  Transportation agencies monitor freeway performance using various measures
such as VMT (Vehicle Miles Traveled), VHD (Vehicle Hours of Delay), and VHT
(Vehicle Hours Traveled). Public transportation agencies typically rely on
point detector data to estimate these freeway performance measures. Point
detectors, such as inductive loops cannot capture the travel time for a
corridor, which can lead to inaccurate performance measure estimation. This
research develops a hybrid method, which estimates of freeway performance
measures using a mix of probe data from third-parties and data from traditional
point detectors. Using a simulated I-210 model, the overall framework using
multiple data sources is evaluated, and compared with the traditional point
detector-based estimation method. In the traditional method, point speeds are
estimated with the flow and occupancy values using the g-factors. Data from 5%
of the total vehicle are used to generate the third-party vendor provided
travel time data. The analysis is conducted for multiple scenarios, including
peak and off-peak periods. Findings suggest that fusing data from both
third-party vendors and point detectors can help estimate performance measures
better, compared to the traditional method, in scenarios that have noticeable
traffic demand on freeways.
