About

The RAILCLIMA integrates the climate change considerations into the development of railway technology, such as train control (both human and automatic), signaling, and maintenance, through real-time estimation of wheel–rail friction conditions.

It adopts an interdisciplinary approach that combines computer vision with model-based estimation using data collected from operational vehicles. This smart system will provide real-time insights into the effects of weather conditions on train dynamics and their impact on asset life cycles.

Background

The European Green Deal has promoted the use of rail transportation to combat the climate crisis. However, global climate change poses significant challenges to the safety and reliability of rail operations, as well as to the resilience of railway infrastructure.

Goal

The RAILCLIMA at Tampere University aims to transform current railway technology—including train control, signaling systems, and maintenance—by integrating climate considerations into their development and implementation.

The project combines a computer vision approach with model-based estimation, creating a novel, interdisciplinary method for real-time wheel-rail friction monitoring.

Impact

This innovation will enhance railway system performance by running computational models directly on in-service trains, using their outputs to optimize train dynamics and enable data-driven maintenance. Furthermore, the use of cost-effective instrumentation and shared computational analysis systems will help vehicle manufacturers to reduce cost.

Structure

WP1. Computer vision–based displacement measurement approach

The goal of WP1 is to estimate the dynamic wheel-rail displacement using data from the Tampere wheel rig and field measurements.

WP2. Model-based wheel–rail friction estimator

WP2 will develop a model-based technique for estimating the wheel–rail friction by integrating a simplified vehicle model with a Kalman filter (KF).

WP3. Experimentation

The goal of WP3 is to provide measurement data and validation results.

WP4. Observation and wear estimation

WP4 aim to estimate and validate the wheel-rail friction and track irregularities.

Funding

RAILCILIMA is funded by 2026 Academy Research Fellowships (377523).

Link: RAILCLIMA: Climate-Aware Wheel–Rail Condition Monitoring Through Computer Vision and Model-Based Friction Estimation – Research.fi

Partners and co-operators

Prof. Mehdi Ahmadian, full professor, Director of the Center for Vehicle Systems & Safety at Virginia Tech in USA.
Prof. Zili Li, full professor, Director of Delft Rail Institute at Delft University of Technology in the Netherlands.
Prof. Javier Cuadrado, full professor and head of the Mechanical Engineering Laboratory at University of A Coruña in Spain.
Prof. Jose Escalona, full professor, head of Railway Group at University of Seville in Spain.
Prof. Paul Allen, full professor and Director of the Institute of Railway Research at University of Huddersfield in UK.
Sami Kalevirta, head of Digital Solution Unit at VR Fleetcare in Finland.
Ville Mattila , head of Smart Maintenance at VR Fleetcare in Finland.
Henri Seppälä, Technology and Environment, Railway Engineering Unit, Infrastructure Management at Finnish Transport Infrastructure Agency in Finland.
Marko Lehtosaari, Track Maintenance, Track Maintenance Control and Development Unit, at Finnish Transport Infrastructure Agency in Finland.