arXiv subjects

Shankar Prawesh

Publications and source records attributed to Shankar Prawesh.

3 recordsLinked to original sources

arXiv2025

Automatic Estimation of Pedestrian Gait Features using a single camera recording: Algorithm and Statistical Analysis for Gender Difference and Obstacle Interactions

The pedestrian gait features - body sway frequency, amplitude, stride length, and speed, along with pedestrian personal space and directional bias, are important parameters to be used in different pedestrian dynamics studies. Gait feature measurements are paramount for wide-ranging applications, varying from the medical field to the design of bridges. Personal space and choice of direction (directional bias) play important roles during crowd simulations. In this study, we formulate an automatic algorithm for calculating the gait features of a trajectory extracted from video recorded using a single camera attached to the roof of a building. Our findings indicate that females have 28.64% smaller sway amplitudes, 8.68% smaller stride lengths, and 8.14% slower speeds compared to males, with no significant difference in frequency. However, according to further investigation, our study reveals that the body parameters are the main variables that dominate gait features rather than gender. We have conducted three experiments in which the volunteers are walking towards the destination a) without any obstruction, b) with a stationary non-living obstacle present in the middle of the path, and c) with a human being standing in the middle of the path. From a comprehensive statistical analysis, key observations include no significant difference in gait features with respect to gender, no significant difference in gait features in the absence or presence of an obstacle, pedestrians treating stationary human beings and stationary obstacles the same given that the gender is same to match the comfort level, and a directional bias towards the left direction, likely influenced by left-hand traffic rule in India.

physics.soc-ph↗

arXiv2025

Variable Goal Approach (VGA) Enhancing Pedestrian Dynamics Modeling

Pedestrian dynamics models have provided valuable insights into pedestrian interactions, collision avoidance, and self-organized crowd behavior using mathematical, computational, AI-based, and heuristic approaches. However, existing models often fail to capture fundamental aspects of human decision-making, particularly the tendency to adopt indirect routes by sequentially selecting intermediate goals within the line of sight. In this study, we propose a novel Variable Goal Approach (VGA) that integrates human intelligence into pedestrian dynamics models by introducing multiple intermediate goals, termed variable goals, which guide pedestrians toward their final destination. These variable goals function as an adaptive guidance mechanism, enabling smoother transitions and dynamic navigation. VGA also enhances the efficiency of a model while minimizing interactions and disruptions. By strategically positioning variable goals, VGA introduces an element of stochasticity. This allows the model to simulate varied pedestrian paths under identical conditions, reflecting the diversity in human decision-making. In addition to its effectiveness in simple scenarios, VGA demonstrates strong performance in replicating high-density scenarios, such as lane formation, providing results that closely match real-world data.

physics.soc-ph↗

arXiv2025

Benchmarking Pedestrian Dynamics Models for Common Scenarios: An Evaluation of Force-Based Models

Extensive research in pedestrian dynamics has primarily focused on crowded conditions and associated phenomena, such as lane formation, evacuation, etc. Several force-based models have been developed to predict the behavior in these situations. In contrast, there is a notable gap in terms of investigations of the moderate-to-low density situations. These scenarios are extremely commonplace across the world, including the highly populated nations like India. Additionally, the details of force-based models are expected to show significant effects at these densities, whereas the crowded, nearly packed, conditions may be expected to be governed largely by contact forces. In this study, we address this gap and comprehensively evaluate the performance of different force-based models in some common scenarios. Towards this, we perform controlled experiments in four situations: avoiding a stationary obstacle, position-swapping by walking toward each other, overtaking to reach a common goal, and navigating through a maze of obstacles. The performance evaluation consists of two stages and six evaluating parameters - successful trajectories, overlapping proportion, oscillation strength, path smoothness, speed deviation, and travel time. Firstly, models must meet an eligibility criterion of at least 80\% successful trajectories and secondly, the models are scored based on the cutoff values established from the experimental data. We evaluated five force-based models where the best one scored 57.14\%. Thus, our findings reveal significant shortcomings in the ability of these models to yield accurate predictions of pedestrian dynamics in these common situations.

physics.soc-ph↗