CosIn:基于统计的行人运动空间-速度时间延迟计算算法

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY Transportation Research Part C-Emerging Technologies Pub Date : 2024-11-09 DOI:10.1016/j.trc.2024.104912
Jinghui Wang , Wei Lv , Shuchao Cao , Zhensheng Wang
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引用次数: 0

摘要

空间-速度时间延迟(TD)的精确评估对于区分行人运动中的预期行为和反应行为至关重要。此外,TD 量表还有助于评估人群的潜在碰撞倾向,从而为评估风险提供重要的量化指标。有鉴于此,本文介绍了用于评估行人运动过程中 TD 的 CosIn 算法,包括 CosIn-1 和 CosIn-2 两种算法。CosIn-1 算法通过分析计算 TD,取代了离散交叉相关的数值方法,而 CosIn-2 算法则从统计角度估算 TD。具体来说,CosIn-1 算法解决了单个行人 TD 的精确计算问题,而 CosIn-2 算法则用于评估人群规模的 TD,同时解决了实时评估的当务之急。CosIn-1 和 CosIn-2 算法的功效分析分别通过单排行人实验和人群穿越实验的数据进行。在此过程中,采用了离散交叉相关法作为基线来评估这两种算法的性能,结果表明这两种算法都具有显著的准确性。该算法有助于精确评估人群中的行为模式和碰撞倾向,从而使我们能够从一个全新的角度了解人群动态。
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CosIn: A statistical-based algorithm for computation of space-speed time delay in pedestrian motion
Precise assessment of Space-speed time delay (TD) is critical for distinguishing between anticipation and reaction behaviors within pedestrian motion. Besides, the TD scale is instrumental in the evaluation of potential collision tendency of the crowd, thereby providing essential quantitative metrics for assessing risk. In this consideration, this paper introduced the CosIn algorithm for evaluating TD during pedestrian motion, which includes both the CosIn-1 and CosIn-2 algorithms. CosIn-1 algorithm analytically calculates TD, replacing the numerical method of discrete cross-correlation, whereas the CosIn-2 algorithm estimates the TD from a statistical perspective. Specifically, the CosIn-1 algorithm addresses the precise computation of TD for individual pedestrians, while the CosIn-2 algorithm is employed for assessing TD at the crowd scale, concurrently addressing the imperative of real-time evaluation. Efficacy analyses of the CosIn-1 and CosIn-2 algorithms are conducted with data from single-file pedestrian experiments and crowd-crossing experiments, respectively. During this process, the discrete cross-correlation method was employed as a baseline to evaluate the performance of both algorithms, which demonstrated notable accuracy. This algorithm facilitate the precise evaluation of behavior patterns and collision tendency within crowds, thereby enabling us to understand the crowds dynamics from a new perspective.
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来源期刊
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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