Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

Big Data Analytics in Intelligent Transportation Systems: A Survey

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 is critical in modern operations management (OM). In this study, we first explore the existing big data‐related analytics techniques, and identify their strengths, weaknesses as well as major functionalities. We then discuss various big data analytics strategies to overcome the respective computational and data challenges. After that, we examine the literature and reveal how different types of big data methods (techniques, strategies, and architectures) can be applied to different OM topical areas, namely forecasting, inventory management, revenue management and marketing, transportation management, supply chain management, and risk analysis. We also investigate via case studies the real‐world applications of big data analytics in top branded enterprises. Finally, we conclude the study with a discussion of future research.

Big Data Analytics in Operations Management

A literature study for DEA applied to energy and environment

An investigation into the causes of squats—Correlation analysis and numerical modeling

Axle box acceleration: Measurement and simulation for detection of short track defects

This paper presents an automatic method for detecting railway surface defects called “squats” using axle box acceleration (ABA) measurements on trains. The method is based on a series of research results from our group in the field of railway engineering that includes numerical simulations, the design of the ABA prototype, real-life implementation, and extensive field tests.We enhance the ABA signal by identifying the characteristic squat frequencies, using improved instrumentation for making measurements, and using advanced signal processing. The automatic detection algorithm for squats is based on wavelet spectrum analysis and determines the squat locations. The method was validated on the Groningen–Assen track in The Netherlands and accurately detected moderate and severe squats with a hit rate of 100%, with no false alarms. The methodology is also sensitive to small rail surface defects and enables the detection of squats at their earliest stage. The hit rate for small rail surface defects was 78%.

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Automatic detection of squats in railway infrastructure

Squat growth—Some observations and the validation of numerical predictions

Railway infrastructure monitoring is a vital task to ensure rail transportation safety A rail failure could result in not only a considerable impact on train delays and maintenance costs, but also on safety of passengers In this article, the aim is to assess the risk of a rail failure by analyzing a type of rail surface defect called squats that are detected automatically among the huge number of records from video cameras We propose an image processing approach for automatic detection of squats, especially severe types that are prone to rail breaks We measure the visual length of the squats and use them to model the failure risk For the assessment of the rail failure risk, we estimate the probability of rail failure based on the growth of squats Moreover, we perform severity and crack growth analyses to consider the impact of rail traffic loads on defects in three different growth scenarios The failure risk estimations are provided for several samples of squats with different crack growth lengths on a busy rail track of the Dutch railway network The results illustrate the practicality and efficiency of the proposed approach

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Rolf Dollevoet

Papers

An investigation into the causes of squats—Correlation analysis and numerical modeling

Axle box acceleration: Measurement and simulation for detection of short track defects

Automatic detection of squats in railway infrastructure

Squat growth—Some observations and the validation of numerical predictions

A big data analysis approach for rail failure risk assessment