Identifying the Transit Needs of Socioeconomic Groups by Evaluating the Relation- ship between a Network’s Supply and Demand A Case Study of the City of Amsterdam Benjamin Drybrough Technische Universiteit Delft Cover photo: https://unsplash.com/photos/XrQD9OqkjYQ Identifying the Transit Needs of Socioeconomic Groups by Evaluating the Relationship between a Network’s Supply and Demand A Case Study of the City of Amsterdam by Benjamin Drybrough This research is done for the partial fulfilment of requirements for the Master of Science degree at the TU Delft, the Netherlands To be defended publicly on November 25th, 2020. Department of Transport and Planning, Delft University of Technology Student number: 4775090 Project duration: February 10, 2020 – November 25, 2020 Thesis committee: Dr. Oded Cats, TU Delft, Chair Dr. ir. Niels van Oort, TU Delft, Daily Supervisor Dr. ir. Maarten Kroesen, TU Delft Machiel Kouwenberg, Vervoerregio Amsterdam An electronic version of this thesis is available at http://repository.tudelft.nl/. Acknowledgements I would like to take this opportunity to thank everyone who helped me over the entire thesis period. For myself, it was a thrilling opportunity to work on a project in the city that peaked my interest of transport and motivated me to complete my studies at the TU Delft. However, without the help of many people the completion of the research would not have been possible. I would like to first say thank you to my committee at the TU Delft consisting of Oded, Niels, Maarten, and Malvika. To Oded, thank you for introducing myself to the topic at the Vervoerregio, providing guidance throughout each stage of the process, and recently starting the refreshing Monday morning masters kick­offs. To Niels, thank you for your supervision and support during the tough Covid­19 months, providing opportunities to share my thoughts on my research, and suggesting almost a year and a half ago how I should go upon searching for practical experience within the Dutch public transport industry. To Maarten, thank you for challenging me in the later phases of my research and pushing me to a point where I am proud of the finished product. And to Malvika, thank you for your thoughtful answers surrounding transport accessibility research. You all played an important role in my thesis, so thank you very much. I would also like to extend a thank you to all the colleagues I met at the Vervoerregio Amsterdam but specifically to Machiel and Mark of team Kennis & Onderzoek. Machiel, thank you for your calming guidance throughout the entire process and connecting me with the people and information required to complete my project. Mark, thank you for having utmost enthusiasm for my project and pushing me to get over the finish line. While I would have wished to spend more time at the Vervoerregio office I will remember my time at the Vervoerregio fondly. Special thanks goes out to all my family and friends who I hold very close to my heart. Specifically to my parents, Dan and Marjolein, for their unconditional love and steadfast support that allowed me to fulfill this idea of completing my masters in the Netherlands. Lastly, I would like to thank my girlfriend Marieke, who has shown more love, support, and patience during this thesis period than I could have ever asked for. Thanks to everyone for making this thesis possible. Ben Drybrough. November 2020. Amsterdam iii Executive Summary An inclusive and sustainable transport network allows people the opportunity to access their daily needs. A transportation planner attempts to facilitate and support these needs by providing a trans­ port network that reaches into neighbourhoods of a transport region. When this is not completed in an effective manner a discrepancy between someones transport need and their transport network arises. This is termed as transport poverty which has negative consequences such as making employment or their social circles inaccessible. It is argued that people of lower socioeconomic and demographic status are most at risk of experienc­ ing transport poverty. This occurs as their transport needs are higher relative to other groups of people with more transport options. This is exacerbated by the fact that current transport networks are often designed for an idealised group of citizens and fails to cater to the specific needs of all individuals. A recent study commissioned by the European Commission explained that transport needs are not constant within a city and outlined eight guiding principles that should be considered when building a transport network for all individuals (Tovaas, 2020). This is an important step in defining specific trans­ port needs for individuals rather than assuming levels of transport need based on their socioeconomic and demographic data. This study attempts to move further away from these assumptions by building on a framework of latent demand proposed by Clifton and Moura (2017). In this framework, transport needs can be identified by observing the transition between latent demand into effective demand when presented with an improved transport network. By observing the transition for different socioeconomic and demographic groups, further insights into people’s transport needs are identified. This study will aim to build on this framework and for which the following research question is developed: How does observing the relationship between the transport network supply and demand provide in­ sights into the transport needs of neighbourhoods defined by their socioeconomic and demographic characteristics in the city of Amsterdam, The Netherlands? When answering this question, it is expected that the transition is quicker for groups of lower socioe­ conomic and demographic characteristics as they are historically assumed to have the most transport need. This is because these groups do not have the same options available in comparison with groups of higher socioeconomic groups. The methodology of this research allows for policy makers to identify needs in a city and make network improvements which attempt to solve these problems. Research Methodologies and Case Study The methodology for this research is developed and implemented in the form of a case study for the transit network of city of Amsterdam. This is completed as the transport authority of the Amsterdam region (Vervoerregio Amsterdam) identifies the necessity of supporting individuals transport needs in the region. There are four main components of the methodology developed in order to answer the research question. First, is the clustering of Amsterdam neighbourhoods using income, car ownership, and family com­ position data. This step is completed using a Latent Class Clustering Analysis (LCCA) which defines clusters of neighbourhoods that maximizes the similarities of characteristics within and the differences between clusters. The distance to the centre as well as urbanity of the neighbourhood are added as covariates to the model in order to refine the allocation of neighbourhoods to a certain cluster. The result is a characteristic profile that defines each cluster based on socioeconomic and demographic v vi 0. Executive Summary characteristics which are relevant for the identification of variable transport needs. The clustering sets the basis on which the relationship between supply and demand for differing socioeconomic and de­ mographic groups is analyzed. The second step of the research defines three supply indicators ­ walking coverage, supply frequency and cumulative opportunity accessibility (termed accessibility in this thesis) ­ for the GVB public trans­ port network in Amsterdam (Carleton and Porter, 2018; Deboosere and El­Geneidy, 2018; Wang et al., 2017). General Transit Feed Specification (GTFS) data is used in a GIS network analysis environment in order to calculate the indicators. These indicators are built to reflect the tangible needs within a public transport network as defined by the Inclusion Project (Tovaas, 2020). The walking coverage is calculated for neighbourhoods to represent the convenience of a potential traveller reaching a transit stop. The supply frequency is calculated to describe the efficiency of the network a potential traveller experiences in their neighbourhood. The last indicator, accessibility, reflects the number of people that a traveller may access within 30 minutes by a public transport journey. This last metric is a common metric in accessibility research which incorporates the convenience of moving around the city, the abil­ ity for multiple routes and vehicles to complete a trip, and providing ample number of locations and destinations that a person can access. Each of these indicators represent a potential transit need for an individual and could be of conscious or unconscious consideration before a trip is completed. Figure 1: Representation of the walking buffer surrounding transit stop in the city of Amsterdam used within each of the supply and demand indicators. The third component of the methodology looks at the number of public transport trips made from neigh­ bourhoods in Amsterdam during morning rush hour of June 2019. The demand indicator is a proxy for the use of the transit network and reflects the trip generation of individuals under different quantity of supply indicators of the transit network. This study utilizes public transport smart card data (OV­ Chipkaart in the Netherlands) to record the number of trips started at each transit stop across the GVB network. Transit trips are allocated and attributed to the surrounding neighbourhoods using a percent­ age of the walking buffer from a transit stop that is present within that neighbourhood. The walking buffer represents the area a person can reach after walking 400m. The pedestrian network of Amster­ dam is gathered from OpenStreetMaps and the walking buffers are calculated using network analysis in the GIS environment (Figure 1). These first three steps of the methodology allow for the socioeconomic clusters to be defined, the constraints/opportunities of the transit network to be calculated, and the use of the network to be found. The last part of the methodology finds the relationship between each of these three components.