Quantifying the vibrancy of streets: Large-scale pedestrian density estimation with dashcam data

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
Takuma Oda , Yuji Yoshimura
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引用次数: 0

Abstract

This paper proposes a new methodology for measuring street-level pedestrian density that combines the strengths of image-based observations with the scalability of drive-by sensing. Despite its importance, existing methods for measuring pedestrian activity have several limitations, including high costs, limited coverage, and privacy concerns. To overcome these issues, our approach exploits operation logs generated by dashboard cameras of moving vehicles to estimate pedestrian density for each street, which is validated with data from approximately 3,000 taxis operating in central Tokyo. We produce vibrancy maps for 292 station areas in central Tokyo by leveraging machine learning to estimate pedestrian density in streets where measurement data is scarce. We also evaluate the reliability and coverage of the measurement and illustrate how the measured pedestrian density data can be utilized for assessing the validity of walkability measures. The paper concludes that this approach could provide valuable data to inform urban planning and city operations.

量化街道的活力:利用行车记录仪数据估算大规模行人密度
本文提出了一种测量街道行人密度的新方法,该方法结合了基于图像观测的优势和驾车感应的可扩展性。尽管行人活动非常重要,但现有的行人活动测量方法存在一些局限性,包括成本高、覆盖范围有限和隐私问题。为了克服这些问题,我们的方法利用行驶车辆仪表盘摄像头生成的运行日志来估算每条街道的行人密度,并通过东京市中心约 3000 辆出租车的数据进行验证。通过利用机器学习估算测量数据稀缺的街道的行人密度,我们绘制了东京市中心 292 个车站区域的活力地图。我们还评估了测量的可靠性和覆盖范围,并说明了如何利用测量的行人密度数据来评估步行能力测量的有效性。本文的结论是,这种方法可以为城市规划和城市运营提供有价值的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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