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The use of human senses by train drivers to detect abnormalities
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.ORCID iD: 0000-0002-3687-7758
2023 (English)In: TRA Lisbon 2022 Conference Proceedings Transport Research Arena, Elsevier BV , 2023, Vol. 72, p. 3650-3655Conference paper, Published paper (Refereed)
Abstract [en]

Driverless and unattended train operation is a foreseeable future. While many functions of the driver can be automatized and replaced but detecting abnormalities is more difficult to automate. This study investigates how train drivers detect abnormalities. The objective is to prepare the way for unattended train operation also for remote areas. Using disruption descriptions, written by train dispatchers, we have identified which senses are used by the train drivers and in which situations. Four of the human senses are used by train drivers to detect abnormalities: the visual, the auditory, the somatosensory, and the olfactory systems. The most used sense by the train drivers to detect abnormalities is the visual system. Before introducing driverless and unattended train operation, alternative tools for detecting abnormalities should be included based on the human senses.

Place, publisher, year, edition, pages
Elsevier BV , 2023. Vol. 72, p. 3650-3655
Series
Transportation Research Procedia, ISSN 2352-1457, E-ISSN 2352-1465
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-334582DOI: 10.1016/j.trpro.2023.11.556Scopus ID: 2-s2.0-85182918130OAI: oai:DiVA.org:kth-334582DiVA, id: diva2:1790651
Conference
TRA Lisbon 2022 Conference Proceedings Transport Research Arena (TRA Lisbon 2022),14th-17th November 2022, Lisboa, Portugal
Note

QC 20231218

Available from: 2023-08-23 Created: 2023-08-23 Last updated: 2024-01-31Bibliographically 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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Jansson, EmilFröidh, Oskar

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