Methodology for the Characterisation of Linear Rail Transport Infrastructures with the Machine Learning Technique and Their Application in a Hyperloop Network

Urban Rail Transit ◽

10.1007/s40864-021-00149-4 ◽

2021 ◽

Author(s):

José Ángel Fernández Gago ◽

Federico Collado Pérez-Seoane

Keyword(s):

Machine Learning ◽

Capital Expenditure ◽

Rail Transport ◽

Transport Infrastructure ◽

Planning Stage ◽

Railway Line ◽

Geometric Patterns ◽

Two Cities ◽

Learning Technique ◽

Strongly Connected

AbstractThe layout solution for linear rail transport infrastructure will always alternate ‘surface’ sections with ‘tunnel’ and ‘viaduct’ sections. The capital expenditure (CapEx) linked at the planning stage to this type of public asset is strongly connected to the quantity of tunnels and viaducts planned. In this context, for similar lengths, a railway line using 15% tunnels and 7% viaducts to link two cities should not have the same financial viability as one using 8% tunnels and 3% viaducts to link the same cities. The process of planning, design and construction of linear works is heavily scrutinised by public administrations in all countries, and in many cases similar standards of work are shared. Firstly, this research paper highlights the existence of hidden geometric patterns in all linear transport infrastructures worldwide. Secondly, it proposes to exploit the existence of such patterns for the benefit of planners through the computational power available today in machine learning-as-a-service (MLaaS) platforms. This article demonstrates how geometric features extracted from any succession of rectangular trapeziums in linear infrastructures can predict the quantity of kilometres in ‘surface’, ‘tunnel’ and ‘viaduct’ sections in future linear rail transport infrastructures that have not yet been built. The practical application of the proposed working methodology has made it possible to intuit the characteristics of a future Hyperloop transport network in Europe of more than 12,000 km in length.

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Machine Learning of Motor Vehicle Accident Categories from Narrative Data

Methods of Information in Medicine ◽

10.1055/s-0038-1634680 ◽

1996 ◽

Vol 35 (04/05) ◽

pp. 309-316 ◽

Cited By ~ 4

Author(s):

M. R. Lehto ◽

G. S. Sorock

Keyword(s):

Machine Learning ◽

Bayesian Model ◽

Keyword Search ◽

Motor Vehicle ◽

Motor Vehicle Accident ◽

Computer Search ◽

Vehicle Accident ◽

Learning Technique ◽

Expert Ratings ◽

Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

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Cryptocurrency Trading-Pair Forecasting, Using Machine Learning and Deep Learning Technique

SSRN Electronic Journal ◽

10.2139/ssrn.3610340 ◽

2020 ◽

Author(s):

Ernest Osifo ◽

Ritabrata Bhattacharyya

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Technique

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What Should Investors Care About? Mutual Fund Ratings by Analysts vs. Machine Learning Technique

SSRN Electronic Journal ◽

10.2139/ssrn.3702749 ◽

2020 ◽

Author(s):

Si Cheng ◽

Ruichang Lu ◽

Xiaojun Zhang

Keyword(s):

Machine Learning ◽

Mutual Fund ◽

Machine Learning Technique ◽

Learning Technique

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The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽

10.3390/atmos11010111 ◽

2020 ◽

Vol 11 (1) ◽

pp. 111 ◽

Cited By ~ 2

Author(s):

Chul-Min Ko ◽

Yeong Yun Jeong ◽

Young-Mi Lee ◽

Byung-Sik Kim

Keyword(s):

Machine Learning ◽

Heavy Rainfall ◽

Extreme Rainfall ◽

Machine Learning Techniques ◽

Precipitation Forecast ◽

Machine Learning Technique ◽

Rainfall Forecast ◽

Quantitative Precipitation Forecast ◽

Correction Technique ◽

Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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