title: Spatially Explicit Modelling of the Belgian Major Endurance Event 'The 100 km Dodentocht'.
abstract: 'The 100 km Dodentocht', which takes place annually and has its start in Bornem, Belgium, is a long distance march where participants have to cover a 100 km trail in at most 24 hours. The approximately 11 000 marchers per edition are tracked by making use of passive radio-frequency-identification (RFID). These tracking data were analyzed to build a spatially explicit marching model that gives insights into the dynamics of the event and allows to evaluate the effect of changes in the starting procedure of the event. For building the model, the empirical distribution functions (edf) of the marching speeds at every section of the trail in between two consecutive checkpoints and of the checkpoints where marchers retire, are determined, taking into account age, gender, and marching speeds at previous sections. These distribution functions are then used to sample the consecutive speeds and retirement, and as such to simulate the times when individual marchers pass by the consecutive checkpoints. We concluded that the data-driven model simulates the event reliably. Furthermore, we tested three scenarios to reduce the crowdiness along the first part of the trail and in this way were able to conclude that either the start should be moved to a location outside the town center where the streets are at least 25% wider, or that the marchers should start in two groups at two different locations, and that these groups should ideally merge at about 20 km after the start. The crowdiness at the start might also be reduced by installing a bottleneck at the start in order to limit the number of marchers that can pass per unit of time. Consequently, the operating hours of the consecutive checkpoints would be longer. The developed framework can likewise be used to analyze and improve the operation of other endurance events if sufficient tracking data are available.
authors.affiliation: KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, B-9000 Ghent, Belgium.
authors.forename: Steffie
authors.initials: S
authors.lastname: Van Nieuland
authors.affiliation: KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, B-9000 Ghent, Belgium.
authors.forename: Jan M
authors.initials: JM
authors.lastname: Baetens
authors.affiliation: KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, B-9000 Ghent, Belgium.
authors.forename: Bernard
authors.initials: B
authors.lastname: De Baets
mesh_terms: D000328:Adult; D000367:Age Factors; D001530:Belgium; D005260:Female; D006801:Humans; D008297:Male; D057233:Radio Frequency Identification Device; D012737:Sex Factors; D013177:Sports; D013997:Time Factors