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

During the last decade, self-driving technology has become increasingly visible in the news, with the vision that people would enter 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 used to 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 acting as 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 and 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.

• Record URL:
  http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-312391
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9789180402675
• Authors:
  • Almlöf, Erik
• Publication Date: 2022

Language

• English

Media Info

• Pagination: 65
• Serial:
  • TRITA-ITM-AVL ;
  • Issue Number: 2022:18
  • Publisher: KTH Royal Institute of Technology, Sweden

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Demand; Forecasting; Passenger transportation; Public transit; Scale models; Travel
• ITRD Terms: 1244: Autonomous vehicle; 285: Demand (econ); 132: Forecast; 698: Journey; 6205: Model (not math); 1146: Passenger transport; 744: Public transport
• Subject Areas: Passenger Transportation; Public Transportation; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01878500
• Record Type: Publication
• Source Agency: Swedish National Road and Transport Research Institute (VTI)
• ISBN: 9789180402675
• Files: ITRD, VTI
• Created Date: Apr 3 2023 4:46PM