GeoAI technologies in earth observation for traffic management
From heatwaves to pandemic diseases, the urban environments of the world face numerous challenges, innovative geospatial and AI technologies offer ground-breaking solutions and insights into the dynamic changes occurring in our natural and social surroundings. The applications of GeoAI are rapidly expanding across various fields, encompassing transportation, urban and public safety, planning, climate change and natural disasters.
Prof. Qihao WENG, Chair Professor of Geomatics and Artificial Intelligence of the Department of Land Surveying and Geo-Informatics, and Global STEM Professor, established the PolyU Research Centre for Artificial Intelligence in Geomatics (RCAIG), to focus on the development of original and innovative AI methodologies and technologies for geomatics and their applications in urban areas, with the goal of it becoming a global R&D hub in GeoAI.
Traffic management
Satellite observations are invaluable tools for our community, relying on satellite imagery, videos and data that are crucial for informed decision-making in urban resilience and public health. For instance, satellite observations help us understand the impact of extreme heatwave on population exposure and aid in the development of urban flood monitoring algorithms. Real-time data acquisition also facilitate applications in traffic conditions, air quality, nature disasters, population movement and urban land use.
GeoAI utilises machine learning and deep learning to effectively analyse intricate information, offering applications like real-time traffic management. Through the integration of diverse data modalities, such as text, images, and knowledge graphs, GeoAI enables accurate traffic flow prediction, route optimisation, accident warnings, and the planning of an efficient traffic network. Consequently, this contributes to the advancement of smart traffic management.
To enhance the efficiency of ride-hailing platforms and achieve intelligent management of their services, research team of the RCAIG has developed a multi-agent order matching and vehicle repositioning (MAMR) approach. This innovative technology focuses on coordinating the supply and demand of ride-hailing services, ultimately aiming to improve their overall efficiency.
This approach provides a ground-breaking solution to tackle two critical aspects of efficient ride-hailing services. Firstly, it addresses order matching by efficiently assigning orders to available vehicles. Secondly, it incorporates proactive vehicle repositioning, strategically deploying idle vehicles to regions with potentially high demand.
Based on multi-agent deep reinforcement learning (MARL), this innovation solves the complex planning in transportation and offers a news perspective on long-term spatiotemporal planning problem. The research conducted by Ms Mingyue XU, a researcher of the RCAIG and the team, titled “Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services,” was published on International Journal of Geographical Information Science. The study demonstrated outperforming results, including reduced passenger rejection rates and driver idle time.
