In 2019 a virus surfaced in China that would later cause a pandemic: the coronavirus. Across the world measures were taken to reduce the spread of the virus. Public Transport (PT) had a unique position, being both an essential service and a place where the virus can easily be spread. This research investigates the measures taken in PT in the Netherlands and internationally in an objective manner. No assessment is made of any kind of any measure. In the Netherlands, the Outbreak Management Team collaborated with the public transport association OV-NL to ensure every companies response was similar. Through an interview with the chairman of OV-NL, Pedro Peters, more information was gathered on this particular aspect of the research. Besides taking measures to reduce the virus, mainly by reducing or avoiding contact (both between people and with surfaces), companies reduced their service levels. This reduction was a response to the large decrease in passenger demand and partly to more of the personnel reporting sick. This research investigated this reduction of service throughout the first period of the virus outbreak in the Netherlands (27th of February – 1st of June). Besides the Netherlands, measures taken in other countries were also investigated, showing many similarities. However, other measures were taken as well, like checking the temperature of passengers in for example Thailand, China or Iran or using a proximity app which happened in Singapore. Not every country experienced the virus outbreak at the same time. In the research, data by the Oxford University and Blavatnik School of government was used to create world maps that visualize the closing of public transport for all days of the outbreak up to June 1st. The same was done to create a map for lockdowns and using data found through an online search a world map visualisation was made about the mandatory use of face masks in public transport. The maps show a general trend: following the virus, but it is clear that some countries responded early or late and that the correct response to the virus was unclear. Especially the face mask visualisation shows a more random pattern. So while within the Netherlands the response to the virus was uniform, internationally the response was quite random. This research can be used to further research the measures taken and assess the measures with regards to for example effectiveness, safety or comfort.

Predicting crowd movements in real-time during mass events has been shown to be a complex yet valuable task in order to reduce the risk of overcrowding. The aim of this research is to propose and validate a crowd movement forecasting method for which simulation is performed offline (i.e. prior to the event) but the forecast is done online, in real-time. A number of scenarios is formulated and simulated creating what is called a database of scenarios. In real-time, based on information from the event's crowd monitoring systems, a scenario from this database is then selected which corresponds to the prediction. The research is focused on addressing the concepts related to the two pillars of the method: the formulation of the scenarios to be included in the database, and the operationalization of the system to select a scenario in real-time.

At mass events, pedestrians can experience the level of crowdedness as unsafe, unpleasant and stressful. To gain a better understanding of perceived crowdedness, the effects of personal, trip and event characteristics at an event are researched. Data collection was performed by a simultaneous survey and monitoring study at the TT Festival in Assen and at the Red light district in Amsterdam. A SEM model shows that perceived crowdedness is influenced by mainly by the density, quantified using Wi-Fi sensor data. Besides that, trip purpose and familiarity with the event influence perceived crowdedness as well. Furthermore, this research shows that perception of safety, comfort, atmosphere and attractiveness of the environment are also related to the perception of crowdedness.