Using Traffic Sensors for Mapping Event Effects on Pedestrian Flow

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Today, the amount of traffic on the streets is more important than ever before. To combat the number of new infections of COVID-19, data must be gathered to make informed decisions. A good place to start is the city centre of our own city, Nijmegen. Because of the narrow streets and the high number of people in those streets, it is sometimes impossible to keep the desired distance from others. To help cities manage crowd control on a well-informed basis, it is useful to use sensor readings about the number of traffic participants. This can be used further to map how events affect the amount of people that are in the city centre. This is possible by comparing the number of people in an area in two periods are compared, that differ in respect to a selected criterion, such as an event (e.g. shopping Sundays, or the four days marches event). Comparing such periods gives insight into the effect of the event on the amount of people in the city. The goal of this thesis is to develop and evaluate a visualization tool that allows for such comparisons and to indicate ways to improve this tool in future research. The tool uses data which is generated by smart city sensors that have been placed in and around the city centre of Nijmegen. The visualisation is created by interpolating the data with the use of radial basis function interpolation. The tool enables the user to visualize the effect of a big variety of events on the amount of people in the city.
Faculteit der Sociale Wetenschappen