The reliability measure calculates the day-to-day variation in travel time on typical routes, and is one of the most used metrics to measure the performance of a road network. With the increasing availability of data sources for travel time, the use of data fusion technique and algorithm has becoming more common in order to maximise the strengths of each data set and avoid their weaknesses. The Intelligent Hybrid Data Model project undertaken by TMR in Queensland, Australia, has developed a hybrid dataset by combining travel time data from STREAMS (the local ITS), HERE, and Bluetooth. However, since the hybrid data can use different data sources from one day to the next, it is important to ensure that this dynamics do not introduce unnecessary variations in the travel time data, which will ultimately impact the reliability measures. Thus, this project investigated the impact of using hybrid data for travel time reliability measurements. It was discovered that, in general, the reliability measures computed by using the hybrid data are higher compared to the current practice of solely using STREAMS data. By the time of the conference, we expect to have more detailed results on the conditions under which the hybrid data produces different results, as well as the underlying causes of these differences.