Title: Eliminating bias in pedestrian density estimation: A Voronoi cell perspective
For pedestrians moving without spatial constraints, extensive research has been devoted to develop methods of density estimation. In this paper we present a new approach based on Voronoi cells, offering a means to estimate density for individuals in small, unbounded pedestrian groups. A thorough evaluation of existing methods, encompassing both Lagrangian and Eulerian approaches employed in similar contexts, reveals notable limitations. Specifically, these methods turn out to be ill-defined for realistic density estimation along a pedestrian’s trajectory, exhibiting systematic biases and fluctuations that depend on the choice of parameters. There is thus a need for a parameter-independent method to eliminate this bias. We propose a modification of the widely used Voronoi-cell based density estimate to accommodate pedestrian groups, irrespective of their size. The advantages of this modified Voronoi method are that it is an instantaneous method that requires only knowledge of the pedestrians’ positions at a give time, does not depend on the choice of parameter values, gives us a realistic estimate of density in an individual’s neighborhood, and has appropriate physical meaning for both small and large human crowds in a wide variety of situations. We conclude with general remarks about the meaning of density measurements for small groups of pedestrians. more »« less
Mullick, Pratik; Appert-Rolland, Cecile; Warren, William H; Pettre, Julien
(, Traffic and Granular Flow ’22)
Rao, KR
(Ed.)
For a group of pedestrians without any spatial boundaries, the methods of density estimation is a wide area of research. Besides, there is a specific difficulty when the density along one given pedestrian trajectory is needed in order to plot an 'individual-based' fundamental diagram. We illustrate why several methods become ill-defined in this case. We then turn to the widely used Voronoi-cell based density estimate. We show that for a typical situation of crossing flows of pedestrians, Voronoi method has to be adapted to the small sample size. We conclude with general remarks about the meaning of density measurements in such context.
Predicting the future trajectories of multiple interacting pedestrians within a scene has increasingly gained importance in various fields, e.g., autonomous driving, human–robot interaction, and so on. The complexity of this problem is heightened due to the social dynamics among different pedestrians and their heterogeneous implicit preferences. In this paper, we present Information Maximizing Spatial-Temporal Graph Convolutional Attention Network (InfoSTGCAN), which takes into account both pedestrian interactions and heterogeneous behavior choice modeling. To effectively capture the complex interactions among pedestrians, we integrate spatial-temporal graph convolution and spatial-temporal graph attention. For grasping the heterogeneity in pedestrians’ behavior choices, our model goes a step further by learning to predict an individual-level latent code for each pedestrian. Each latent code represents a distinct pattern of movement choice. Finally, based on the observed historical trajectory and the learned latent code, the proposed method is trained to cover the ground-truth future trajectory of this pedestrian with a bi-variate Gaussian distribution. We evaluate the proposed method through a comprehensive list of experiments and demonstrate that our method outperforms all baseline methods on the commonly used metrics, Average Displacement Error and Final Displacement Error. Notably, visualizations of the generated trajectories reveal our method’s capacity to handle different scenarios.
When two streams of pedestrians cross at an angle, striped patterns spontaneously emerge as a result of local pedestrian interactions. This clear case of self-organized pattern formation remains to be elucidated. In counterflows, with a crossing angle of 180°, alternating lanes of traffic are commonly observed moving in opposite directions, whereas in crossing flows at an angle of 90°, diagonal stripes have been reported. Naka (1977) hypothesized that stripe orientation is perpendicular to the bisector of the crossing angle. However, studies of crossing flows at acute and obtuse angles remain underdeveloped. We tested the bisector hypothesis in experiments on small groups (18-19 participants each) crossing at seven angles (30° intervals), and analyzed the geometric properties of stripes. We present two novel computational methods for analyzing striped patterns in pedestrian data: (i) an edge-cutting algorithm, which detects the dynamic formation of stripes and allows us to measure local properties of individual stripes; and (ii) a pattern-matching technique, based on the Gabor function, which allows us to estimate global properties (orientation and wavelength) of the striped pattern at a time T . We find an invariant property: stripes in the two groups are parallel and perpendicular to the bisector at all crossing angles. In contrast, other properties depend on the crossing angle: stripe spacing (wavelength), stripe size (number of pedestrians per stripe), and crossing time all decrease as the crossing angle increases from 30° to 180°, whereas the number of stripes increases with crossing angle. We also observe that the width of individual stripes is dynamically squeezed as the two groups cross each other. The findings thus support the bisector hypothesis at a wide range of crossing angles, although the theoretical reasons for this invariant remain unclear. The present results provide empirical constraints on theoretical studies and computational models of crossing flows.
Breda, Joseph; Campos_Zamora, Daniel; Patel, Shwetak; Froehlich, Jon E
(, ACM)
Nighttime sidewalk illumination has a significant and unequal influence on where and whether pedestrians walk at night. Despite the importance of pedestrian lighting, there is currently no approach for measuring and communicating how humans experience nighttime sidewalk light levels at scale. We introduce NightLight, a new sensing approach that leverages the ubiquity of smartphones by re-appropriating the built-in light sensor—traditionally used to adapt screen brightness—to sense pedestrian nighttime lighting conditions. We validated our technique through in-lab and street-based evaluations characterizing performance across phone orientation, phone model, and varying light levels demonstrating the ability to aggregate and map pedestrian-oriented light levels with unaltered smartphones. Additionally, to examine the impact of light level data on pedestrian route choice, we conducted a qualitative user study with 13 participants using a standard map vs. one with pedestrian lighting data from NightLight. Our findings demonstrate that people changed their routes in preference of well-light routes during nighttime walking. Our work has implications for improving personalized navigation, understanding pedestrian route choice, and expanding passive urban sensing.
This paper is on a pedestrian collision warning and avoidance system for road vehicles based on V2X communication. In cases where the presence and location of a pedestrian or group of pedestrians cannot be determined using line-of-sight sensors like camera, radar and lidar, signals from pedestrians' smartphone apps are used to detect and localize them relative to the road vehicle through the DSRC radio used for V2X communication. A hardware-in-the-loop setup using a validated automated driving vehicle model in the high fidelity vehicle dynamics simulation program Carsim Real Time with Sensors and Traffic is used along with two DSRC modems emulating the vehicle and pedestrian communications in the development and initial experimental testing of this method. The vehicle either stops or, if possible, goes around the pedestrians in a socially acceptable manner. The elastic band method is used to locally modify the vehicle trajectory in real time when pedestrians are detected on the nearby path of the vehicle. The effectiveness of the proposed method is demonstrated using hardware-in-the-loop simulations.
Mullick, Pratik, Appert-Rolland, Cécile, Warren, William H, and Pettré, Julien. Eliminating bias in pedestrian density estimation: A Voronoi cell perspective. Retrieved from https://par.nsf.gov/biblio/10652661. Physica A: Statistical Mechanics and its Applications 657.C Web. doi:10.1016/j.physa.2024.130251.
Mullick, Pratik, Appert-Rolland, Cécile, Warren, William H, & Pettré, Julien. Eliminating bias in pedestrian density estimation: A Voronoi cell perspective. Physica A: Statistical Mechanics and its Applications, 657 (C). Retrieved from https://par.nsf.gov/biblio/10652661. https://doi.org/10.1016/j.physa.2024.130251
Mullick, Pratik, Appert-Rolland, Cécile, Warren, William H, and Pettré, Julien.
"Eliminating bias in pedestrian density estimation: A Voronoi cell perspective". Physica A: Statistical Mechanics and its Applications 657 (C). Country unknown/Code not available: Elsevier. https://doi.org/10.1016/j.physa.2024.130251.https://par.nsf.gov/biblio/10652661.
@article{osti_10652661,
place = {Country unknown/Code not available},
title = {Eliminating bias in pedestrian density estimation: A Voronoi cell perspective},
url = {https://par.nsf.gov/biblio/10652661},
DOI = {10.1016/j.physa.2024.130251},
abstractNote = {For pedestrians moving without spatial constraints, extensive research has been devoted to develop methods of density estimation. In this paper we present a new approach based on Voronoi cells, offering a means to estimate density for individuals in small, unbounded pedestrian groups. A thorough evaluation of existing methods, encompassing both Lagrangian and Eulerian approaches employed in similar contexts, reveals notable limitations. Specifically, these methods turn out to be ill-defined for realistic density estimation along a pedestrian’s trajectory, exhibiting systematic biases and fluctuations that depend on the choice of parameters. There is thus a need for a parameter-independent method to eliminate this bias. We propose a modification of the widely used Voronoi-cell based density estimate to accommodate pedestrian groups, irrespective of their size. The advantages of this modified Voronoi method are that it is an instantaneous method that requires only knowledge of the pedestrians’ positions at a give time, does not depend on the choice of parameter values, gives us a realistic estimate of density in an individual’s neighborhood, and has appropriate physical meaning for both small and large human crowds in a wide variety of situations. We conclude with general remarks about the meaning of density measurements for small groups of pedestrians.},
journal = {Physica A: Statistical Mechanics and its Applications},
volume = {657},
number = {C},
publisher = {Elsevier},
author = {Mullick, Pratik and Appert-Rolland, Cécile and Warren, William H and Pettré, Julien},
}
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