Estimating pedestrian volume using Street View images: A large-scale validation test. (May 2020)

• Record Type: Journal Article
• Authors: Chen, Long; Lu, Yi; Sheng, Qiang; Ye, Yu; Wang, Ruoyu; Liu, Ye
• Is Part Of: Computers, environment and urban systems. Volume 81(2020)
• Publication Date: 2020-05
• Subjects: Street view images -- Pedestrian volume -- Big data -- Machine learning; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2020.101481
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Pedestrian volume is an important indicator of urban walkability and vitality. Hence, information on pedestrian volumes of different streets is indispensable for creating healthy, pedestrian-oriented cities. Pedestrian volume data have traditionally been collected through field observations, which has many methodological limitations, e.g. time-consuming, labor-intensive, and inefficient. Assessing pedestrian volume automatically from Street View images (SVIs) with machine learning techniques can overcome such limitations because this approach offers a wide geographic reach and consistent image acquisition. Nevertheless, this new method has not been rigorously validated, and its accuracy remains unclear. In this study, we conducted a large-scale validation test by comparing pedestrian volume extracted from SVIs with the results from field observations for more than 700 street segments in Tianjin, China. A total of 4507 sampling points along these street segments were used to collect SVIs. The results demonstrated that using SVIs with machine learning techniques is a promising method for estimating pedestrian volumes with a large geographic reach. Automated pedestrian volume detection could achieve reasonable (Cronbach's alpha ≥0.70) or good (Cronbach's alpha ≥0.80) levels of accuracy. It is worth noting that various factors of SVIs and street segments may affect the accuracy. SVIs with higher image quality, larger image size, and collection times closer to the targeted periods produced more accurate results. The automated method also worked better in areas with high pedestrian volume and high street connectivity. Highlights: Pedestrian volume was assessed with street view images and machine learning. Automated pedestrian detection was validated against field observation. The street connectivity and pedestrian volume affects the level of accuracy. The quality, size, and collection time of street view image also affects accuracy.