Wayfinding and Navigation for People with Disabilities Using Social Navigation Networks

To achieve safe and independent mobility, people usually depend on published information, prior experience, the knowledge of others, and/or technology to navigate unfamiliar outdoor and indoor environments. Today, due to advances in various technologies, wayfinding and navigation systems and services are commonplace and are accessible on desktop, laptop, and mobile devices. However, despite their popularity and widespread use, current wayfinding and navigation solutions often fail to address the needs of people with disabilities (PWDs). We argue that these shortcomings are primarily due to the ubiquity of the compute-centric approach adopted in these systems and services, where they do not benefit from the experience-centric approach. We propose that following a hybrid approach of combining experience-centric and compute-centric methods will overcome the shortcomings of current wayfinding and navigation solutions for PWDs

A Personalized Trip Planner For Vulnerable Road Users

This research presents an adaptive and personalized routing model that enables individuals with disabilities to save their route preferences to a mobility assistant platform. The proactive approach based on anticipated user need accommodates vulnerable road users’ personalized optimum dynamic routing rather than a reactive approach passively awaiting input. Most of the currently available trip planners target the general public’s use of simpler route options prioritized based on static road characteristics. These static normative approaches are only satisfactory when conditions of intermediate intersections in the network are consistent, a constant rate of change occurs per each change of the segment condition, and the same fixed routes are valid every day regardless of the user preference. In this study, we model the vulnerable road user mobility problem by accommodating personalized preferences changing by time, sidewalk segment traversability, and the interaction between sidewalk factors and weather conditions for each segment contributing to a path choice. The developed reinforcement learning solution presents a lower average cost of personalized, accessible, and optimal path choices in various trip scenarios and superior to traditional shortest path algorithms (e.g., Dijkstra) with static and dynamic extensions

Operationalizing transformative tourism: Creating dementia-friendly outdoor and nature-based visitor experiences

© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/A values-based approach embedded in the transformative tourism research paradigm is used to examine dementia-friendly outdoor and nature-based experiences. Interview and site audits were conducted to explore the visitor economy-nature-well-being nexus. Using thematic analysis, researchers set out to understand how organizational change can improve the visitor journey through values and actions that create an accessible visitor experience for people suffering from dementia. The findings show that while the current practice of providing events and tailored sessions for the local community is a good start, further development is needed; this paper offers selected pathways to becoming a dementia-friendly business, including the Importance of an organisational champion and an accessible site and a nature-based experience or event that appeals to a wide audience. Theoretically, this work operationalizes transformative tourism and provides a framework for future work.Peer reviewe