Extending the work on CRUISE, we are also leveraging on our expertise to develop new traffic control and detection strategies to improve movement of public transport. This will utilize CRUISE, ERP2, Central Fleet Management System (CFMS), new infrastructure sensors placed at bus stops and EZLINK fare card data to intelligently derive insights. Machine learning techniques will be used to carry out in-depth analysis that enables prediction of bus load and passenger demand at bus stops, which can be used to estimate and regulate total time spent at bus stops. New control schemes for regulating/facilitating the processes of bus entering and leaving bus stops/bus bays with signal control, adaptive bus lane control and pre-signaling operations for busses to bypass queues will be introduced. Besides the solutions mentioned above, the Division aims to work on the development of a city wide short term traffic prediction solution that aims to carry out fast and accurate prediction of traffic state for time-scales of up to 30 minutes. Such a solution can be used to support many other ITS systems such as traffic control, fleet management systems, route guidance, etc.