Using Dutch Land and Property Data to Improve Trip Generation Based on Open Data
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MASTER THESIS Using Dutch Land and Property Data to Improve Trip Generation based on Open Data Master: Civil Engineering and Management Master track: Transport Planning and Modelling Author: J. M. Kuiper Student ID: s1594931 Date: 11/08/2021 Contact information Author: Martijn Kuiper Student number: s1594931 Email: [email protected] Location thesis project: Intern UT supervisor: Prof. Dr. Ir. E.C. van Berkum Daily supervisor: Dr. T. Thomas University: University of Twente Address: Drienerlolaan 5, 7522 NB, Enschede Master: Civil Engineering and Management Master track: Transport Planning and Modelling Version: Final version II Preface Before you lies the thesis “Using Dutch land and property data to improve trip generation based on open data”, which I wrote as the last step in finishing my Masters in Civil Engineering at the University of Twente. From December 2020 till the beginning of August 2021, I have been engaged in researching and writing this thesis. Due to the COVID pandemic, all research was conducted at home. The absence of fellow students and the challenges that rose during the conduct of this thesis project lead to hurdles I had not faced before during my study. I have learned a lot in the last few months, both professionally and personally. A special thanks to my daily supervisor Tom Thomas who was always available, always responding and who put a lot of time and effort into assisting me during the project. The regular meetings we had helped structuring my project, and the feedback always proved more than useful. Furthermore, I would like to thank Eric van Berkum for his time and effort put into my research, as UT supervisor. And finally I would like to thank my wife who made sure I did not lose my focus, and who provided helpful feedback. After this graduation project, I will start my professional career as a BIM engineer, which I am looking forward to. I am excited to start this new learning phase, and I will look back at many joyful study years. I hope you enjoy reading my thesis as much as I enjoyed writing it. Martijn Kuiper Deventer, August 2021 III Summary This thesis investigates the potential of open Dutch land and property data, so-called ‘Basisregistratie Adressen en Gebouwen’ (BAG) data, to be used for trip generation modelling at the urban level. In a trip generation model (the first step of a transportation model) it is determined how many trips are produced by a zone, and how many trips are attracted by a zone. In the literature, a lot of research has been put in developing a trip generation model itself, in which through advanced analysis it is investigated how certain personal or zonal characteristics affect the travel behavior of individuals. However, the more advanced the models, the more specific the data that is required. The availability of data is a bottleneck for the application of trip generation models. In literature, there is little focus on this aspect of trip generation modelling, while in practice modelers encounter major challenges when developing a database for a trip generation model. And furthermore, developing a database quickly becomes expensive and time consuming. Not every institution or organization has the means to purchase such required data. In the Netherlands, the Central Bureau of Statistics (CBS) is the main provider of open census data. Information on the number of residents, income, car ownership and other factors often used in trip generation studies are supplied at different administrative levels. But, in terms of aggregation level, completeness and novelty, the CBS data is lacking. For estimating work trips at the urban level, no open job data or activity data is available. Finally, for activities as shopping, sporting and other leisure, conventional open data sources are also lacking. In Dutch municipal transportation studies, sporting and other leisure activities are not even considered. To improve the possibilities of estimating trip generation based on open data at the urban level, the use of BAG data as a source for trip generation factors and activities is researched in this master thesis. BAG is disaggregated, open data containing every building in the Netherlands including the surface area of all of its spaces and the function for which the building is constructed, such as living, industry, office, shop and sport. And for residences, different residence types are included, such as detached, multi- family and terraced. And furthermore, buildings are included for which a construction permit has been granted, which means that residences and other buildings can be included in a trip generation model that is constructed in the next few years. To research the potential of BAG at the activity side, it has been researched what open data already is available to supply for activities and factors. Mainly for work and shopping trips, BAG data can be of added value. For shopping activities, operations have been developed that successfully identify shopping activities in BAG. Furthermore, the ability of BAG to predict trip generation factors at the household side has been evaluated. Based on BAG attributes it is possible to predict car ownership levels and the number of residents in a zone. This predictive capacity of BAG can be used in two ways; subdividing CBS District data into lower aggregated zones, suitable for estimating trip generation at the urban level, and predicting the number of residents and car ownership levels for new housing developments. Finally, in a case study in the municipality of Ede, it is showcased how BAG enables identifying bottlenecks caused by future travel demand, based on BAG predictions. In this research, it has been found that BAG increases the possibilities of estimating trip generation based on open data, by complementing shortcomings of CBS open data at the household side, by enabling precise trip generation estimation at the activity side and by providing future land and property data that could be used to predict travel demand. IV Nomenclature Term Meaning A Surface area AFC Automatic Fare Collection BAG Basisregistratie Adressen en Gebouwen [Key Register Adresses and Buildings] BRT Basisregistratie Topografie [Base Registration Topography] CBS Centraal Bureau Statistiek [Central Bureau of Statistics] DBSCAN Density-Based Spatial Clustering of Applications with Noise) dens_C Indicator for the density of the cluster (explained later) DSA Databestand SportAanbod [Database Sport Accommodations] edf Estimated degrees of freedom eps Maximum distance allowed between two cluster points. GAM Generalized Additive Models GCV Generalized Cross-Validation GFA Gross Floor Area GP General Practitioner HB Home-Based HH Household IBIS Integral Business Area Information System ITE Institute of Transportation Engineers K+R Kiss and Ride KW Klinkenbergerweg max_A Surface area of the largest VBO present in the cluster mean_A Average surface area of the cluster minPoints Minimum cluster size for cluster to be considered a cluster N Cluster size NHB Non-Home-Based NRM Nederlands Regionaal Model [Dutch Regional Model] NZ New Zealand OD Origin Destination OSM OpenStreetMap PC4/6 Dutch postal code with 4 or 6 digits (e.g. 1234AB) PT Public Transport RMSE Root-Mean-Square Error RQ Research Question SD Standard Deviation TAZ Traffic Analysis Zone UK United Kingdom VBO Verblijfsobject [Accommodation object] VGI Volunteered Geographic Information V Table of contents Preface ................................................................................................................................................... III Summary ............................................................................................................................................... IV Nomenclature ......................................................................................................................................... V Table of contents ................................................................................................................................... VI Chapter 1. Introduction ..................................................................................................................... 1 Chapter 2. Literature study ................................................................................................................ 3 2.1 Theoretical cadre: Trip generation modelling practices ................................................... 4 2.2 Trip generation data gathering ......................................................................................... 7 2.3 Research gap .................................................................................................................. 12 Chapter 3. Problem definition ......................................................................................................... 13 3.1 Problem description........................................................................................................ 14 3.2 Research goal and questions .......................................................................................... 14 3.3 Scope .............................................................................................................................. 16 Chapter 4. Data ............................................................................................................................... 17 4.1 BAG data ........................................................................................................................ 18 4.2 Motives ..........................................................................................................................