Congestion is one of the main problems prevalent in surface public transport systems. Congestion affects travel time, service regularity, and system costs. The accuracy of identification of speed-related problems depends on the quality and precision of the tools used to analyze the available operational data, that is, data on the impact on users and operational costs. Furthermore, traffic signals contribute significantly to operational delay, and programming traffic signals to prioritize transit can substantially increase the operating speeds. For example, green split redistributions are simple and low-cost adjustments to traffic signals that can significantly improve operating speeds. However, it might be expensive to collect the necessary traffic flow information. This study had a twofold contribution. First, it presents an extension of a tool that uses buses GPS data to identify and rank bottlenecks, in which queue lengths and bus load profiles are now considered for estimating user delay. Second, a methodology to identify which of these bottlenecks can be easily removed (Quick wins) by reallocating the traffic signal’s green times among phases. The modified methodology was applied in the city of Santiago de Chile, where the inclusion of both the queue length and bus load profiles is shown to modify the ranking. Additionally, Quick wins opportunities were detected and addressed resulting in an average 85% reduction in bus delays.