Single-file pedestrian flow experiments under high-density conditions

Jin, Cheng-Jie; Jiang, Rui; Li, Ruiwen; Li, Dawei

doi:10.1016/j.physa.2019.121718

Cited by 27 publications

(20 citation statements)

References 50 publications

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“…In this context, experiments have been performed to study the microscopic and macroscopic movement characteristics of crowds, such as the relation between velocity and density, jam and stop-andgo-waves, lateral oscillations, etc. [8]- [10], [18], [32]- [37]. For example, Jin et al [18] performed single-file movement experiments to analyze unidirectional pedestrian flow under high-density situations in China.…”

Section: Related Workmentioning

confidence: 99%

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Experimental Investigation on the Alleged Gender-Differences in Pedestrian Dynamics: A Study Reveals No Gender Differences in Pedestrian Movement Behavior

et al. 2020

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Pedestrian dynamics are affected by several factors including pedestrian compositions. In this article, we examine the movement characteristics of Palestinian pedestrians using the fundamental diagram for single-file movement experiments conducted with an emphasis on gender compositions. Our findings show that the mean velocity of exclusively male pedestrians is approximately the same as exclusively female pedestrians. For instance, when the number of pedestrians is 20, the velocities for male and female are 0.72 ± 0.10 ms −1 and 0.71 ± 0.11 ms −1 , respectively, whereas their velocity decreases gradually if they walk in mixed groups with an average velocity of 0.61 ± 0.11 ms −1. We also compare our findings with other culture-based experiments to demonstrate that pedestrian cultures have an effect on their movement characteristics. Moreover, we demonstrate that age is another factor that affects pedestrians' movement. A comparative analysis is performed between Palestinian and Chinese experiments for this purpose. Our results confirm that for relatively high densities older Chinese pedestrians walk faster than young Palestinians in groups of mixed gender. INDEX TERMS Comparative analysis, fundamental diagram, gender differences, pedestrian dynamics, single-file movement.

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Section: Related Workmentioning

confidence: 99%

“…[8]- [10], [18], [32]- [37]. For example, Jin et al [18] performed single-file movement experiments to analyze unidirectional pedestrian flow under high-density situations in China. A total of 203 pedestrians were involved in these experiments, where a global density of 4 m −1 was measured.…”

Section: Related Workmentioning

confidence: 99%

Experimental Investigation on the Alleged Gender-Differences in Pedestrian Dynamics: A Study Reveals No Gender Differences in Pedestrian Movement Behavior

et al. 2020

View full text Add to dashboard Cite

show abstract

“…It is worth mentioning that despite that many experiment setups in straight line walking are loop corridors/passageways, most of them have not considered the turning movements at corners and restricted the measuring areas to the straight parts. Such oval-shaped loop corridors are also different from the settings of ring-shaped corridors where pedestrian kept turning during their movements [57,60]. Seyfried et al (2005) set up experiments in a loop corridor with up to 34 German participants [57].…”

Section: Straight Linementioning

confidence: 99%

“…To investigate the flow states under hypercongested situation, Jin et al (2017) organized up to 278 college students to walk in a ring corridor with an inner width of 2 m and outer width of 4 m [60]. As a combined consideration of Jelić et al (2012) and Moussaïd et al (2012), both unidirectional and bidirectional flows were considered and the state transitions were described before and after lane formation process [93,94].…”

Section: Turningmentioning

confidence: 99%

A State-of-the-Art Review on Empirical Data Collection for External Governed Pedestrians Complex Movement

Shi

Shiwakoti

et al. 2018

Journal of Advanced Transportation

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Complex movement patterns of pedestrian traffic, ranging from unidirectional to multidirectional flows, are frequently observed in major public infrastructure such as transport hubs. These multidirectional movements can result in increased number of conflicts, thereby influencing the mobility and safety of pedestrian facilities. Therefore, empirical data collection on pedestrians’ complex movement has been on the rise in the past two decades. Although there are several reviews of mathematical simulation models for pedestrian traffic in the existing literature, a detailed review examining the challenges and opportunities on empirical studies on the pedestrians complex movements is limited in the literature. The overall aim of this study is to present a systematic review on the empirical data collection for uni- and multidirectional crowd complex movements. We first categorized the complex movements of pedestrian crowd into two general categories, namely, external governed movements and internal driven movements based on the interactions with the infrastructure and among pedestrians, respectively. Further, considering the hierarchy of movement complexity, we decomposed the externally governed movements of pedestrian traffic into several unique movement patterns including straight line, turning, egress and ingress, opposing, weaving, merging, diverging, and random flows. Analysis of the literature showed that empirical data were highly rich in straight line and egress flow while medium rich in turning, merging, weaving, and opposing flows, but poor in ingress, diverging, and random flows. We put emphasis on the need for the future global collaborative efforts on data sharing for the complex crowd movements.

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“…At earlier stage, pedestrian motion data were extracted manually or using semi-automatic tracking techniques [10,[15][16][17]. Some researchers used some simple software to extract the data of these caps manually, e.g., Tracker (http://physlets.org/tracker/) [18][19][20][21]. But it needs lots of time to finish, and the efficiency is quite low.…”

Section: Introductionmentioning

confidence: 99%

The Automatic Detection of Pedestrians under the High-Density Conditions by Deep Learning Techniques

Jin

Shi

Hui

et al. 2021

Journal of Advanced Transportation

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The automatic detection and tracking of pedestrians under high-density conditions is a challenging task for both computer vision fields and pedestrian flow studies. Collecting pedestrian data is a fundamental task for the modeling and practical implementations of crowd management. Although there are many methods for detecting pedestrians, they may not be easily adopted in the high-density situations. Therefore, we utilized one emerging method based on the deep learning algorithm. Based on the top-view video data of some pedestrian flow experiments recorded by an unmanned aerial vehicle (UAV), we produce our own training datasets. We train the detection model by using Yolo v3, a very popular deep learning model among many available detection models in recent years. We find the detection results are good; e.g., the precisions, recalls, and F1 scores could be larger than 0.95 even when the pedestrian density is as high as 9.0 ped / m 2 . We think this approach could be used for the other pedestrian flow experiments or field data which have similar configurations and can also be useful for automatic crowd density estimation.

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Single-file pedestrian flow experiments under high-density conditions

Cited by 27 publications

References 50 publications

Experimental Investigation on the Alleged Gender-Differences in Pedestrian Dynamics: A Study Reveals No Gender Differences in Pedestrian Movement Behavior

Experimental Investigation on the Alleged Gender-Differences in Pedestrian Dynamics: A Study Reveals No Gender Differences in Pedestrian Movement Behavior

A State-of-the-Art Review on Empirical Data Collection for External Governed Pedestrians Complex Movement

The Automatic Detection of Pedestrians under the High-Density Conditions by Deep Learning Techniques

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