Dynamic Delay Predictions for Large-Scale Railway Networks: Deep and Shallow Extreme Learning Machines Tuned via Thresholdout

Oneto, Luca; Fumeo, Emanuele; Clerico, Giorgio; Canepa, Renzo; Papa, Federico; Dambra, Carlo; Mazzino, Nadia; Anguita, Davide

doi:10.1109/tsmc.2017.2693209

Cited by 83 publications

(37 citation statements)

References 75 publications

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“…As more and more devices join the rapidly rising IoT, there is more and more scope for the railway industry to take advantage of the data. For a high-performance model, a deeper analysis of data comes from different devices, as long as more information is available for the design of the model [81]. This has resulted in an improvement of advanced technologies in the big data analysis (BDA), in both academia and industry [18,82].…”

Section: Machine Learning and The Railway Stationsmentioning

confidence: 99%

Wireless Sensor Networks: Toward Smarter Railway Stations

Alawad

Kaewunruen

2018

Infrastructures

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Railway industry plays a critical role in transportation and transit systems attributed to the ever-growing demand for catering to both freight and passengers. However, owing to many challenges faced by railway stations such as harsh environments, traffic flow, safety and security risks, new and adaptive systems employing new technology are recommended. In this review, several wireless sensor networks (WSNs) applications are proposed for use in railway station systems, including advanced WSNs, which will enhance security, safety, and decision-making processes to achieve more cost-effective management in railway stations, as well as the development of integrated systems. The size, efficiency, and cost of WSNs are influential factors that attract the railway industry to adopt these devices. This paper presents a review of WSNs that have been designed for uses in monitoring and securing railway stations. This article will first briefly focus on the presence of different WSN applications in diverse applications. In addition, it is important to note that exploitation of the state-of-the-art tools and techniques such as WSNs to gain an enormous amount of data from a railway station is a new and novel concept requiring the development of artificial intelligence methods, such machine learning, which will be vital for the future of the railway industry.

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Section: Machine Learning and The Railway Stationsmentioning

confidence: 99%

Wireless Sensor Networks: Toward Smarter Railway Stations

Alawad

Kaewunruen

2018

Infrastructures

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“…In addition to Hadoop MapReduce, another cloud computing technique called Apache Spark has also found railway applications [71][72][73]. Within the Apache Spark framework, there are four main components: a driver node, a number of worker nodes, a cluster manager, and executors (the same number as for worker nodes).…”

Section: Hadoop and Sparkmentioning

confidence: 99%

Parallel computing in railway research

Spiryagin

Cole

et al. 2018

International Journal of Rail Transportation

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Available computing power for researchers has been increasing exponentially over the last decade. Parallel computing is possibly the best way to harness computing power provided by multiple computing units. This paper reviews parallel computing applications in railway research as well as the enabling techniques used for the purpose. Nine enabling techniques were reviewed and Message Passing Interface, Domain Decomposition and Hadoop & Apache are the top three most widely used enabling techniques. Seven major application topics were reviewed and iterative optimisations, continuous dynamics and data & signal analysis are the most widely reported applications. The reasons why these applications are suitable for parallel computing were discussed as well as the suitability of various enabling techniques for different applications. Computing time speed-ups that were reported from these applications were summarised. The challenges for applying parallel computing for railway research are discussed. ARTICLE HISTORY

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“…Kecman and Goverde [6] apply linear regression, decision trees and random forests in order to predict running and dwelling times considering current train position and traffic information. Oneto et al [7] present extreme learning machines used on historical and exogenous data like weather records to predict the delay that will affect a train at next checkpoints with respect to its delay at last visited points and to the delays of other trains running over the same section.…”

Section: Related Workmentioning

confidence: 99%

Estimating Long-Term Delay Risk with Generalized Linear Models

Faverges

Russolillo

Picouleau

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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This paper presents an original methodology to estimate delay risk a few days before operations with generalized linear models. These models represent a given variable with any distribution from the exponential family, allowing to compute for any subject its own probability distribution according to its features. This methodology is applied on small delays (less than 20 minutes) of high-speed trains arriving at a major french station. Several distributions are tested to fit delay data and three scenarios are evaluated: a single GLM with a negative binomial distribution and two two-part models using both a logistic regression as first part to compute the probability of arriving on time, and a second part using a negative binomial or a lognormal distribution to obtain the probabilities associated with positive delay values. This paper also proposes a validation methodology to assess the quality of these probabilistic predictions based on two aspects: calibration and discrimination.

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Dynamic Delay Predictions for Large-Scale Railway Networks: Deep and Shallow Extreme Learning Machines Tuned via Thresholdout

Cited by 83 publications

References 75 publications

Wireless Sensor Networks: Toward Smarter Railway Stations

Wireless Sensor Networks: Toward Smarter Railway Stations

Parallel computing in railway research

Estimating Long-Term Delay Risk with Generalized Linear Models

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