The Future of Transportation: How to Improve Railway Operation Performance via Advanced AI Techniques

Li, B; Guo, T; Wang, Y; Chen, F

The Future of Transportation: How to Improve Railway Operation Performance via Advanced AI Techniques

Li, B Guo, T

Wang, Y

Chen, F

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Publisher:: Springer
Publication Type:: Chapter
Citation:: Humanity Driven AI, 2022, pp. 85-110
Issue Date:: 2022-12-02

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Field	Value	Language
dc.contributor.author	Li, B
dc.contributor.author	Guo, T https://orcid.org/0000-0001-5130-3237
dc.contributor.author	Wang, Y https://orcid.org/0000-0002-6815-0879
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.date.accessioned	2023-03-14T22:11:22Z
dc.date.available	2023-03-14T22:11:22Z
dc.date.issued	2022-12-02
dc.identifier.citation	Humanity Driven AI, 2022, pp. 85-110
dc.identifier.isbn	978-3-030-72187-9
dc.identifier.uri	http://hdl.handle.net/10453/167327
dc.description.abstract	Transportation plays an essential role in the urban area. Efficiency and effectiveness management can improve the operation level and safety of cities and reduce the cost. Meanwhile, modern transportation systems continue to be improved to become more intelligent by deploying advanced sensors and other techniques. The smarter transportation systems generate massive valuable data daily. With the introduction of artificial intelligence, it is a powerful method and can capture hidden correlations and feature in the data and model them as a system. Benefits from the advantage, it can provide excellent decision support to the management of transportation. Well-functioning urban transport networks are essential for the free-flow of people and freight. Of all types of transportation, urban railways are an important form of conduit for those movements. Rail has key strengths in long-distance urban travel and in radial-based commuting travel linking city centers with suburbs. In this chapter, we propose data-driven machine learning solutions to evaluate the train performance and decision making, which are applied to the critical transportation system in Great Sydney Area (Sydney Trains). The solutions aim to ensure timetables/response plans are operationally robust and resilient. By applying advanced AI techniques, our methods enable the railway services to meet the performance metrics and recover from incidents with consideration of a range of impacting factors.
dc.format.extent	15
dc.language	en
dc.publisher	Springer
dc.relation	iMOVE Australia Limited1-019
dc.relation.ispartof	Humanity Driven AI
dc.relation.isbasedon	10.1007/978-3-030-72188-6_5
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	The Future of Transportation: How to Improve Railway Operation Performance via Advanced AI Techniques
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-14T22:11:20Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
dc.location	Switzerland

Abstract:

Transportation plays an essential role in the urban area. Efficiency and effectiveness management can improve the operation level and safety of cities and reduce the cost. Meanwhile, modern transportation systems continue to be improved to become more intelligent by deploying advanced sensors and other techniques. The smarter transportation systems generate massive valuable data daily. With the introduction of artificial intelligence, it is a powerful method and can capture hidden correlations and feature in the data and model them as a system. Benefits from the advantage, it can provide excellent decision support to the management of transportation. Well-functioning urban transport networks are essential for the free-flow of people and freight. Of all types of transportation, urban railways are an important form of conduit for those movements. Rail has key strengths in long-distance urban travel and in radial-based commuting travel linking city centers with suburbs. In this chapter, we propose data-driven machine learning solutions to evaluate the train performance and decision making, which are applied to the critical transportation system in Great Sydney Area (Sydney Trains). The solutions aim to ensure timetables/response plans are operationally robust and resilient. By applying advanced AI techniques, our methods enable the railway services to meet the performance metrics and recover from incidents with consideration of a range of impacting factors.

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http://hdl.handle.net/10453/167327