Transforming Urban Transport: FCL Global’s AI for Smart Mobility Symposium showcases AI’s role in revolutionising smart mobility
Held at the Urban Redevelopment Authority (URA) centre, the symposium showcased Adaptive Mobility, Infrastructure, and Land Use (AMIL) Module's enhanced research and engagement with local AI-transport ecosystem.
On 3 July, the Adaptive Mobility, Infrastructure and Land Use (AMIL) module organised the ‘AI for Smart Mobility’ symposium at the Urban Redevelopment Authority (URA) centre. The event kicks off AMIL’s AI-enhanced research and facilitates our exploration as well as engagement with local AI-transport ecosystem.
The symposium attracted approximately 50 participants, including local stakeholders (Land Transport Authority LTA, URA, and Singapore Land Authority), industry partners (JTC, Surbana Jurong Consultants, Arup, CPG, Ramboll) among others and researchers from local universities and institutes (National University of Singapore, Nanyang Technological University, Singapore University of Technology and Design, and Singapore Management University). The event was moderated by external page Assistant Professor Prateek Bansal, the Singapore-PI of AMIL with ETH-PI, Prof. Dr Bryan T. Adey.
Four distinguished speakers presented their cutting-edge research on the application of AI methods to address urban transport challenges for smart mobility.
- external page Associate Professor Hai Wang from SMU delivered a presentation titled ‘Synergizing Learning for the Optimization of Transportation and Logistics Systems’. His talk focused on the latest advancements in AI approaches to enhance the performance of maritime logistics and food delivery systems.
- external page Assistant Professor Feng Zhu from NTU presented on ‘The Application of Transfer Learning in Traffic Flow Modeling’. This presentation introduced transfer models developed for advanced traffic management, aimed at alleviating urban traffic congestion.
- external page Assistant Professor Kaidi Yang from NUS discussed ‘Trustworthy Decision-Making for Connected and Automated Transportation Systems’. He explained their research on applying reinforcement learning for autonomous vehicle motion control and fleet management for enhancing the safety and efficiency of transport.
- external page Assistant Professor Mai Anh Tien from SMU presented ‘Reinforcement Learning for Route Choice Predictions’. His talk demonstrated the use of inverse reinforcement learning methods for dynamic route choices.
These presentations showcased how human-augmented AI techniques can improve trust between human users and machines, while achieving more sustainable, efficient, and safe urban mobility in the future. The audience actively engaged with the speakers during the separate Q&A sessions and the networking segment, fostering a productive exchange of ideas and insights in AI for smart mobility.
