Density and Velocity Patterns during One Year of Pedestrian Tracking

Brščić, Dražen; Zanlungo, Francesco; Kanda, Takayuki

doi:10.1016/j.trpro.2014.09.011

Cited by 30 publications

(48 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using three-dimensional (3D) range sensors and the algorithm described in [23], we tracked the position and velocity of pedestrians in a ≈900 m 2 area of the building for more than 800 h during a one-year time span, and we video recorded the tracking area using 16 different cameras. The environment, described in detail in [24] and shown in Fig. 1, consists mainly of a large atrium and a long "corridor," and it has a mixed population composed of commuters and local workers (prevalent on working days) and shoppers (prevalent on nonworking days).…”

Section: A Trackingmentioning

confidence: 99%

Spatial-size scaling of pedestrian groups under growing density conditions

Zanlungo¹,

Brščić²,

Kanda³

2015

Phys. Rev. E

Self Cite

View full text Add to dashboard Cite

We study the dependence on crowd density of the spatial size, configuration, and velocity of pedestrian social groups. We find that, in the investigated density range, the extension of pedestrian groups in the direction orthogonal to that of motion decreases linearly with the pedestrian density around them, both for two- and three-person groups. Furthermore, we observe that at all densities, three-person groups walk slower than two-person groups, and the latter are slower than individual pedestrians, the differences in velocities being weakly affected by density. Finally, we observe that three-person groups walk in a V-shaped formation regardless of density, with a distance between the pedestrians in the front and back again almost independent of density, although the configuration appears to be less stable at higher densities. These findings may facilitate the development of more realistic crowd dynamics models and simulators.

show abstract

Section: A Trackingmentioning

confidence: 99%

Spatial-size scaling of pedestrian groups under growing density conditions

Zanlungo¹,

Brščić²,

Kanda³

2015

Phys. Rev. E

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recent progress of crowd simulation in VGEs [10][11][12][13][14] coupled with advancements in virtual reality technologies and devices have provided novel insights into GIS. The existing crowd simulation models often disregard interactions between and within pedestrian groups such as relatives, friends, and colleagues [15], although the rules of crowd dynamics, such as arching at exits [15,16], linear and V-like walking formations [15,17], and the influence of crowd density on pedestrian behaviors [18][19][20], are well known. Actually, groups occupy a high proportion in crowds in various types of locations, such as entertainment places or office areas [15,[21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Social Force Model-Based Group Behavior Simulation in Virtual Geographic Environments

Huang

Gong

et al. 2018

IJGI

View full text Add to dashboard Cite

Virtual geographic environments (VGEs) are extensively used to explore the relationship between humans and environments. Crowd simulation provides a method for VGEs to represent crowd behaviors that are observed in the real world. The social force model (SFM) can simulate interactions among individuals, but it has not sufficiently accounted for inter-group and intra-group behaviors which are important components of crowd dynamics. We present the social group force model (SGFM), based on an extended SFM, to simulate group behaviors in VGEs with focuses on the avoiding behaviors among different social groups and the coordinate behaviors among subgroups that belong to one social group. In our model, psychological repulsions between social groups make them avoid with the whole group and group members can stick together as much as possible; while social groups are separated into several subgroups, the rear subgroups try to catch up and keep the whole group cohesive. We compare the simulation results of the SGFM with the extended SFM and the phenomena in videos. Then we discuss the function of Virtual Reality (VR) in crowd simulation visualization. The results indicate that the SGFM can enhance social group behaviors in crowd dynamics.

show abstract

“…Fine-scale data collections via pedestrian tracking have been growing in complexity and acquisition scales [7][8][9], both in [10,11] and out [6,12,13] of controlled laboratory environments.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, measurements in real-life enable and are necessary if one aims at resolved statistical descriptions of physical observables (e.g., positions, velocities, accelerations) or to quantify related rare events [16,17]. In this case, large amount of data are required, and they can be obtained via accumulating measurements with continuous and long-time ranged experimental campaigns [8,12]. From the technical point of view, real-life measurements present the challenge for computer vision to identify pedestrians automatically (see e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Frame vs. Trajectory Analyses of Pedestrian Dynamics Asymmetries in a Staircase Landing

Corbetta¹,

Lee²,

Muntean³

et al. 2017

Coll Dyn

View full text Add to dashboard Cite

Real-life, out-of-laboratory, measurements of pedestrian walking dynamics allow extensive and fully-resolved statistical analyses. However, data acquisition in real-life is subjected to the randomness and heterogeneity that characterizes crowd flows over time. In a typical real-life location, disparate flow conditions follow one another in random order: for instance, a low density pedestrian co-flow dynamics may suddenly turn into a high density counter-flow scenario and then back again. Isolating occurrences of similar flow conditions within the acquired data is a paramount first step in the analyses in order to avoid spurious statistics and to enable qualitative comparisons.In this paper we extend our previous investigation on the asymmetric pedestrian dynamics on a staircase landing, where we collected a large statistical database of measurements from ad hoc continuous recordings [1]. This contribution has a two-fold aim: first, method-wise, we discuss an analysis workflow to consider large-scale experimental measurements, suggesting two querying approaches to automatically extract occurrences of similar flow scenarios out of datasets. These pursue aggregation of similar scenarios on either a frame or a trajectory basis. Second, we employ these two different perspectives to further explore asymmetries in the pedestrian dynamics in our measurement site. We report cross-comparisons of statistics of pedestrian positions, velocities and accelerations vs. flow conditions as well as vs. querying approach.

show abstract

Density and Velocity Patterns during One Year of Pedestrian Tracking

Cited by 30 publications

References 10 publications

Spatial-size scaling of pedestrian groups under growing density conditions

Spatial-size scaling of pedestrian groups under growing density conditions

Social Force Model-Based Group Behavior Simulation in Virtual Geographic Environments

Frame vs. Trajectory Analyses of Pedestrian Dynamics Asymmetries in a Staircase Landing

Contact Info

Product

Resources

About