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Exploring societal impacts of self-driving public transport using four-step transport models

Time: Tue 2022-06-07 10.00 - 12.00

Video link: Zoom (

Doctoral student: Erik Almlöf

Opponent: Clas Rydergren, Associate Professor in Traffic Informatics at Linköping University

Supervisor: Mikael Nybacka, Erik Jenelius and Mia Hesselgren

Examiner: Xiaoliang Ma, Senior researcher and docent in Intelligent Transport Systems

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During the last decade, self-driving technology has become increasingly visible in the news, with the vision that people would enter their self-driving vehicles that drive themselves, and that people could instead rest, read the newspaper, or have a meeting. However, these visions have mainly focused on the potential for car usage, even though public transport could benefit greatly from self-driving technology. For bus traffic, the bus driver accounts for half of the cost of driving, and savings on personnel costs could, for example, be reinvested in expanded public transport service or reduced costs resulting in lower taxes.

At the same time, more research has shown potential problems linked to self-driving technology, for example that more comfortable driving would lead to more traffic, which in turn would lead to increased emissions, higher noise levels in cities or further focus on car-centric infrastructure. For public transport, the driver's role in creating safety and being problem solvers has also been emphasized - who should I ask for directions, if there is no knowledgeable driver on board?

Various methods have previously been used to explore the social effects of self-driving technology, in this dissertation I have used so-called "four-stage models", more specifically the Swedish transport model Sampers. Four-stage models have been used for 50 years to evaluate effects on the transport system from e.g. infrastructure changes, but these models face new challenges, handling vehicles that drive by themselves. In my research, I have adjusted the model to simulate self-driving technology and investigated what effects this has on, for example, traffic volumes and emissions.

In the three articles that are part of the dissertation, I have four main conclusions:

  • Self-driving technology can mean large savings in costs for public transport, primarily for bus traffic but also to some extent for rail traffic. In addition, a smoother driving behaviour would mean more comfortable travel, which would increase the attractiveness of public transport. In addition, public transport not limited by, for example, driver schedules or current commercial conditions, could develop new types of services, such as on-demand public transport.
  • Four-stage models have previously been used to model the transport system and have been shown to have good results, at least at an overall level. Within my research, I have made some adaptations of these models to mimic self-driving technology, but the models in their current form cannot consider, for example, vehicle sharing.
  • It is important to point out that bus and train drivers currently perform many tasks that are not directly related to the driving of the vehicle, such as answering questions, maintaining social order among passengers and taking care of faults that occur during the trip. Today, self-driving technology cannot fulfil these roles.
  • Self-driving technology for public transport would affect people's accessibility, driving style for vehicles, safety on board, how we plan traffic and the people who currently work as drivers. In fact, a multitude of societal effects have been identified, affecting all areas of transport. In addition, the effects are generally not similar across geographies, time units or for different actors, which further emphasizes that the total effect is not easy to summarize.