UrbanAccess: Query Driven Urban Analytics Platform for Detecting Complex Accessibility Event Patterns using Tactile Surfaces

Abstract

The smart city concept has now become one of the key enablers in urban city management. The adoption and permeation of ICT and AI-driven techniques have enabled the authorities to resolve poor urban planning issues with improved delivery of citizen services. Major urban problem is addressing the accessibility issue across cities road crossing and facilitating visually impaired people via well-defined infrastructure. The research presented in this paper emphasized urban analytics that studies the road crossings and challenges one faces when accessing the footpaths of a city using the Tactile surfaces. This work demonstrates a distributed event analytics platform- GNOSIS to detect complex accessibility event patterns. GNOSIS ingest video data streams from cities infrastructure such as CCTV and detect tactile surface event patterns using an ensemble of deep learning models using a declarative query language. The work analyzes mainly three types of tactile surface - Blister, Cycleway and Directional, collected from different cities in Ireland using crowd-sourcing techniques. GNOSIS makes decisions in real-time based on the type of tactile surface, colour and the making pattern.