Analysis and Modelling of Effects of Traffic Light Operations Variability to Violation Rates at Junction

Galatioto, Fabio; Giuffrè, Tullio; Bell, Margaret Carol; Tesoriere, Giovanni · 2012 · Crossref

DOI: 10.5539/mas.v6n10p53

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Summary

This study addresses the quantitative evaluation of "proneness" to unintentional red-light running (RLR) at urban signalized intersections, specifically focusing on how variability in traffic light operations influences violation rates. Motivated by the significant safety and financial costs associated with RLR, which is identified as a primary cause of crashes, the research aims to validate and extend a micro-simulation modeling framework previously developed by the authors. The primary objective is to demonstrate the transferability of this modeling approach to a new site and to assess its capability to predict RLR behaviors under varying traffic signal timings, thereby providing tools for traffic operators to optimize junction designs and signal strategies. The research was conducted at a four-arm isolated signalized junction in Enna, Italy, over a 13-day period. Data collection involved extensive video recording and manual on-street measurements of traffic flows, speeds, and RLR violations. To test the impact of operational variability, the authors, in collaboration with local police, implemented different traffic light cycle lengths (84 and 94 seconds) and varied green/red time allocations daily for the first seven days, before maintaining a constant setup for the remaining six days. The study utilized the AIMSUN micro-simulation model, calibrated with hourly Origin-Destination matrices and driver behavior parameters such as reaction times and acceleration limits. To simulate unintentional RLR, the model employed a "double traffic light" methodology, using a virtual stop line to capture vehicles that crossed the physical stop line during the amber phase, effectively modeling the dilemma zone behavior. The results showed that the micro-simulation model achieved a high level of accuracy in reproducing traffic flows, with a regression coefficient ($R^2$) of 0.99. In terms of RLR prediction, the model generally followed the trends of observed violations, particularly at the beginning and end of the observation period. However, during the days when traffic light settings were changed daily, observed violations were consistently lower than modeled predictions. The authors attribute this discrepancy to driver uncertainty and reduced aggression caused by the unpredictable signal changes. Conversely, when the signal timing remained constant, drivers adapted to the pattern, leading to violation rates that aligned more closely with the simulation. The data indicated that variability in traffic light settings sensibly reduced the total number of violations compared to static timings. The significance of this study lies in the validation of micro-simulation as a robust tool for predicting unintentional RLR proneness at the design stage. The findings suggest that implementing actuated traffic lights, which introduce variability in cycle and green timings to accommodate traffic flow changes, could serve as an effective countermeasure to reduce violations. Additionally, the study highlights the importance of improving signal visibility, such as prohibiting on-street parking near junctions, to further mitigate unintentional violations. The paper concludes that while the model performs well, further research is needed to understand driver adaptation to signal changes over longer periods, and the approach holds potential for broader application by local authorities to identify high-risk junctions and optimize traffic control strategies.

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• roadway lighting effects