Doctoral theses at NTNU, 2019:69 Anette Østbø Sørensen Østbø Anette Anette Østbø Sørensen Exploiting available data sources for ex-post evaluation of railway projects Doctoral thesis Doctoral Case illustrations with traffic and mobile phone data ISBN 978-82-326-3742-3 (printed ver.) ISBN 978-82-326-3743-0 (electronic ver.) ISSN 1503-8181 Doctoral theses at NTNU, 2019:69 Doctoral NTNU Engineering Philosophiae Doctor Philosophiae Faculty of Engineering Faculty Thesis for the Degree of the Degree Thesis for Department of Mechanical and Industrial Department of Mechanical Norwegian University of Science and Technology of Science University Norwegian Anette Østbø Sørensen Exploiting available data sources for ex-post evaluation of railway projects Case illustrations with traffic and mobile phone data Thesis for the Degree of Philosophiae Doctor Trondheim, March 2019 Norwegian University of Science and Technology Faculty of Engineering Department of Mechanical and Industrial Engineering NTNU Norwegian University of Science and Technology Thesis for the Degree of Philosophiae Doctor Faculty of Engineering Department of Mechanical and Industrial Engineering © Anette Østbø Sørensen ISBN 978-82-326-3742-3 (printed ver.) ISBN 978-82-326-3743-0 (electronic ver.) ISSN 1503-8181 Doctoral theses at NTNU, 2019:69 Printed by NTNU Grafisk senter Preface This thesis is prepared in partial fulfilment of the requirements for the degree of Philosophiae doctor (PhD) at the Department of Mechanical and Industrial Engi- neering under the Faculty of Engineering Science and Technology at the Norwe- gian University of Science and Technology (NTNU). The work was conducted from September 2014 until November 2018. The PhD research has been carried out in close collaboration with my main su- pervisor, Professor Nils O. E. Olsson at the Department of Mechanical and Industrial Engineering. My co-supervisors are research scientist Andreas Dypvik Landmark at SINTEF Digital and senior research scientist Agnar Joahnsen at SINTEF Building and Infrastructure. My interest and curiosity in utilising available data sources to support processes in companies started growing during the final year of my master's degree studies and my work with the Department of Technology Management at SINTEF. While working on this PhD, I have experienced both successful and challenging periods. One of the more demanding challenges I found throughout these studies was moving between disciplines. With my background in industrial mathematics, I am not unfamiliar with numbers, but I was less familiar with the project manage- ment discipline. The interdisciplinarity made the PhD work both challenging and rewarding in the struggle to recognise each disciplines' integrity - in the way they think and the way they write. iii iv PREFACE Acknowledgements This thesis would not have become a reality without those who have contributed to the research and supported me throughout the process. First and foremost, I would like to thank my supervisor, Nils Olsson, for invalu- able guidance and support during the project period, for his positive engagement, for connecting me to people in the industry and research field, and for invaluable discussions and guidance through the final stages of writing the dissertation. I would also like to thank my co-supervisors, Andreas Landmark, for constructive discussions on opportunities and methods, and Agnar Johansen, for informative talks and for his interest in my work. And both for the helpful discussions and feedback about writing the dissertation. In addition, I wish to thank several co-authors who have played an important role in this research. Thanks to Johannes Bjelland at Telenor, Siva Ekambaram, Heidi Bull-Berg and Andreas Seim at SINTEF, and Mohsin Akhtar for their cooperation. I have learned a lot through working with them. I would like to thank Telenor, BaneNor and other railway operators for granting me access to data. Thanks are also due to Johan Nystr¨omand Ida Kristoffersson at the Swedish National Road and Transport Research Institute (VTI) Stockholm, Johan for or- ganising the VTI-seminar and Ida for playing the role of opponent and providing valuable feedback on the study I presented. I am very grateful for the opportunity I had to stay at the Construction Industry Institute (CII) in Austin, Texas, in the United States, and for the opportunity to attend the CII Seminar while I was in Austin. I would therefore like to thank Stephen Mulva, director of CII; Daniel Oliveira, research associate at CII; and Terri Buvia, Cynthia King, Hyeon-Yong Park and the other co-workers and students at the CII office. I want to thank SINTEF Technology and Society for providing me with office space. Thanks also go to Tor Nicolaisen at BaneNor for providing me a desk at the offices at Marienborg in the early phase of the PhD work. I also want to thank my fellow PhD researchers at NTNU and colleagues at SINTEF Digital, Department of Technology Management, who have made the time at the office more enjoyable. I am also thankful to all my friends in Trondheim with whom I have spent time outside of the office, especially Karen Synnøve for our coffee breaks during the first years, and Sandra, Marcin and Tomas. I am very grateful to my family, my parents Elin and Arne, and my sister v vi ACKNOWLEDGEMENTS Benedikte for their unwavering support and encouragement and for always showing interest in my work and giving me energy. Finally, I want to thank my boyfriend Are Bertheussen for his support and encouragement, for providing me with good distractions and for helping me to worry less. Trondheim, November 2018 Anette Østbø Sørensen Summary Technological developments increase the amount of available data sources in all industries. This thesis focuses on how we can exploit these data sources in ex-post project evaluation to support evaluators with new insight. The focus has been on railways and railway projects. A project should be evaluated to establish to what degree the intended effects on the users and the society are achieved. The ex-post evaluation should be carried out a few years after major infrastructure projects are completed. One matter that should be evaluated is tactical success, which is measured in terms of effectiveness. Effectiveness measures if the project achieved its goals and is typically assessed based on the change of state before and after. However, evaluators experience problems in getting hold of essential data on the pre- and post-situation. For the evaluators, data collected from the railway operations are mainly available as aggregated numbers from performance and evaluation reports. Typical goals of railway projects are to reduce travel time and increase capacity and demand. Two measures of particular interest are therefore punctuality and traffic volume. Punctuality is whether the traffic runs according to the timetable, and statistical numbers on punctuality are well-developed measures. However, a deeper evaluation of punctuality can be provided by analysing delay propagation, and that has been more difficult to obtain a good measure on. The number of travellers is an important performance measure that has been difficult for evaluators to obtain good data on, often because train operators consider such data confidential business information. In addition, there is a challenge regarding varying quality and coverage of the available data on the number of travellers. Data of relevance to ex-post project evaluation are generated and collected from both the construction phase and the operation phase. The data collected from railway operations are useful when evaluating the tactical and strategic success of a project. The conclusions drawn from this evaluation can provide useful insight and learning to the strategic planning and concept development of future projects. The author has provided two practical examples of how data generated and collected during railway operations can be exploited to obtain relevant information for the evaluation. This was done through thorough investigation into two measures of particular interest for ex-post evaluation of railway projects, i.e. delay propagation and number of travellers. Delay propagation was analysed based on traffic data, and a method was devel- oped to find cases of knock-on delay on single tracks. The tool allows an attempt at indicating the direction of knock-on effects. In addition, the method traces the vii viii SUMMARY propagation of delay from one train to the next to find the networks of dynamic delay propagations. Mobile phone data were investigated as an alternative source of the number of travellers. The possibility of using mobile phone data is interesting because it is independent of the railway operators. The study showed that it is possible to combine mobile phone data with railway infrastructure and train traffic data. The findings show a potential for utilising mobile phone data to collect the number of travellers on the railway. The data collected from technologies used during the construction project are useful when evaluating the project execution. This includes the experience report, which is the internal form of knowledge sharing, and the evaluation of efficiency, which examines time, cost and quality. These evaluations are important for learning from similar projects during the construction phase and can contribute to faster decisions. Ex-post project evaluation has traditionally been qualitative, but because of technological developments, more data from the railway operations are available. It is still a problem to get hold of data, especially when it is about private information, while other data sources have become more available. The two empirical studies are good examples of how such data can provide useful insight into the ex-post evaluation of railway projects. List of Publications This dissertation consists of the following publications, which are referred to in the text by their corresponding Roman numerals. I. Sørensen, A. Ø., Landmark, A. D., Olsson, N. O. E. & Seim, A. A. (2017). Method of analysis for delay propagation in a single-track network.