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Challenges of replacing train drivers in driverless and unattended railway mainline systems—A Swedish case study on delay logs descriptions
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.ORCID iD: 0000-0002-1695-4445
Norwegian University of Science and Technology, Department of Mechanical and Industrial Engineering.ORCID iD: 0000-0002-1344-8555
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.ORCID iD: 0000-0002-3687-7758
2023 (English)In: Transportation Research Interdisciplinary Perspectives, ISSN 2590-1982, Vol. 21, p. 100875-100875, article id 100875Article in journal (Refereed) Published
Abstract [en]

Currently, the challenges of driverless or unattended train operation have not been described in detail and are often grouped into one category. This paper contributes to filling a knowledge gap regarding the roles of the train driver about the potential use of automatic train operation (ATO) on high grade of automation (GoA) levels. The results contribute to a better understanding of the challenges with driverless or unattended train operation to support strategies on how to utilize ATO on a wider range of trains than is presently the case. We use the Swedish railway network as a case study and delay logs written by train dispatchers for 2019. Our research quantifies how often unplanned events occur in which the train driver is needed, and the role of the train driver in solving these problems. In addition to this we elaborate on existing GoA levels definitions and propose a revised model that highlights more aspects of the train drivers’ roles. We have identified six categories in which an action by the driver is required: Detect, Report, Inspect, Adjust, Manage passengers, and Respond to train orders. The study illustrates some of the challenges with driverless or unattended train operation, and points to the need to develop strategies not only for the driving aspects of ATO but also for the more general technical operational management of rolling stock in high GoA levels.

Place, publisher, year, edition, pages
2023. Vol. 21, p. 100875-100875, article id 100875
Keywords [en]
Railway, Automatic train operation, Driverless trains, Train driver, Roles, Automation challenges
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-333356DOI: 10.1016/j.trip.2023.100875ISI: 001141847200001Scopus ID: 2-s2.0-85166931478OAI: oai:DiVA.org:kth-333356DiVA, id: diva2:1790644
Funder
Swedish Transport Administration, TRV 2019/123866
Note

QC 20230823

Available from: 2023-08-23 Created: 2023-08-23 Last updated: 2024-02-01Bibliographically approved
In thesis
1. Challenges with Driverless and Unattended Train Operations
Open this publication in new window or tab >>Challenges with Driverless and Unattended Train Operations
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Demand for transportation continues to increase, for both freight and passenger services. One of the most energy-efficient modes of transportation is rail. One solution to increase the attractiveness of rail transport is to introduce automatic train operation (ATO) with a high grade of automation (GoA). Driverless and unattended train operation could entail positive effects but would also bring challenges when removing the train driver. Thus, there is a need to understand the role of train drivers, especially in unplanned events. The main research objectiveis to understand the train driver roles during unplanned events and the frequency of such events. This thesis includes three papers to fulfill the research objective.

This thesis studied delay logs and trackside sensor logs. A qualitative method, thematic analysis, was used to identify themes of the roles performed by train driver from the delay logs. The chi-square test statistical method was used to analyze these trackside sensor logs.

Six main categories of tasks for train drivers were identified for unplanned events. Detect, Report, Inspect, Adjust, Manage passengers, and Respond to train orders. Each category was analyzed for each grade of automation by giving the responsibility for each category. The results highlight in a novel way the varied challenges between grade of automation in mainline systems. Detecting abnormalities was the most common task train drivers performed during unplanned events. Train drivers use four human senses to detect abnormalities: sight, hearing, touch, and smell. This indicates the need for onboard sensors. However, the real challenge is in processing all sensor data to gain anaccurate evaluation of any fault. One specific type of unplanned event in which the train driver is needed involves trackside sensor alarms. Freight trains are ten times more likely to trip an alarm than passenger trains. Alarms are more frequent in colder climate zones during winter months. These differences are statistically significant and indicate that not all lines and train types might be suitable for a high grade of automation.

If driverless or unattended train operation will become a reality in future, many challenges must be met. This thesis gives deeper understanding of these challenges using a novel way to identify and quantify train driver tasks during unplanned events.

Abstract [sv]

Efterfrågan på transporter fortsätter att öka, både gods- och persontransporter. Ett av de mest energieffektiva transportmedlen är järnväg. En möjlighet att öka järnvägens attraktivitet skulle kunna vara att introducera automatic train operation (ATO) med en hög grad av automatisering. Förarlös och obemannad tågdrift skulle kunna medföra postiva effekter, men det skulle också medföra utmaningar med att ta bort lokföraren. Det finns därför ett behov att förstå lokförarens roll, speciellt i oplanerade situationer. Huvudsyftet är att förstå de olika rollerna lokföraren har vid oplanerade situationer och även frekvensen av dessa situationer. Licentiatuppsatsen är uppbyggd av tre vetenskapliga artiklar för uppnå syftet.

Den här licentiatuppsatsen har använt förseningsbeskrivningar och detektorloggar. En kvalitativ metod, tematisk analys, har använts för att identifiera teman för lokförarnas olika roller utifrån förseningsbeskrivningarna. En statistisk metod, chi-square-test, har använts för att analysera detektorloggarna. 

Sex huvudkategorier av lokförarens roller vid oplanerade händelser har identifierats: Upptäcka, Rapportera, Kontrollera, Justera, Hantera resenärer och Hantera tågordrar. Varje kategori har analyserats utifrån de olika graderna av automation genom att ge visa hur de skulle kunna genomföras. Resultaten belyser de olika utmaningarna mellan graderna av automation på ett nytt sätt i ett nationellt järnvägssystem. Att upptäcka felaktigheter var den vanligaste uppgiften för lokförare vid oplanerade händelser. Lokförare använder fyra sinnen för att upptäcka felaktigheter, syn, hörsel, känsel och lukt. Det indikerar behovet av ombordsensorer, men den stora utmaningen blir att hantera all sensordata för en korrekt bedömning av verkliga fel. En specifik oplanerad händelse då lokföraren behövs är vid detektorlarm. Godståg har en tio gånger högre risk att utlösa ett detektorlarm än ett persontåg. Detektorlarm förekommer oftare i kallt klimat under vintermånader. Skillanderna är statistiskt säkerställda och ger en indikation på att alla sträckor och tågtyper inte är lämpliga för en hög grad av automatisering. 

Om förarlösa eller obemannade tåg ska bli en verklighet i framtiden behöver flera utmaningar hanteras. Den här licentiatuppsatsen ger en djupare förståelse av dessa utmaningar genom att använda ett nytt sätt att identifiera lokförarnas uppgifter vid oplanerade händelser. 

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2023. p. 34
Series
TRITA-ABE-DLT ; 2334
Keywords
Railway, Automatic train operation, Driverless trains, Train driver, Roles, Automation challenges, Human senses, Trackside sensors
National Category
Transport Systems and Logistics
Research subject
Transport Science, Transport Systems; Transport Science
Identifiers
urn:nbn:se:kth:diva-334887 (URN)978-91-8040-690-1 (ISBN)
Presentation
2023-09-27, M108, Brinellvägen 23, KTH Campus, https://kth-se.zoom.us/s/66592546366,, Stockholm, 14:15 (English)
Opponent
Supervisors
Funder
Swedish Transport Administration, TRV 2019/123866EU, Horizon 2020, 881806 - X2Rail-4 - H2020-S2RJU- 2019/H2020-S2RJU-CFM-2019
Note

QC230830

Available from: 2023-08-30 Created: 2023-08-29 Last updated: 2023-09-04Bibliographically approved

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Publisher's full textScopushttps://authors.elsevier.com/sd/article/S2590-1982(23)00122-7

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Jansson, EmilFröidh, Oskar

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