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Using mobile phone big data and street view images to explore the mismatch between walkability and walking behavior
Transportation Research Part A: Policy and Practice ( IF 6.3 ) Pub Date : 2024-01-21 , DOI: 10.1016/j.tra.2023.103946
Xuan He , Sylvia Y. He

Stimulating more citizens to walk plays an essential role in building a healthy city. This paper explores the mismatch between walkability and walking behavior, using mobile phone data, street view images, and various sources of open data. Using Shenzhen as our case study, we identified walking trips of 6 months in 2021 from cellular mobile data, taking the rule-based heuristics approach. We collected ground truth GPS data to validate the walking trip extraction method. Open data and deep learning enabled quantifying walkability from the perspective of four pedestrian needs: safety, convenience, continuity, and attractiveness. We employed geospatial techniques to identify the mismatch areas between walkability and walking behavior in the city. We also explored the spatially varying effects of walkability on walking behavior. Our results showed that the mismatch areas with high-level walking trips but low-level walkability mainly occurred in the fringe areas of the central business district (CBD) and subcenters that require prioritizing more interventions. Moreover, walkability showed strong effects on walking trips in the inner suburbs. For the four aspects of our walkability framework, safety and convenience had greater positive effects on walking trips in suburbs than in urban areas. Continuity promotes walking trips mainly in the city’s western sector. The positive effect of attractiveness on walking trips clustered in the central and western parts of the city. Based on the findings, we provide prioritized and contextualized built-environment intervention strategies and policy recommendations for urban designers and transportation planners.



中文翻译:

利用手机大数据和街景图像探索步行适宜性与步行行为的不匹配

鼓励更多市民步行对于建设健康城市具有重要作用。本文利用手机数据、街景图像和各种开放数据源,探讨了步行适宜性和步行行为之间的不匹配。以深圳为案例研究,我们采用基于规则的启发式方法,从蜂窝移动数据中确定了 2021 年 6 个月的步行旅行。我们收集了地面实况 GPS 数据来验证步行行程提取方法。开放数据和深度学习可以从四个行人需求的角度来量化步行性:安全性、便利性、连续性和吸引力。我们采用地理空间技术来识别城市中步行适宜性和步行行为之间的不匹配区域。我们还探讨了步行适宜性对步行行为的空间变化影响。结果表明,步行出行水平较高但步行适宜性较低的不匹配区域主要发生在需要优先进行更多干预的中央商务区(CBD)和副中心的边缘地区。此外,步行适宜性对内城区的步行旅行显示出强烈的影响。对于我们的步行框架的四个方面,安全性和便利性对郊区步行旅行的积极影响大于城市地区。连续性主要促进了城市西部地区的步行旅行。吸引力对步行出行的积极影响集中在城市中西部地区。根据研究结果,我们为城市设计师和交通规划者提供优先考虑和情境化的建筑环境干预策略和政策建议。

更新日期:2024-01-26
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