SmartBFA: A passive crowdsourcing system for point-to-point barrier-free access

• Main Authors: KAMALDIN, Mohammed Nazir, KEE, Susan, KONG, Songwei, LEE, Chengkai, LIANG, Huiguang, SAINI, Alisha, TAN, Hwee-pink, TAN, Hwee Xian
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: IoT devices; mobility aids; wheelchair users; smart mobility and accessibility for barrier free access; pedestrian infrastructure; public infrastructure; PwDs; persons with disabilities; SmartBFA; point-to-point accessibility information; accessible point-to-point routes; Databases and Information Systems; Health Information Technology
• Online Access: https://ink.library.smu.edu.sg/sis_research/4667; https://ink.library.smu.edu.sg/context/sis_research/article/5670/viewcontent/2019_lcn_smartbfa.pdf
• Institution: Singapore Management University

At the Bloomberg Live `Sooner Than You Think' forum [1] held in Singapore in 2018, nearly 75% of delegates picked inclusiveness to be the key measure of success for a smart city. An inclusive smart city is a citizen-centered approach that extends the experiences provided by smart city solutions to all citizens, including seniors and persons with disabilities (PwDs).Despite existing regulations on barrier-free accessibility for buildings and public infrastructure, pedestrian infrastructure is generally still inaccessible to PwDs in many parts of the world. In this paper, we present SmartBFA (Smart Mobility and Accessibility for Barrier Free Access) - a publicly-funded initiative in Singapore that aims to design a scalable and sustainable system that can collect, classify and determine accessible point-to-point routes to address interconnection gaps in first and last mile BFA paths for persons requiring barrier-free access (such as wheelchair users and seniors with mobility aids).In SmartBFA, point-to-point accessibility information is passively crowdsourced from IoT devices that are retrofitted on the wheelchairs of participants, as they go about their daily commute. We share preliminary findings from data acquired from 68 wheelchair participants between May 2018 to Mar 2019, spanning across 23,000 hrs and 40,000 km of traveled paths. We compare travel patterns of participants with varying wheelchair types, as well as demonstrate the feasibility and scalability of such a crowdsourced approach for acquiring accessibility data.