Experimental Method to Estimate the Density of Passengers on Urban Railway Platforms

Paulo Aguayo, Sebastian Seriani (), Jose Delpiano, Gonzalo Farias, Taku Fujiyama and Sergio A. Velastin
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Paulo Aguayo: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile
Sebastian Seriani: Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile
Jose Delpiano: Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago 7620001, Chile
Gonzalo Farias: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile
Taku Fujiyama: Faculty of Civil, Environmental and Geomatic Engineering, University College London, London WC1E 6BT, UK
Sergio A. Velastin: Department of Computer Science and Engineering, Universidad Carlos III de Madrid, 28903 Madrid, Spain

Sustainability, 2023, vol. 15, issue 2, 1-19

Abstract: The platform–train interface (PTI) is considered a complex space where most interactions occur between passengers boarding and alighting. These interactions are critical under crowded conditions, affecting the experience of traveling and therefore the quality of life. The problem is that urban railway operators do not know what the density at the PTI is in real time, and therefore it is not possible to obtain a measure of the personal space of passengers boarding and alighting the train. To address this problem, a new method is developed to estimate the density of passengers on urban railway platforms using laboratory experiments. In those experiments, the use of computer vision is attractive, through the training of neural networks and image processing. The experiments considered a mock-up of a train carriage and its adjacent platform. In the boarding process, the results showed that the density using Voronoi polygons reached up to a 300% difference compared to the average values of density using Fruin’s Level of Service. However, in the case of alighting, that difference reached about 142% due to the space available for wheelchair users who needed assistance. These results would help practitioners to know where passengers are located at the PTI and, therefore, which part of the platform is more congested, requiring the implementation of crowd management measures in real time. Further studies need to include other types of passengers and different situations in existing stations.

Keywords: passenger; platform–train interface; density; boarding and alighting; Voronoi polygons; computer vision (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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