{"title":"Quantifying the vibrancy of streets: Large-scale pedestrian density estimation with dashcam data","authors":"Takuma Oda , Yuji Yoshimura","doi":"10.1016/j.trc.2024.104840","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104840"},"PeriodicalIF":7.6000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part C-Emerging Technologies","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968090X24003619","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
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.
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.