Disclosing the Impact of Micro-level Environmental Characteristics on Dockless Bikeshare Trip Volume: A Case Study of Ithaca

Song, Qiwei; Li, Wenjing; Li, Jintai; Wei, Xianshun; Qiu, Waishan

doi:10.1007/978-3-031-31746-0_8

Cited by 4 publications

(1 citation statement)

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“…The micro-scale built environment described by SVI is also related to other indicators of residential behaviors, including walkability (Ha et al, 2023), bikeability (Ito and Biljecki, 2021;Qiu and Chang, 2021;Song et al, 2023), running (Dong et al, 2023), public transit ridership (Su et al, 2022), therefore the mode choice (Koo et al, 2023;Wu, Yao and Wang, 2023) and active living (Sallis et al, 2006;Steinmetz-Wood et al, 2019). Moreover, SVI can infer the urban forms like street canyons and density (Middel et al, 2019;Qiu et al, 2021) that explains local climate zones (Cao et al, 2022;Ignatius et al, 2022;X.…”

Section: Knowledge Gapmentioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Preprint

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Measuring and predicting Carbon Emission (CE) is important to enabling the main culprit of various urgent environmental issues including global warming. However, prior studies did not fully incorporate the impact of micro-level urban streetscapes, which might lead to biased prediction of urban CE. To fill the gap, we developed an effective framework to predict residential CE in urban areas from widely existing and publicly available street-view images (SVI) using machine learning. First, we used a semantic segmentation algorithm to classify more than 30 streetscape elements from SVI images to describe the built environment whose features might affect residential and transportation CE. Second, based on the streetscapes quantified, we trained a 10-fold cross-validation method with various machine learning models to predict the CE at the 1KM grid level using CE data from the PlanetData. We found first, built environment features such as sidewalks, roads, fences, buildings, and walls are significantly correlated with the residential CE. Second, the presence of buildings and subtle streetscape features (e.g., walls, fences) indicates higher-density residential areas which are related to more residential CE. Third, vegetation (e.g., trees and grass) are reversely related to residential CE. Our findings shed light on the feasibility of using a single and open data source (i.e., the SVI) to effectively model neighborhood-level CE for regions across diverse urban forms. Our framework is useful for urban planners to inform new town development and urban regeneration towards the low CE goals.

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