Implementing and Testing the Safety of Non-Motorized Road Users through Connected Everything and Traffic Signal Operations in Virtual Reality
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2025-10-01
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Edition:Final Report
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Abstract:This project developed an integrated digital twin-based human-in-the-loop simulation framework to study traveler interactions under controlled and realistic conditions. The framework coupled PTV Vissim, which managed background traffic operations and signal control through detailed microsimulation, with CARLA, which provided an immersive, physics-based virtual environment for real-time participation of human drivers, bicyclists, and pedestrians. Together, these components created a synchronized virtual replica of the Delaware Avenue corridor, enabling systematic testing of vehicle-to-pedestrian, vehicle-to-bicyclist, vehicle-to-infrastructure, and bicycle-to-infrastructure communication strategies. The first group of experiments examined how connectivity-based communication affects pedestrian behavior and decision-making at intersections, providing a deeper understanding of the mechanisms by which information exchange can enhance safety in complex crossing scenarios. The second group focused on driver-bicyclist conflicts, introducing a novel dual-simulator configuration where both agents operated simultaneously within a shared connected environment. This setup successfully reproduced measurable conflict situations-defined through surrogate safety indicators and physiological responses-demonstrating that such systems can realistically replicate stress-inducing events comparable to real-world near misses. By validating a Digital Twin framework that merges physical realism, human interaction, and connected communication, this research advances experimental methodologies for assessing multimodal safety and provides a foundation for evaluating future connected and automated mobility applications.
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Main Document Checksum:urn:sha-512:d22078e3978f6777cbfae5fe97bc543dacff5406e93745ebe50c5deed29633f0194fe2a5121b5045f851e22161cfe04504d6056844c15b705afcf5ce3344f10b
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