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Crashes take place at intersections even when signalized due to the presence of dilemma zone, where the driver feels uncertain whether to cross or stop at the trigger of yellow signal. Erroneous decisions taken by the driver leads to rear-end or right-angle crashes, causing safety implications. Accurate position of the dilemma zone can help in implementing various safety measures and enhance the safety at signalized intersections. This study evaluates the dilemma boundaries for mixed traffic conditions using Support Vector Machine (SVM), a supervised machine learning technique. The study results indicate that the position of the dilemma zone is influenced by traffic and geometric characteristics. Results show that trucks and motorized three-wheelers have dilemma zone closer to the intersection compared to other vehicle types. The dilemma zone for mixed traffic conditions is found to be in the range of 30 m to 80 m from the stop line. The outcome of this study contributes to better understand the dilemma boundaries in developing nations with mixed traffic conditions, like India.

Investigating dilemma zone boundaries for mixed traffic conditions using support vector machines