Navigating the Intersection: How Traffic Control Types Affect Cyclist Right- of-Way using A Mixed Logit Model Analysis

Abushattal, Mousa; Alhomaidat, Fadi; El-Yabroudi, Mohammad · 2025 · Crossref

DOI: 10.21203/rs.3.rs-6108064/v1

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Summary

This study investigates how different intersection traffic control types—specifically traffic signals, stop/yield signs, and uncontrolled intersections—affect the likelihood of drivers failing to yield the right-of-way to cyclists. Motivated by the rising popularity of cycling and the disproportionate number of cyclist fatalities occurring at intersections, the research aims to identify specific design and operational factors that contribute to driver-cyclist conflicts. The authors seek to fill a gap in existing literature by comprehensively analyzing how traffic control measures influence driver behavior and crash patterns, particularly regarding yielding failures. The researchers utilized ten years of police crash investigation data from Michigan, focusing on single-vehicle, single-cyclist intersection crashes. After filtering for missing data and multi-vehicle incidents, the final dataset comprised 5,084 crashes. The study employed a Mixed Logit Model to analyze driver responsibility, coding fault as a binary variable (driver at fault vs. not at fault). The analysis distinguished between crashes caused by a failure to yield and those involving other hazardous actions. The model accounted for unobserved heterogeneity by treating seven Michigan Department of Transportation zones as random variables, while controlling for fixed variables such as driver age, gender, weather, vehicle type, and specific driver-cyclist interaction scenarios. The results indicate that driver age, weekday occurrence, vehicle type, and speed consistently influenced the probability of yielding failure across all intersection control types. Elderly drivers showed a significantly increased likelihood of failing to yield, with odds ratios of 1.17 for yield crashes and 1.16 for other hazardous actions. Driving under the influence of alcohol or drugs had a distinct and severe impact, increasing the probability of a driver failing to yield by approximately 3.91 times compared to sober drivers. Interaction scenarios revealed significant heterogeneity; for instance, when a cyclist rode straight ahead while a driver turned left, the odds of driver fault in yield crashes increased 12.35 times. Conversely, driving straight ahead was negatively associated with yielding failures. Signalized intersections accounted for the highest number of crashes, primarily due to failed-to-yield incidents, suggesting that traffic signals alone do not prevent non-compliance. The findings underscore the need for a deeper understanding of driver attitudes and behaviors in interactions with cyclists. The study concludes that strategies to reduce intersection complexity, such as better coordination of bicycle facilities with specific traffic control types and enhanced driver education regarding cyclist behavior and traffic rules, are essential for improving safety. By identifying that certain maneuvers and demographic factors significantly increase crash risk, the research provides actionable insights for engineers and planners to design safer intersections and mitigate driver-cyclist conflicts.

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Topics

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• cyclist safety
• driver vru interaction