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• W4306981601 endingPage "104603" @default.
• W4306981601 startingPage "104603" @default.
• W4306981601 abstract "• An integrated methodology for multicriteria walkability of urban streets evaluation. • Automated physical walkability measurement using GIS , deep learning, and AHP. • Developed evaluation criteria for perceived walkability of urban streets. • Determining the extent to which physical walkability represents perceived walkability. • Provides a street-level walkability analysis tool that suggests walkable paths. The walkability of urban streets (WoUS) benefits public health and urban livability, but there is still no consensus on the quantitative and comprehensive evaluation of walkability. However, emerging deep-learning, sensor-network, and GIS technologies suggest the possibility of overcoming previous limitations. Therefore, this study explores a new approach for multicriteria evaluation of WoUS, including physical and perceived walkability. After capturing street-view images, traffic-flow data, and environmental-sensor data covering the streets to be analyzed, we automatically calculate physical walkability from four criteria (walkability index, street quality, traffic flow, and physical environment). We first integrate 2-dimensional analysis of walk score, noise, and light using GIS, then perform 3-dimensional composition calculations of greenery, enclosure, and relative walking width using semantic segmentation, vehicle density using multi-object tracking, and indicator weighting distributions using an analytic hierarchy process. Perceived walkability is evaluated by a hierarchy of walking needs model comprising feasibility, accessibility, safety, comfort, and pleasurability. As an empirical study, we conduct an experiment within Anonymous University for physical and perceived walkability evaluations. The results indicate that with perceived WoUS as a benchmark, the automated physical WoUS results reveal the strengths in data integration and processing that are feasible, reliable, and cost-effective. This was an initial attempt at constructing an integrated research methodology for WoUS from various walking-related variables. This framework could support designers as an assistive tool for generating both quantitative and qualitative judgments from various criteria and recommending walkable pedestrians paths to motivate people to walk further, longer, and more often, rather than providing the shortest paths based on a path-finding algorithm." @default.
• W4306981601 created "2022-10-22" @default.
• W4306981601 creator A5041836482 @default.
• W4306981601 creator A5048679031 @default.
• W4306981601 creator A5059468598 @default.
• W4306981601 date "2023-02-01" @default.
• W4306981601 modified "2023-10-18" @default.
• W4306981601 title "Integrating GIS, deep learning, and environmental sensors for multicriteria evaluation of urban street walkability" @default.
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• W4306981601 doi "https://doi.org/10.1016/j.landurbplan.2022.104603" @default.
• W4306981601 hasPublicationYear "2023" @default.
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