A Collaborative System for Suitable Wheelchair Route Planning

Abstract

Route planning is a challenging problem for urban computing that usually involves the processing of a huge amount of data and collaborative user feedback. Traditionally, route planning services are street-based, that is, even paths for a pedestrian are suggested in terms of streets. However, such models are not suitable for users with certain disabilities. To address this problem, we have performed a requirement analysis with a group of wheelchair-users and their companions to understand their urban mobility experience. Given that perspective, we describe in this article a sidewalk-based model to accommodate the needs for a wheelchair route planning service. The model is mathematically defined as a graph, where the vertices are the city block corners and the edges are the sidewalks or crosswalks. The edge costs are derived from important accessibility features, such as distance, path inclination, and existence and maintenance conditions of curb ramps, crosswalks, and sidewalks. The model has been designed so that user feedback is considered to help updating the model when accessibility issues are detected, by wheelchair-users and companions, or solved, by the department of city planning. We also present a route planning algorithm that provides a set of alternative routes based on accessibility conditions, and a shortcut recommender algorithm to support accessibility-related decision making by the department of city planning. Experiments, by using PgRouting and PostGIS with open data, are reported for a Brazilian city neighborhood to validate the model and the route planning service.

Accessible wayfinding and navigation: a systematic mapping study

“…Considering the smartphone operating system, in the majority of the cases, the applications are developed to run on Android, taking advantages of the high diffusion and the affordability of Android devices (e.g., [16,29,88,119]). The Apple iOS architecture seems the best choice for applications focused on blind and visually impaired users, thanks to the advanced built-in accessibility features provided by Apple devices (e.g., [6,13,57,71,75]).…”

“…The web applications presented in [92,99,143] are conceived as additional tools, with respect to mobile applications, to obtain information about accessible wayfinding and navigation. The works in [8,16,53], instead, present original web-based solutions that deal with GIS (Geographical Information System) data and applications.…”

“…Other papers paying attention to wheelchair users propose software applications for visualizing paths that are suitable to wheelchairs. In [16], a sidewalk-based model is specified to satisfy the requirements of a wheelchair route planning service; characteristics of sidewalks are very important for wheelchair users because they may present different accessibility conditions (e.g., inclinations, curb ramps, path width). A prototype of an Android mobile application is then proposed, which calculates wheelchair routes and displays them according to their accessibility characteristics; it also allows citizens to report accessibility issues to the department of urban planning.…”

“…In the outdoor context, a feasible solution to present mapbased information and routes is leaning on Google Maps web services (e.g., [4,16,31,82,85]). Alternatively, Open-StreetMap is an open-source web mapping service that is often adopted by developers for the same purposes (e.g., [22,92,97,99,114,121,122]).…”

“…A wealth of research has been conducted, along the last decade, to study and design software and devices devoted to improve the quality of life of users with disability while moving across the urban environment, with the aim of shifting the smart mobility paradigm into a smart and accessible mobility [5,118]. Scholars provided variegate approaches to accessible wayfinding and navigation, focused on different aspects, ranging from specific citizens' needs (i.e., blind users, wheelchair users [3,16]), to different contexts of use (i.e., indoor or outdoor [20,27]), to specific technologies (for instance to identify user's position [28]), to different data sources exploited (such as open data provided by municipalities or crowdsourced and crowdsensed data [31,48]). In spite of that, the presented approaches do not seem mature enough to be deployed in large-scale scenarios and in a pervasive fashion.…”