Rapid urbanisation, coupled with the lack of coordination in the use of resources, such as taxis and security personnel, has negatively affected a wide array of quality-of-life metrics. These include waiting time in queues, response time for emergencies, and the number of traffic violations in cities. Using AI and Machine Learning methods, aggregation systems have been developed and adopted to improve the matching of resources and demand, thereby enhancing the efficiency of real-world transportation, emergency response and security systems.

In this podcast, Associate Professor Pradeep Varakantham from the SMU School of Information Systems shares how AI can be used to improve transportation and security.