Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Tackling Climate Change with Machine Learning

Big data analytics (BDA) has increasingly attracted a strong attention of analysts, researchers and practitioners in railway transportation and engineering. This urges the necessity for a review of recent research development in this field. This survey aims to provide a comprehensive review of the recent applications of big data in the context of railway engineering and transportation by a novel taxonomy framework, proposed by Mayring (2003). The survey covers three areas of railway transportation where BDA has been applied, namely operations, maintenance and safety. Also, the level of big data analytics, types of big data models and a variety of big data techniques have been reviewed and summarized. The results of this study identify the existing research gaps and thereby directions of future research in BDA in railway transportation systems.

Recent applications of big data analytics in railway transportation systems: A survey

Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change. 

Reliability and availability analysis of stochastic degradation systems based on bivariate Wiener processes

Track geometry data exhibits classical big data attributes: value, volume, velocity, veracity and variety. Track Quality Indices-TQI are used to obtain average-based assessment of track segments and schedule track maintenance. TQI is expressed in terms of track parameters like gage, cross-level, etc. Though each of these parameters is objectively important but understanding what they collectively convey for a given track segment often becomes challenging. Several railways including passenger and freight have developed single indices that combines different track parameters to assess overall track quality. Some of these railways have selected certain parameters whilst dropping others. Using track geometry data from a sample mile track, we demonstrate how to combine track geometry parameters into a low dimensional form (TQI) that simplifies the track properties without losing much variability in the data. This led us to principal components. To validate the use of principal components as TQI, we employed a two-phase approach. First phase was to identify a classic machine learning technique that works well with track geometry data. The second step was to train the identified machine learning technique on the sample mile-track data using combined TQIs and principal components as defect predictors. The performance of the predictors were compared using true and false positive rates. The results show that three principal components were better at predicting defects and revealing salient characteristics in track geometry data than combined TQIs even though there were some correlations that are potentially useful for track maintenance.

Principal components analysis and track quality index: A machine learning approach

Increased demand for railway transportation is creating a need for higher train speeds and axle loads. These, in turn,increase the likelihood of track degradation and failures. Modelling the degrad ...

Track geometry degradation and maintenance modelling: A review:

The objective of this study has been to develop an approach to the allocation of an effective maintenance limit for track geometry maintenance that leads to a minimisation of the total annual maint...

Allocation of effective maintenance limit for railway track geometry

The aim of this study has been to develop a data-driven analytical methodology for prediction of isolated track geometry defects, based on the measurement data obtained from a field study. Within t...

Prediction of railway track geometry defects: a case study

The aim of this study has been to predict the track geometry degradation rate using artificial neural network. Tack geometry measurements, asset information, and maintenance history for five line s...

https://www.tandfonline.com/doi/pdf/10.1080/23248378.2021.1875065

Prediction of track geometry degradation using artificial neural network: a case study

Tamping is one of the major activities applied by railway maintenance managers to recover track geometry condition. Modelling the tamping effectiveness along with track geometry degradation is esse ...

Iman Soleimanmeigouni

Papers

Track geometry degradation and maintenance modelling: A review:

Allocation of effective maintenance limit for railway track geometry

Prediction of railway track geometry defects: a case study

Prediction of track geometry degradation using artificial neural network: a case study

Evaluation of the effect of tamping on the track geometry condition: A case study: