Third-Party Data Fusion to Estimate Freeway Performance Measures

Abstract

Transportation agencies monitor freeway performance using various measures such as VMT (vehicle-miles traveled), VHD (vehicle-hours of delay), and VHT (vehicle-hours traveled). They typically rely on data from point detectors to estimate these freeway performance measures. Point detectors such as inductive loops cannot capture the travel time for a corridor, leading to inaccurate performance measure estimation. This research develops a hybrid method, which estimates freeway performance measures using a mix of probe vehicle data provided by third-party vendors and data from traditional point detectors. Using a simulated model of a freeway (Interstate-210), 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 g-factors. Data from 5% of the total vehicles are used to generate the third-party provided travel time data. The analysis is conducted for multiple scenarios, including peak and off-peak periods. Results suggest that fusing probe vehicle data from third-party vendors with data from point detectors can help transportation agencies estimate performance measures better than the traditional method, in scenarios that have noticeable traffic demand on freeways.