University of Twente Student Theses


Using Dutch land and property data to improve trip generation based on open data

Kuiper, J.M. (2021) Using Dutch land and property data to improve trip generation based on open data.

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Abstract: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, multifamily 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.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Programme:Civil Engineering and Management MSc (60026)
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