predictive maintenance, using advanced sensors and algorithms to monitor equipment conditions and predict failures before they occur. This approach significantly reduces downtime and costs associated with corrective maintenance. For example:

Track and rolling stock monitoring: Sensors installed on tracks and trains would collect data on vibrations, temperature, and wear and tear. AI algorithms analyze this data to identify signs of deterioration.
Reduced outages: Failures can be predicted weeks or even months in advance, allowing for more efficient planning of repairs.

1.2 OPTIMIZATION OF RAIL TRAFFIC

Efficient rail traffic management is essential to ensure punctuality and safety. AI can analyze large volumes of data in real time, optimizing operations in the following ways:

Automatic control systems: machine learning algorithms adjust routes and schedules in real time, minimizing conflicts between trains and improving infrastructure utilization.
Simulation and planning: AI can model various scenarios to anticipate problems and propose solutions.

1.3 ENHANCED SECURITY

Safety is a priority in rail transport, and AI can significantly improve it through:

Anomaly detection: AI-based systems can identify unusual behavior on the tracks, such as foreign objects or suspicious movements, triggering automatic alerts.
Fatigue monitoring: Computer vision systems analyze drivers' movements and facial expressions to detect signs of fatigue and prevent accidents.

1.4 PASSENGER EXPERIENCE

AI would also improve the user experience on rail systems through:

Recommendation systems: intelligent platforms that suggest personalized schedules and routes based on passenger preferences.
Customer service: Chatbots and virtual assistants help resolve doubts about schedules, fares and services in real time.

1.5 ENERGY AND SUSTAINABILITY

In an increasingly environmentally conscious world, AI plays a crucial role in the sustainability of rail transport. Some possible applications would include:

Energy consumption optimization: advanced algorithms would adjust speed and braking to minimize energy consumption.
Demand prediction: AI would anticipate peak demand, enabling efficient allocation of resources.

2 HOW DO I APPLY AN IA?

Applying Artificial Intelligence (AI) in railroads involves identifying specific problems or processes that can benefit from this technology and then designing AI-based solutions that optimize the operation, safety and sustainability of the system. The following will detail the key steps to implement an AI solution in the railway sector.

1.1 IDENTIFY SPECIFIC NEEDS

Evaluates critical areas of the rail system that can benefit from AI. Some applications may include:

Predictive maintenance: Monitoring and analysis of the condition of assets (tracks, trains, stations).
Schedule and traffic optimization: Real-time adjustment to avoid delays and congestion.
Safety: Detection of anomalies on tracks or passenger behavior.
Energy management: Minimization of energy consumption in operations.

1.2 COLLECT AND PREPARE DATA

AI depends on quality data to generate accurate results. This involves:

Installing sensors and data collection systems: Sensors on trains, rails and stations collect information on vibrations, temperature, wear and tear, etc.
Consolidating databases: Centralize and structure historical and real-time data.
Clean and tag data: Eliminate errors or irrelevant data and organize information to facilitate AI model training.

1.3 APPROACH

Select the type of AI model depending on the problem to be solved:

Supervised learning: for specific predictions such as predictive maintenance.
Unsupervised learning: For identifying patterns in complex data, such as fault clustering.
Reinforcement learning: For autonomous systems, such as automated trains.
Machine vision: For track and safety monitoring through image or video analysis.

1.4 DESIGN AND TRAINING

Model development: Uses tools and platforms such as TensorFlow, PyTorch or Azure ML to build custom algorithms.
Training: Feed the model with the collected data so that it learns to recognize patterns or make predictions.
Validation: Tests the model on a different data set to ensure its accuracy.

1.5 IMPLEMENTATION AND MONITORING

Once the model has been trained, it is important to integrate it into the railway system:

Real-time deployment: Installs the model on platforms that analyze live data, such as traffic control or maintenance systems.
Continuous monitoring: Monitor the model's performance and update it regularly with new data.

1.6 SCALE UP AND IMPROVE

With early results, identify new opportunities to apply AI and improve existing systems. Some ideas include:

IoT (Internet of Things) integration: Connect smart sensors to obtain real-time data.
Develop user-friendly interfaces: Create dashboards that allow operators and managers to visualize and understand the results of the AI model.
Collaboration with experts: Work with companies specializing in AI and rail transport to implement advanced solutions.

3 CHALLENGES IN THE IMPLEMENTATION OF IA.

Implementing artificial intelligence (AI) in railroads presents several challenges, which can be divided into technological, economic, operational and regulatory aspects. Despite its benefits, the implementation of AI in railroads faces several challenges, ranging from social impact issues to cybersecurity issues. The following are the main obstacles that may arise during implementation.

1.1 INTEGRATION WITH LEGACY SYSTEMS

Many rail systems operate with legacy technology, making it difficult to adopt new AI-based solutions.

Outdated infrastructure: Traditional sensors and control systems may not be compatible with advanced algorithms.
Interoperability: Integrating AI with existing software and hardware requires extensive adaptation efforts.

1.2 INITIAL INVESTMENT COSTS

Development and implementation of AI-based systems requires significant investment in hardware, software, and personnel training.

Advanced equipment and sensors: Required to collect real-time data (sensors on tracks, locomotives, stations).
Employee training: Operators and technicians must learn to use the new technologies.

1.3 DATA QUALITY AND QUANTITY

For an AI model to be accurate and reliable, it needs high quality and high quantity data.

Insufficient or inconsistent data: It can be difficult to obtain reliable historical records on failures, maintenance, or rail traffic.
Storage and processing: The volumes of data generated by sensors can be enormous and require advanced computing infrastructures.

1.4 SAFETY AND CYBERSECURITY

The use of AI in railroads involves the transmission of critical data, which makes them vulnerable to cyber attacks.

Cyber-attacks: Hackers could compromise railway traffic control systems.
Data protection: Privacy and security policies must be ensured in information management.
1.5 REGULATIONS AND STANDARDS

The adoption of AI in railroads must comply with national and international regulations.

Regulatory compliance: In some countries, the automation of certain railway processes may be restricted.
Certification of autonomous systems: Validation of AI models is required prior to implementation in critical operations.

1.6 RESISTANCE TO CHANGE

Railway personnel and companies may show resistance to the adoption of new technologies.

Lack of confidence: Automation may generate fear of job loss or operational errors.
Change in operational processes: Adapting AI requires a redesign of maintenance, traffic and safety management.

4 CONCLUSION.

Artificial Intelligence can transform the rail sector, offering solutions that improve efficiency, safety and sustainability. While there are challenges, the long-term benefits far outweigh the initial difficulties. As the technology continues to evolve, we are likely to see even greater integration of AI into all aspects of rail transport, driving a new era of innovation and connectivity.

Implementation will require planning, substantial infrastructure investment and technical expertise to see AI operating, including changing national or international standards for implementation. However, the long-term benefits; such as increased operational efficiency, reduced environmental impact and improved passenger experience make it worth the effort. Despite the challenges of implementing AI on railroads, it will offer long-term benefits in efficiency, safety and sustainability.

Autor.

Rene ArellanoResponsible for the topography area of Egis in Mexico.

Insight Tags.

Insights • 10 Mins •

Geomatics in Railways

Trazado Geometrico De Ferrocarriles Analisis De Las Principales Soluciones De Software En La Industria

Insights • 5 Mins •

Railroad Geometric Tracing: Analysis of Leading Software Solutions in the Industry

Press & media Our white papers

Stay updated with our newsletter

Follow Global Feed •