Methods for Travel Pattern Analysis Using Large-Scale Passive Data

Abstract

First, I would like to thank my main supervisor Clas Rydergren and my co-supervisor David Gundlegård. I very much appreciate that you have always been available and gave me guidance and inspiration. Thank you for sharing your knowledge in lengthy discussions and taking the time to give feedback that challenged me to continuously improve. I very much enjoyed working together with you! I would also like to thank Lars Sköld, Simon Moritz, Ida Kristofferson and Di Yuan, and all others who made this work possible with their engagement.The research in this thesis has been to a large extend funded by the projects Mobile Network Origin Destination Estimation (MODE) and Mobile Network Data in Future Transport Systems (MOFT), both financed by Vinnova and Demand model estimation based on combination of active and passive data collection (DEMOPAN) funded by Trafikverket.I also want to thank all my colleagues at the division of Communication and Transportation Systems (KTS). I really appreciated being part of a group this international, with different research fields and perspectives. The last year of working from home made it clear how much I miss the interaction and informal discussion with you! In particular, I want to thank the group of PhD students for the great company, fun times and all mutual support. Special thanks go to Niki and Mats for their commitment to represent the PhD students at KTS! A big thank also to Morten Eltved, Jesper Bláfoss Ingvardson and Otto Anker Nielsen at DTU in Copenhagen. I really enjoyed working with you, even though my research visit turned out to be two and a half only weeks instead of three months, due to the pandemic. Yet, two fredagsøl were enough to start appreciating the Danish culture.Finally, I would like to thank all my friends, my sister Annalena and my dear parents Marita and Gerd-Herbert, for their unconditional support. Thank you, Fanny, for all your love and for reminding me that there are other things in life than research.

Towards efficient urban road transport using multimodal traffic management

“…• Public transport ticket data Smart cards have been introduced in the public transport system in Stockholm. It has increased the convenience for the passengers and decreased the costs for operators (Breyer, 2021). The data set of smart card data in the Stockholm region is presented in more details in (Gundlegård et al, 2024).…”

“…Mobile network data refers to data collected by cellular network operators, with penetration rates of up to 20-50 %. The location is typically approximated by the cell location (Breyer, 2021).…”

Trip Attraction Rates of Banking Services in Developing Countries' Cities

“…Many studies used demographic and socioeconomic variables such as employment, level of education, age, average household income, average car ownership, household size, and population density to develop trip generation models [4][5][6]. These models are based on household trip questionnaires, which are expensive and time-consuming to create [7,8]. It is practically impossible to apply the trip-generation model, which includes demographic and socioeconomic variables, to assess the traffic impact of any new development [9].…”