Urban Pedestrian Routes’ Accessibility Assessment Using Geographic Information System Processing and Deep Learning-Based Object Detection

Abstract

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.

Keywords

• inclusiveness
• geographic information system (GIS)
• pedestrian crossing
• deep learning
• wheelchair-friendly routes
• inertial sensors