Influence of Pedestrian Density on the Use of the Danger Zone at Platforms of Train Stations

Thurau, Jasmin; Heuvel, Jeroen van den; Keusen, Nicolas; Ofwegen, Marcel van; Hoogendoorn, Serge P.

doi:10.1007/978-3-030-11440-4_32

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…The reason might be the lower frequency of use of this side of the ramp. This shows that additional factors, such as person density, influence pedestrians use of the danger zone (see also [1]).…”

Section: Results For H3: Duration Of Use Of the Danger Zonementioning

confidence: 81%

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Influence of Obstacles on the Use of the Danger Zone on Railway Platforms

Thurau¹,

Keusen²

2020

Coll Dyn

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Growing passenger numbers and the lack of space available led to research on pedestrians’ behaviour on railway platforms in Switzerland. By using stereo sensors, pedestrians’ tracks were collected on a platform in the train station of Bern. The analysis of pedestrians stepping into the danger zone showed clearly that obstacles have a large influence on the frequencies of pedestrians using the danger zone. By presenting four hypotheses the effect of obstacles on pedestrians’ use of the danger zone on train station platforms is investigated.

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Section: Results For H3: Duration Of Use Of the Danger Zonementioning

confidence: 81%

“…Latest research analysed the relation between pedestrian density on platforms and the number of pedestrians entering the danger zone [1]. show that there is still further research needed when defining the maximum number of passengers allowed on platforms.…”

Section: Introductionmentioning

confidence: 99%

Influence of Obstacles on the Use of the Danger Zone on Railway Platforms

Thurau¹,

Keusen²

2020

Coll Dyn

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“…The system consists of 3 commercial pedestrian tracking sensors (Xovis TM ), used in e.g. train stations [14,[22][23][24], to monitor complex crowd flows. Every sensor records images at f = 10 frames per second and processes the stereo images in real-time only storing pedestrian locations as x, y coordinate pairs.…”

Section: Tracking Technologies and Experimental Setupsmentioning

confidence: 99%

“…Over the last few years, for instance, managers of public transport facilities have adopted pedestrian tracking technologies to gain valuable insights in the flow dynamics through their facilities (see e.g. [14,[22][23][24]).…”

Section: Introductionmentioning

confidence: 99%

Benchmarking High-Fidelity Pedestrian Tracking Systems for Research, Real-Time Monitoring and Crowd Control

et al. 2022

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High-fidelity pedestrian tracking in real-life conditions has been an important tool in fundamental crowd dynamics research allowing to quantify statistics of relevant observables including walking velocities, mutual distances and body orientations. As this technology advances, it is becoming increasingly useful also in society. In fact, continued urbanization is overwhelming existing pedestrian infrastructures such as transportation hubs and stations, generating an urgent need for real-time highly-accurate usage data, aiming both at flow monitoring and dynamics understanding. To successfully employ pedestrian tracking techniques in research and technology, it is crucial to validate and benchmark them for accuracy. This is not only necessary to guarantee data quality, but also to identify systematic errors. Currently, there is no established policy in this context. In this contribution, we present and discuss a benchmark suite, towards an open standard in the community, for privacy-respectful pedestrian tracking techniques. The suite is technology-independent and it is applicable to academic and commercial pedestrian tracking systems, operating both in lab environments and real-life conditions. The benchmark suite consists of 5 tests addressing specific aspects of pedestrian tracking quality, including accurate line-based crowd flux estimation, local density estimation, individual position detection and trajectory accuracy. The output of the tests are quality factors expressed as single numbers. We provide the benchmark results for two tracking systems, both operating in real-life, one commercial, and the other based on overhead depth-maps developed at TU Eindhoven, within the Crowdflow topical group. We discuss the results on the basis of the quality factors and report on the typical sensor and algorithmic performance. This enables us to highlight the current state-of-the-art, its limitations and provide installation recommendations, with specific attention to multi-sensor setups and data stitching.

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