Development and Demonstration of a Novel Red Light Running Warning System Using Connected v2i Technology

Levin, Michael W.; Sun, Zongxuan; He, Suiyi; Zamanpour, Maziar; Guo, Jianshe · 2024 · ROSA P / Minnesota. Department of Transportation. Office of Research & Innovation

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Summary

**Summary** This paper addresses the persistent safety issue of red-light running (RLR), which caused 928 fatalities and approximately 116,000 injuries in the United States in 2020. While existing countermeasures such as red-light cameras and pavement markings offer some mitigation, they are often reactive or provide uniform guidance that fails to account for individual driver behavior and dynamic traffic conditions. The research team from the University of Minnesota, in collaboration with the Minnesota Department of Transportation (MnDOT), developed and demonstrated a novel Red Light Running Warning System (RLRWS) utilizing connected vehicle-to-infrastructure (V2I) technology. The system aims to provide proactive, individualized warnings to drivers to prevent violations by accounting for real-time traffic states and driver reaction models. The methodology involved a multi-stage development and validation process. First, the team installed a Roadside Unit (RSU) at the Scott County CSAH 18/CSAH 21/Southbridge Parkway intersection to broadcast Signal Phasing and Timing (SPaT) data. They constructed an on-road testbed using Onboard Units (OBUs) equipped with GPS and DSRC receivers to collect Basic Safety Messages (BSM) and SPaT data. Parallel to this, a microsimulation testbed was developed using the SUMO software to model intersection dynamics and vehicle trajectories. The core algorithm consists of three components: a traffic prediction algorithm that forecasts future traffic states using V2V and V2I data, an individual warning signal optimizer formulated as a Model Predictive Control (MPC) problem, and a driver warning display. The system was tested in two configurations: for an individual target vehicle and for vehicles in car-following platoons, where the system accounts for the behavior of preceding vehicles. Results from both simulated scenarios and real-world road tests demonstrated that the proposed RLRWS provides more effective and accurate warnings than traditional systems. In simulations, the system successfully identified scenarios where drivers were likely to run red lights, including cases involving emergency vehicles and congested traffic, and issued appropriate braking recommendations. On-road demonstrations confirmed that the system could reliably detect signal phasing and generate timely audio/visual warnings. The study also included a cost-benefit analysis, estimating the economic benefits of reduced crash rates against the hardware and installation costs. The significance of this work lies in the transition from generic, static traffic safety measures to dynamic, personalized interventions enabled by connected vehicle technology. By integrating traffic prediction with MPC-based optimization, the RLRWS offers a scalable solution that can be integrated into standard vehicle safety features. The findings suggest that such systems can significantly reduce red-light violations by providing drivers with precise, context-aware guidance on when and how much to brake, thereby enhancing intersection safety without the unintended consequences of enforcement-only strategies.

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• vru facing ehmi