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

Springer Science and Business Media LLC - 2021
José Ángel Fernández Gago1, Federico Collado Pérez-Seoane1
1Ingeniería y Economía del Transporte S.M.E. M.P. S.A. (Ineco), Madrid, Spain

Tóm tắt

The 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.

Từ khóa


Tài liệu tham khảo

Musk, E (2013) Hyperloop Alpha. SpaceX

AASHTO (2001) A policy on geometric desing of highways and streets. pp. ix (Preface)

LópezArchila MC, EcheverryArciniegas CA (1999) Manual de DiseñoGeométrico para Carreteras. Ciencia e IngenieríaNeogranadina 8(145–156):155

National Association of Australian State Road Authorities (1972) Guide Policy for Geometric Design o Freeways and Expressways

The Indian Road Congress (1973) Recommendations about the Alignment Survey and Geometric Design of Hill Roads

Secretaria de Comunicaciones y Transportes, Dirección General de Servicios Técnicos de los Estados Unidos Mexicanos (1976) Manual de Proyecto Geométrico de Carreteras

Actisa Development Team (2014) Tadil software. Spain

Zhu Y, Chen F, Wang Z (2019) Spatio-temporal analysis of rail station ridership determinants in the built environment. Transportation 46:2269–2289

Sysyn M, Gerber U, Nabochenko O (2019) Prediction of rail contact fatigue on crossings using image processing and machine learning methods. Urban Rail Transit 5:123–132

Ghasedi M, Sarfjoo M, Bargegol I (2021) Prediction and analysis of the severity and number of suburban accidents using logit model, factor analysis and machine learning: a case study in a developing country. SN Appl. Sci. 3:13

Yan X, Radwan E, Abdel-Aty M (2005) Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model. Accid Anal Prev 37(6):983–995

Ochmanski M, Modoni G, Bzowka J (2015) Prediction of the diameter of jet grouting columns with artificial neural networks. Soils Found 55(2):425–436

Hadj-Mabrouk H (2019) Contribution of artificial intelligence to risk assessment of railway accidents. Urban Rail Transit 5:104–122

Faghih-Roohi S, Hajizadeh S, Núñez A, Babuska R, De Schutter B (2016) Deep convolutional neural networks for detection of rail surface defects. In: International joint conference on neural networks (IJCNN), Vancouver, BC, 2016, pp 2584–2589

You J, Wang J, Guo J (2017) Real-time crash prediction on freeways using data mining and emerging techniques. J Mod Transport 25:116–123

Shamshirband S, Rabczuk T, Chau K (2019) A survey of deep learning techniques: application in wind and solar energy resources

Santur Y, Karaköse M, Akin E (2017) A new rail inspection method based on deep learning using laser cameras. In: International artificial intelligence and data processing symposium (IDAP), Malatya, pp 1–6 (2017)

TayefehHashemi S, Ebadati OM, Kaur H (2020) Cost estimation and prediction in construction projects: a systematic review on machine learning techniques. SN Appl Sci 2:1703

Amin M (2017) Development of cost estimation model for residential building. Int J CivStructEng Res 5(1):1–4

Jiang Q (2019) Estimation of construction project building cost by back-propagation neural network. J Eng Des Technol 18(3):601–609

BigML (2011) BigML, Inc. Corvallis, Oregon, USA

Berthold M (2006) KNIME (Konstanz Information Miner). Universität Konstanz

Actisa SL (2020) High speed railway line from Santiago de Chile to Valparaiso and Viña del Mar. Northern Corridor or Southern Corridor?

Centro Nacional de Información Geográfica, file: RT-ESPANA-PORMODOS.ZIP. 15 November 2018

QGIS Development Team (2002) QGIS Noosa. Windows—GNU/Linux—Mac OS X—BSD—Android. USA: Open Source Geospatial Foundation

Schneider A (2002) GPS visualizer. USA: GPS Visualizer

Thông tin
Thông tin xuất bản

Springer Science and Business Media LLC

Thông tin tác giả