Evaluating a signalized intersection performance using unmanned aerial Data

Ashqer, Mujahid I.; Ashqar, Huthaifa I.; Elhenawy, Mohammed; Almannaa, Mohammed; Aljamal, Mohammad A.; Rakha, Hesham; Bikdash, Marwan · 2023 · OpenAlex-citations

DOI: 10.1080/19427867.2023.2204249

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Summary

This paper presents a novel microscopic methodology for computing measures of effectiveness (MOEs) at signalized intersections using vehicle trajectory data collected by Unmanned Aerial Vehicles (UAVs). The research addresses the limitations of traditional traffic data collection methods, such as inductive loops and probe vehicles, which often lack spatial coverage or suffer from measurement errors in congested urban areas. By leveraging drone data, the study aims to provide a low-cost, minimally intrusive, and highly accurate means of monitoring traffic performance, including queue lengths, delays, emissions, and safety metrics, in real-time. The study utilizes a 14-minute video dataset from the pNEUMA experiment, recorded by a drone hovering over a busy three-way signalized intersection in Athens, Greece. The researchers extracted second-by-second trajectories for over 750 vehicles, including cars, buses, and heavy-duty vehicles, across five lanes. The methodology involves dividing the intersection area into lane-specific sub-polygons using Google Earth and Keyhole Markup Language (KML) files. Vehicle trajectories were mapped to these lanes to create time-space diagrams. Shockwave analysis was then applied to identify backward formation and recovery waves, allowing for the precise determination of queue lengths and spillbacks. The study defines a vehicle as stopped if its speed is less than or equal to 1.2 m/s. The proposed framework estimates several key MOEs. Travel time and delay were calculated by tracking individual vehicle entry and exit times within the defined links. Vehicle stops were quantified by recording deceleration events, capturing multiple partial stops per vehicle. Crash rates were estimated using a time-based exposure model derived from US national crash statistics, while fuel consumption and CO2 emissions were computed using the Virginia Tech Comprehensive Power-Based Fuel Consumption Model (VT-CPFM), which relies on instantaneous vehicle power. Additionally, the study calibrated the Van Aerde car-following model using the drone data to construct fundamental diagrams, overcoming limitations of traditional Greenshields and Pipes models. Results indicate that the microscopic approach successfully captured transient vehicle behaviors and lane-changing movements. The analysis revealed that heavy-duty vehicles experienced the maximum travel time (approximately 32 seconds) on Lane 3, while Lane 5 had the highest average travel time due to its proximity to the curb. The 95th percentile speed was found to be approximately 42 km/h. The study confirms that UAV data can effectively estimate complex MOEs, including spillback occurrences and emission levels, with high accuracy and relatively low computational complexity. These findings suggest that drone-based monitoring is a viable tool for real-time traffic management and intersection performance evaluation, offering detailed insights into traffic dynamics that macroscopic methods often miss.

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• exposure measurement