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Challenges with Driverless and Unattended Train Operations

Time: Wed 2023-09-27 14.15

Location: M108, Brinellvägen 23, Stockholm

Video link:

Language: English

Subject area: Transport Systems Transport Science

Doctoral student: Emil Jansson , Transportplanering

Opponent: Professor Ronghui Liu, University of Leeds

Supervisor: Docent Oskar Fröidh, Transportplanering, Järnvägsgruppen, JVG; Professor Nils O.E. Olsson, Norwegian University of Science and Technology, Department of Mechanical and Industrial Engineering; Dr. Ingrid Johansson, Transportplanering, Järnvägsgruppen, JVG; Biträdande universitetslektor Carl-William Palmqvist, Lund University, Department of Technology and Society; Dr. Olov Lindfeldt, Transportplanering, MTR Pendeltåg

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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.