A dynamic traffic light management system based on wireless sensor networks for the reduction of the red-light running phenomenon

Collotta, Mario; Pau, Giovanni; Scatà, Gianfranco; Campisi, Tiziana · 2014 · OpenAlex-citations

DOI: 10.2478/ttj-2014-0001

archive: archived pipeline: cataloged verified

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Summary

This paper addresses the safety and efficiency challenges associated with Red Light Running (RLR), a dangerous driving behavior often caused by driver frustration from excessive waiting times at traffic light junctions. The authors argue that traditional fixed-cycle or manually controlled traffic lights fail to adapt to real-time traffic conditions, leading to poor queue management and increased accident risks. To mitigate this, the study proposes a dynamic traffic light management system based on Wireless Sensor Networks (WSNs) that adjusts green light durations in real-time to optimize queue lengths and reduce RLR occurrences. The proposed system architecture utilizes a distributed WSN deployed near a four-arm intersection. Reduced Function Devices (RFDs) equipped with magnetic sensors are placed along road sections to detect vehicle presence, forwarding data to Full Function Devices (FFDs) and ultimately to a First Pan Coordinator (FPC). The FPC executes an algorithm that dynamically calculates green times based on three parameters: road section length, subsection length, and approximate vehicle crossing speed. The algorithm categorizes queue lengths as normal, medium, or long. If a queue exceeds normal thresholds, the system recalculates the green time to allow more vehicles to pass, assigning higher priority to roads with longer queues. This mechanism aims to smooth traffic flow and minimize the stress that leads to intentional or unintentional RLR violations. Performance evaluations were conducted using simulations of a 4-arm junction in Enna, Italy, comparing fixed-cycle and dynamic-cycle scenarios over 60 cycles (one hour). The results demonstrated significant improvements in traffic smoothing. For Road A, the fixed cycle smoothed only 49% of vehicles (11.33 vehicles/minute), whereas the dynamic cycle smoothed 91% (21.08 vehicles/minute). Similarly, for Road B, the fixed cycle smoothed 43.35% (10.81 vehicles/minute), while the dynamic approach achieved 80% smoothing (19.93 vehicles/minute). The study also analyzed behavioral factors contributing to RLR, noting correlations between signal variation frequency, driver demographics, and violation rates, reinforcing the need for adaptive signal timing. The significance of this work lies in its demonstration that WSN-based dynamic traffic management can effectively reduce queue lengths and, consequently, the incidence of RLR accidents. By providing real-time data processing and adaptive signal control, the system addresses the limitations of static traffic lights in high-density urban areas. The findings suggest that optimizing waiting times through intelligent transportation systems can enhance road safety and traffic efficiency, offering a scalable solution for managing congestion and reducing driver frustration-induced violations.

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• automated enforcement cameras