Martínez-Chao Tomás E, Menéndez-Díaz Agustín, García-Cortés Silverio, D'Agostino Pierpaolo
Department of Civil, Building and Environmental Engineering, University of Naples "Federico II", 80125 Naples, Italy.
Department of Construction and Manufacturing Engineering, University of Oviedo, 33004 Oviedo, Spain.
Sensors (Basel). 2024 Jun 5;24(11):3667. doi: 10.3390/s24113667.
The need to establish safe, accessible, and inclusive pedestrian routes is considered one of the European Union's main priorities. We have developed a method of assessing pedestrian mobility in the surroundings of urban public buildings to evaluate the level of accessibility and inclusion, especially for people with reduced mobility. In the first stage of assessment, artificial intelligence algorithms were used to identify pedestrian crossings and the precise geographical location was determined by deep learning-based object detection with satellite or aerial orthoimagery. In the second stage, Geographic Information System techniques were used to create network models. This approach enabled the verification of the level of accessibility for wheelchair users in the selected study area and the identification of the most suitable route for wheelchair transit between two points of interest. The data obtained were verified using inertial sensors to corroborate the horizontal continuity of the routes. The study findings are of direct benefit to the users of these routes and are also valuable for the entities responsible for ensuring and maintaining the accessibility of pedestrian routes.
建立安全、可达且包容的行人路线被视为欧盟的主要优先事项之一。我们开发了一种评估城市公共建筑周边行人通行能力的方法，以评估可达性和包容性水平，特别是针对行动不便者。在评估的第一阶段，使用人工智能算法识别行人横道，并通过基于深度学习的卫星或航空正射影像目标检测确定精确地理位置。在第二阶段，使用地理信息系统技术创建网络模型。这种方法能够验证选定研究区域内轮椅使用者的可达性水平，并确定两点之间最适合轮椅通行的路线。使用惯性传感器对获取的数据进行验证，以证实路线的水平连续性。研究结果对这些路线的使用者有直接益处，对负责确保和维护行人路线可达性的实体也很有价值。
