Artificial Intelligence for transportation & security
Artificial Intelligence for transportation & security
Our research of this matching can be very useful, primarily because we are looking at not just the current set of requests you have, but also looking at the potential future growth and then planning for that potential future growth while taking policy decisions right now.
Pradeep Varakantham
In brief
- The rapid urbanisation of many cities and the lack of coordination in their use of resources such as taxis and security personnel have negatively affected a wide array of quality-of-life metrics.
- Associate Professor Pradeep Varakantham shared how AI can be integrated into transportation and security systems to improve our quality of life. For example, AI can help passengers to book rides faster while allowing drivers to be in suitable locations to get more requests.
- Aggregation systems are integral to the most advanced transportation and services technologies. Through AI and Machine Learning methods, it has been developed and adopted to improve the matching of resources and demand, thereby enhancing the efficiency of real-world transportation infrastructure, emergency response and security systems.
Artificial intelligence can be integrated into transportation and security systems to improve our quality of life.
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, Professor Pradeep Varakantham from the SMU School of Information Systems shares how AI can be used to improve transportation and security.