Modelling the Demand Evolution of New Shared Mobility Title Services( Dissertation_全文 ) Author(s) Zhang, Cen Citation 京都大学 Issue Date 2019-03-25 URL https://doi.org/10.14989/doctor.k21747 Right Type Thesis or Dissertation Textversion ETD Kyoto University Modelling the Demand Evolution of New Shared Mobility Services ZHANG CEN i ii Acknowledgments The writing of this dissertation has been one of the most significant academic challenges I have ever had to face. A great many people have contributed to its production. Without their supports and guidance, this study would not have been completed. First of all, I would like to thank my sincere gratitude and deep regards to my supervisor, Associate Prof. Jan-Dirk Schmöcker, in Kyoto University for his patience, motivation, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. Besides my advisor, I would like to thank the rest of my thesis committee and co- supervisors: Prof. Yamata and Associate Prof. Uno in the Kyoto University, for their insightful comments and encouragements, but also for the questions which inspired me to widen my research from various perspectives. Also my sincere thanks also goes to Prof Fuji in the Kyoto University, Asscoiate Prof. Nakamura in Nagoya University who advised me at several stages. Without their precious support it would not be possible to conduct this research. Regards the assistance of administration process, I hope to give this opportunity to express a deep sense of gratitude to thanks to the secretaries, Mrs. Nagata, Mrs. Nishimura and Mrs. Ichihashi, their various helps have made me concentrate on my research. Above all, I would like to thank members of ITS laboratory and all friends who have a great time and willing to share their thoughts and ideas with me in Japan. Last but not the least, I would like to thank my family for supporting me spiritually throughout writing this thesis and my life in general. iii iv Abstract Recently, shared mobility, including car-sharing, bicycle-sharing, microtransit and on demand ride service, has emerged as popular forms of alternative transport. This form of collaborative consumption, which promotes the sharing of access to transport service, rather than having individual ownership, will largely save the cost as well as space and of course, reduce carbon emissions. With the emergence of these and other new transport modes, our transport systems are undergoing major changes and behavioural responses are expected (and desired). Understanding long term demand dynamics remains an important challenge for new transport systems. To achieve this, this dissertation contains two parts: One is the adoption process of potential users, that leads a person from initial recognising the scheme and then starting to use it. The other part is the change in behaviour over usage life-time, that is variation of usage frequency from initial usage to finally dropping out/quitting the system. In the first part of this dissertation, our objective is to be able to forecast the number of new adopters and the potential market with facility extension, including when new stations/service areas are added to the system. In order to do so, potential users are divided into two types: Fast and Hesitant adopters. Stations are classified into four groups according to their location: Residential area, Business area, Public service area and Transit Hub. We formulate the adoption model of fast-adopters integrated with an information diffusion model to capture the initial changes. The adoption model for the Hesitant-adopters considers both a follower effect and a hesitant effect. Further, to estimate spatial differences in adoption rates and interconnectedness of stations, location specific parameters and a redistribution model are established. The purpose of the latter is to recognize that despite initial usage being carried v out at a specific station, the new user might be attracted to join the system due to the existence of several stations. The least square method and gradient descent optimization algorithms are used to estimate the parameters. The methodology is applied to data from the Ha:mo RIDE car share system in Toyota city. We observe two peaks in the new user curve which can be explained by our model, the initial peak may be caused by information diffusion whereas the later peak can be explained by market saturation. A comparison between a range of models for estimation and forecasting are made. In the second part of this dissertation, the focus is on describing the individual behaviour changes over time and then forecasting the demand dynamics of system. At first, to describe the stochastic behaviour changes over time by using panel data, an approach based on stochastic state equations (Markov model) is proposed. Transition functions determine the likely change in behaviour from one time period to another. To overcome the problem of a dynamic population and explain seasonal irregularities, we introduce “life-cycle”, “potential demand” and “willingness to use” into our models. With this we discuss time-homogeneity issues, possibilities to identify usage states and calibrate the transition function. The life-cycle model is applied to panel data from Kyoto University’s bicycle share system. The errors between actual and estimated values are analysed to evaluate two model specifications. The findings help us understanding adaptation, “recovery” and drop-out behaviour. Later, by introducing some latent “Life Stages” into the lifecycle models, the model can deal better with time-heterogeneity in transition, leading to an improved description on user behaviour changes. Therefore, a total of five individual life-cycle stages are define; Birth, Growing, Maturity, Recession and Death. As the current stage of users can change over time, transition functions in different stages will further decide the state transitions within each stage. The model can be considered as a special case of Hierarchical Hidden Markov Model (HHMM). To estimate the latent variables and the transition functions, the Expectation- vi maximization (EM) algorithm is used. The extended lifecycle model is applied to panel data from Montreal`s Free Floating Car Sharing Service (FFcs). The results show that the model can fit the observed demand distribution curve in lifetime and real-time well. At the same time, an impact of facility extension (new service areas) on user behaviour is noticed, in that a higher drop out ratio but increased usage frequency at all stages is observed. It is also found that differences in behaviour exist between users with experience of using the prior existing station-based car sharing service (SBcs) and users without such experience. Experienced users have longer lifetime but also slower adaptation and lower usage frequency at the beginning. These findings help understanding impacts of other factors, such as previous experience and facility extension on initial trials, adaptation, maintaining, decline and drop-out behaviour. Finally, to further explore the usefulness and limitations of our methodologies, a combination of the adoption model and the user life-cycle model is conducted to forecast the demand dynamics of new transport system with the facility extension in case study Ha:mo. Keywords: Shared Mobility; Demand Evolution; Adoption and Diffusion; Stochastic Process; Lifecycle Model; vii viii Preface Papers published or submitted related to this thesis are listed in the following: I. Zhang, C.,Schmöcker, J.-D (2017). Modelling User Adaptation to a Campus Bicycle Share System. 96th TRB Annual Meeting, Washington DC, USA. (Chapter 5) II. Zhang, C.,Schmöcker, J. D. (2017). A Markovian model of user adaptation with case study of a shared bicycle scheme. Transportmetrica B: Transport Dynamics, 1-14. (Chapter 5) III. Zhang, C., Schmöcker, J.-D (2019). Stochastic process based life-cycle modelling for user behavior changes. 98th TRB Annual Meeting, Washington DC, USA. (Chapter 6) IV. Zhang, C., Schmöcker, J.-D, Toshiyuki Nakamura, Masahiro Kuwahara. (2017), Modeling adoption and diffusion of new transportation system in station level, Autumn Conference of Committee of Infrastructure Planning and Management, Morioka, Japan. (Chapter 4) V. Zhang, C., Schmöcker, J.-D, Toshiyuki Nakamura, Nobuhiro Uno, Masahiro Kuwahara (2018). Modeling temporal and spatial differences in the adoption of new transport systems, Transportation Research Part C: Emerging Technologies (under review) (Chapter 4) ix x Table of Contents _Toc534923292 Acknowledgments ......................................................................................... iii Abstract ........................................................................................................... v Preface ........................................................................................................... ix Table of Contents ........................................................................................... xi List of Tables................................................................................................. xv List of Figures ............................................................................................. xvii CHAPTER 1. INTRODUCTION .............................................................. 1 1.1. Background ............................................................................... 1 1.2. Research Objective .................................................................... 2 1.3. Outline and