24014 Barrier-free facilities real-time monitoring for smart mobility of people with disabilities

Project Description:

Smart mobility is one of the key pillars in smart city. Integrating geospatial technologies with smart mobility solutions can address the challenges faced by large populations in urban areas such as Great Bay Area (GBA) with over 80 million people. In particular, a real-time search platform for barrier-free facilities will improve accessibility and mobility for people with disabilities (PwDs), the elderly, and families with strollers, and commuters carrying suitcases. This platform can be used as a navigation option to improve travel safety after being integrated with the existing transportation system.

This project will have two approaches for data collection. One way will utilize artificial intelligence (AI) techniques, particularly computer vision and machine learning algorithms, to extract and identify barrier-free facilities from street view photos. This process will involve training models on large datasets of urban imagery to recognize features such as ramps, wide doorways, tactile paving, and other accessibility indicators. If the city (such as Hong Kong, Shenzhen, Guangzhou) has an existing accessibility open database, this project will integrate and cross-verify the extracted data with existing databases of barrier-free facilities. The other way to maintain a real-time search platform is to implement a crowd-sourcing approach to data collection, allowing users to upload information about new or status of existing barrier-free facilities. This way will be supported by a user-friendly interface within mobile apps or website, encouraging community participation and engagement. The uploaded materials are not limited to locations and photos. The integration of object extraction from street view photos and crowd-sourced data ensures comprehensive coverage and community engagement. This project sets a precedent for smart mobility solutions, underlining the crucial role of technology in fostering inclusive urban environments.

Intern Tasks and Responsibilities:

For doctor students:

  • Proficiency with computer vision is essential. Who have experience in developing and training models, particularly for object recognition and data analysis is welcome.
  • Knowledge of database management systems, both relational and non-relational to store, manage, and query large datasets effectively.
  • Handle real-time data and integrate with various APIs and databases.

For master students:

  • Proficient with geographic information systems (GIS) and spatial data analysis.
  • Ability to process and analyze large datasets, including real-time data streams, using data analytics tools and techniques to extract meaningful insights.
  • User interface design.

Minimum qualifications of Intern student:

Two doctor students, two master students for lasting at least 6 months.

Term of the Project: May to Dec, 2024

Mentors:

Xintao Liu, The Hong Kong Polytechnic University