Oppilatio: The Forecast of Crowd Congestions on Street Networks During Public Events

Biedermann, Daniel H.; Kielar, Peter M.; Borrmann, André

doi:10.1007/978-3-319-33482-0_41

Cited by 2 publications

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“…It extends and improves our previous work in this research field [4]. In contrast to crowd simulations, no background knowledge about pedestrian dynamics is required, and the input data for our method can be easily collected.…”

Section: Introductionsupporting

confidence: 56%

Oppilatio+ - A data and cognitive science based approach to analyze pedestrian flows in networks

et al. 2017

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Public transport services are a widespread and environmentally friendly option for mobility. In the majority of cases, passengers of public transport services will have to walk from a subway, train, or bus station to their desired travel destination. In an urban environment with a network of narrow streets, this can lead to crowd congestions during rush hour, due to the fact that passengers tend to arrive in waves. In order to monitor and analyze such crowding behavior, city planners, crowd managers, and organizers of public events must ascertain which routes these pedestrians will take from the respective station to their destination. The Oppilatio + approach is suitable for solving this problem. It is an easy-to-apply approach to predict way-finding behavior with a minimal set of information. The necessary data includes the schedule of incoming transport vehicles at the stations and the time-stamped count of pedestrians at the respective destinations. Under these conditions, the Oppilatio + approach is suitable for estimating the distribution of pedestrians on all possible walkways between stations and destinations. This information helps crowd control experts to recognize weak spots in the infrastructure and help event organizers to ensure an undisturbed arrival at their event. We validated our approach using two field experiments. The first one was a field study on a public event, and the second one was a case study for a large Swiss train station.

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Section: Introductionsupporting

confidence: 56%