Application of unmanned aerial vehicles to inspect and inventory interchange assets to mitigate wrong-way entries

Das, Subasish · 2019 · ITE journal

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Summary

This study addresses the critical safety issue of wrong-way driving (WWD) crashes on freeways, which are disproportionately severe and fatal compared to other motor vehicle collisions. Exit-ramp terminals are identified as common entry points for WWD incidents, often exacerbated by inadequate signage, poor pavement markings, and confusing geometric designs. Traditional on-the-ground field surveys to inventory these assets are time-consuming, labor-intensive, and expose crews to traffic hazards. Motivated by the need for safer and more efficient data collection to support the "Toward Zero Deaths" initiative, this research evaluates the feasibility of using unmanned aerial vehicles (UAVs) to inspect and inventory interchange assets associated with WWD risks. The researchers employed a structured framework for UAV operations, encompassing scope definition, flight planning, implementation, data acquisition, processing, and interpretation. Field data were collected at two freeway interchanges in New Jersey: a modified diamond interchange (U.S. 1/College Farm Rd.) and a partial cloverleaf interchange (U.S. 18/Davidson Rd.). A DJI Phantom 4 UAV, equipped with a 12.4-megapixel camera, was used to capture high-resolution photos and video. Due to environmental risks such as overhead wires and traffic, flights were conducted manually rather than autonomously. The team developed a specific checklist to monitor critical parameters, including the presence and condition of regulatory signs (e.g., DO NOT ENTER, WRONG WAY), pavement markings (e.g., wrong-way arrows, elephant tracks), and geometric features (e.g., raised curb medians). The results demonstrated that UAVs could effectively collect high-quality inventory data for WWD mitigation. The process required approximately one hour per location for planning and flight operations, with the UAV airborne for only 5–10 minutes per flight. Data processing took an additional 30 minutes per interchange. The analysis revealed specific deficiencies at the study sites; for instance, Interchange #1 lacked wrong-way arrows, elephant tracks, stopping lines, and raised curb medians, while Interchange #2 was missing DO NOT ENTER signs and similar protective markings. The captured images, containing GPS coordinates, were compatible with GIS and CAD tools for further analysis. The study concludes that UAV technology offers a viable, safer, and more precise alternative to traditional ground surveys for inspecting interchange assets. By reducing crew exposure to traffic hazards and enabling rapid data collection, UAVs can help transportation agencies identify and address infrastructure deficiencies that contribute to WWD crashes. The findings suggest that integrating UAVs into routine safety inspections can support the development of effective countermeasures, thereby reducing the frequency and severity of wrong-way entries. The authors recommend expanding future research to include larger sample sizes and leveraging ongoing technological advancements in UAVs to further enhance transportation safety applications.

Key finding

Unmanned aerial vehicles can effectively and safely inspect interchange assets related to wrong-way driving, providing high-quality data more efficiently than traditional ground-based field surveys.

Methodology

field_study

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• naturalistic crash near crash