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Machine learning-assisted macro simulation for yard arrival prediction
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- Minbashi, Niloofar, 1990- (författare)
- KTH Royal Institute of Technology,KTH,Transportplanering
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- Sipilä, Hans, 1975- (författare)
- KTH Royal Institute of Technology,KTH,Transportplanering
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- Palmqvist, Carl-William (författare)
- KTH Royal Institute of Technology,Lund University,Lunds universitet,KTH,Transportplanering,Trafik och väg,Institutionen för teknik och samhälle,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: Energiomställningen,LTH profilområden,Järnvägsteknik,Forskargrupper vid Lunds universitet,Transport and Roads,Department of Technology and Society,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: The Energy Transition,LTH Profile areas,Faculty of Engineering, LTH,Railway Operation,Lund University Research Groups
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- Bohlin, Markus, 1976- (författare)
- KTH Royal Institute of Technology,KTH,Transportplanering
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- Kordnejad, Behzad, 1980- (författare)
- KTH Royal Institute of Technology,KTH,Transportplanering
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(creator_code:org_t)
- Elsevier BV, 2023
- 2023
- Engelska.
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Ingår i: Journal of Rail Transport Planning & Management. - : Elsevier BV. - 2210-9706 .- 2210-9714. ; 25
- Relaterad länk:
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https://doi.org/10.1...
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http://dx.doi.org/10... (free)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://lup.lub.lu.s...
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Abstract
Ämnesord
Stäng
- Increasing the modal share of the single wagonload transport in Europe requires improving the reliability and predictability of freight trains running between the yards. In this paper, we propose a novel machine learning-assisted macro simulation framework to increase the predictability of yard departures and arrivals. Machine learning is applied through a random forest algorithm to implement a yard departure prediction model. Our yard departure prediction approach is less complex compared to previous yard simulation approaches, and provides an accuracy level of 92% in predictions. Then, departure predictions assist a macro simulation network model (PROTON) to predict arrivals to the succeeding yards. We tested this framework using data from a stretch between two main yards in Sweden; our experiments show that the current framework performs better than the timetable and a basic machine learning arrival prediction model by R2 of 0.48 and a mean absolute error of 35 minutes. Our current results indicate that combination of approaches, including yard and network interactions, can yield competitive results for complex yard arrival time prediction tasks which can assist yard operators and infrastructure managers in yard re-planning processes and yard-network coordination respectively.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Yards
- Delay prediction
- Macroscopic simulation
- Machine learning
- Rail traffic
- Delay prediction
- Machine learning
- Macroscopic simulation
- Rail traffic
- Yards
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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