Crowd-Based Disability-Friendly Route Finder: A Microservice-Oriented Global Accessibility Navigation System

Abstract

This paper presents the Crowd-Friendly Smart Route Finder, an accessibility-aware, real-time route guidance system designed to assist users with diverse mobility needs. The platform integrates live crowd levels, weather conditions, and infrastructure data such as ramps, stairs, and obstacles to provide personalized, adaptive navigation. Built using a FastAPI Python backend and an interactive Leaflet.js frontend, the system features voice-assisted search and navigation, dynamic heatmaps, and user profile-based routing mode selection (foot, bike, car, wheelchair). Unlike prior accessibility-focused navigation tools that rely primarily on static map data, this system incorporates real-time user-generated data and external APIs to enhance route reliability and safety. Testing demonstrates consistent performance with sub-second route computation times and effective adaptive routing based on live environmental inputs. This scalable, multimodal platform aims to improve urban mobility for differently-abled and general users by delivering smart, crowd-aware, and accessible routing globally.

Introduction

Conventional navigation systems often ignore accessibility needs, focusing on the fastest or shortest routes without considering real-time obstacles like stairs, crowds, or weather. To address this, a smart route guidance system was developed that integrates annotated map data, real-time crowd levels, weather, and obstacle reports. Using user mobility profiles, FastAPI, Leaflet.js, and a modified Dijkstra algorithm, the platform provides personalized, accessibility-aware navigation with dynamic route adjustments.

The system employs a distributed microservice architecture with backend routing, frontend visualization, and static data integration from global OSM networks. Routes are computed using a graph-based model with attributes such as slope, step height, path type, and live overlays, ensuring safe, adaptive navigation. Testing showed the system delivers real-time, profile-adaptive routes globally, with response times of 1.2–1.6 seconds, confirming its effectiveness for diverse mobility needs.

Conclusion

The Crowd-Friendly Smart Route Finder demonstrates that combining live crowd, weather, and obstacle data with accessibility profiles enables truly adaptive and inclusive navigation. The system reliably generates optimal, personalized routes for all users, including those with mobility challenges. By integrating real-time and static data on an efficient, scalable platform, this solution advances smart, accessible urban mobility and sets the stage for future enhancements and wider adoption.

References

[1] Melis et al., \"A Microservice-Based Architecture for the Development of Accessible, Crowdsensing-Based Mobility Platforms,\" CTS, 2016. [2] A. Darvishy, H.-P. Hutter, R. Mosimann, \"Towards Personalized Accessible Routing,\" ICCHP, 2022. [3] A. Mobasheri et al., \"Wheelmap: The Wheelchair Accessibility Crowdsourcing Platform,\" Open Geospatial Data, 2017. [4] A. Darvishy et al., \"Making Mobile Map Applications Accessible for Visually Impawired People,\" Springer, 2019. [5] OpenStreetMap. https://www.openstreetmap.org

Copyright

Copyright © 2025 Ajay S. Chhajed, Chavan, Nidhish Chincholkar, Vivek Chaudhari, Swayam Bukkawar, Sharvari Burakle, Chaitanya Chavan, Pranjal Chhatwani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.