2020/09/06 by Sakib Mahmud Khan, Khan, Sakib Mahmud, Anthony D. Patire +1
Engineering · Social Sciences · #FOS: Electrical engineering #Signal Processing (eess.SP) #Traffic Prediction and Management Techniques #Traffic control and management #Transportation Planning and Optimization #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2009.04401
openalex publication_date 2020/09/06 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Transportation agencies monitor freeway performance using various measures\nsuch as VMT (Vehicle Miles Traveled), VHD (Vehicle Hours of Delay), and VHT\n(Vehicle Hours Traveled). Public transportation agencies typically rely on\npoint detector data to estimate these freeway performance measures. Point\ndetectors, such as inductive loops cannot capture the travel time for a\ncorridor, which can lead to inaccurate performance measure estimation. This\nresearch develops a hybrid method, which estimates of freeway performance\nmeasures using a mix of probe data from third-parties and data from traditional\npoint detectors. Using a simulated I-210 model, the overall framework using\nmultiple data sources is evaluated, and compared with the traditional point\ndetector-based estimation method. In the traditional method, point speeds are\nestimated with the flow and occupancy values using the g-factors. Data from 5%\nof the total vehicle are used to generate the third-party vendor provided\ntravel time data. The analysis is conducted for multiple scenarios, including\npeak and off-peak periods. Findings suggest that fusing data from both\nthird-party vendors and point detectors can help estimate performance measures\nbetter, compared to the traditional method, in scenarios that have noticeable\ntraffic demand on freeways.\n