{"title":"Investigating pedestrian stepping characteristics via intrinsic trajectory","authors":"","doi":"10.1016/j.physa.2024.130045","DOIUrl":null,"url":null,"abstract":"<div><p>Investigating pedestrian stepping is essential for pedestrian dynamics research, aiding in understanding pedestrian behavior and crowd modeling. However, how to calculate the basic step metrics is still controversial, and the differences between straight walking and turning steps are often overlooked in past studies. In this work, we proposed the trajectory-based measurement to more accurately calculate the step metrics and further analyze the differences between the straight walking and turning steps. The trajectory-based measurement takes the intrinsic trajectory of the pedestrian as the reference frame to guide a more universal measurement for stepping characteristics. By applying the proposed trajectory-based measurement to revisit the dataset of a single-file experiment, we identify differences between the straight walking step and the turning step from multiple perspectives. The results show that when density is low, straight walking steps exhibit larger step velocity and length, whereas turning steps display more unbalanced lateral motion. As density increases, both types of steps demonstrate greater forward motion imbalance, while pedestrians prefer to take the step on the outer side of the turn to propel their forward motion when taking turning steps. These findings deepen our understanding of pedestrian stepping behavior and provide valuable insights for future studies of pedestrian dynamics.</p></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica A: Statistical Mechanics and its Applications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378437124005545","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Investigating pedestrian stepping is essential for pedestrian dynamics research, aiding in understanding pedestrian behavior and crowd modeling. However, how to calculate the basic step metrics is still controversial, and the differences between straight walking and turning steps are often overlooked in past studies. In this work, we proposed the trajectory-based measurement to more accurately calculate the step metrics and further analyze the differences between the straight walking and turning steps. The trajectory-based measurement takes the intrinsic trajectory of the pedestrian as the reference frame to guide a more universal measurement for stepping characteristics. By applying the proposed trajectory-based measurement to revisit the dataset of a single-file experiment, we identify differences between the straight walking step and the turning step from multiple perspectives. The results show that when density is low, straight walking steps exhibit larger step velocity and length, whereas turning steps display more unbalanced lateral motion. As density increases, both types of steps demonstrate greater forward motion imbalance, while pedestrians prefer to take the step on the outer side of the turn to propel their forward motion when taking turning steps. These findings deepen our understanding of pedestrian stepping behavior and provide valuable insights for future studies of pedestrian dynamics.
期刊介绍:
Physica A: Statistical Mechanics and its Applications
Recognized by the European Physical Society
Physica A publishes research in the field of statistical mechanics and its applications.
Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents.
Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.