A methodology for prediction accuracy assessment of intelligent traffic signal control algorithms with SPaT messages

New smart traffic signal control algorithms are capable of predicting the traffic signal state (red, green, or amber) changes, which can be provided to users to achieve more efficient traffic flow. However, these predictions pose an uncertain impact on the traffic flow and safety depending upon the quality of prediction. The information regarding the current state as well as the predicted residual time of state is communicated to other users in the form of Signal Phase and Timing (SPaT) data. In this paper, the SPaT message data is analyzed from an on-road pilot of different traffic signal control algorithms on provincial roads in the Province of North Holland. For analysis, new methods and indicators for quantification of prediction accuracy and quality of algorithm are proposed. These indicators can either be used for correction of state change prediction in real-time or for comparative analysis of the performance of different traffic signal control algorithms. This paper presents three main findings. First, it is found that a half fixed algorithm has very high prediction accuracy up to 99% for optimized directions. Second, the prediction accuracy of Time to Amber predictions improved by around 30% with this algorithm. Third, the overall reliability of prediction always increased with the use of the algorithm.