Unsignalized intersections are critical locations in terms of the risk and vulnerability involved in incorrect assessment of gaps by drivers on minor roads and higher speeds of vehicles on major roads. The speed profile of minor road and major road vehicles with subject movements under consideration is analyzed. Right-turning movement from a minor road is a critical maneuver with several conflict points. This paper examines the performance level and risk associated with right-turn movements from minor roads by estimating the critical gap and capacity with due consideration to the vehicle types. Analyzing drivers’ gap acceptance behavior is essential for designing safer and more efficient intersections in terms of performance. The model is formulated for gap acceptance of right-turn movements from minor roads by logistic regression. The critical gap, the minimum time interval deemed safe by drivers, is crucial for estimating intersection capacity and associated risk toward planning prospective traffic management measures. By analyzing critical gaps for different vehicle types, estimating the probability of gap acceptance, and developing predictive models, this research aims to enhance traffic flow and safety at unsignalized intersections. The findings will inform urban traffic planners, contributing to enhancing the design and overall traffic efficiency of an unsignalized intersection.

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Critical Gap Estimation and Its Effects on Capacity and Safety at a High-Speed Uncontrolled T-Intersection

  • B. S. Bhaghyalekshmi,
  • P. N. Salini,
  • T. Ajitha

摘要

Unsignalized intersections are critical locations in terms of the risk and vulnerability involved in incorrect assessment of gaps by drivers on minor roads and higher speeds of vehicles on major roads. The speed profile of minor road and major road vehicles with subject movements under consideration is analyzed. Right-turning movement from a minor road is a critical maneuver with several conflict points. This paper examines the performance level and risk associated with right-turn movements from minor roads by estimating the critical gap and capacity with due consideration to the vehicle types. Analyzing drivers’ gap acceptance behavior is essential for designing safer and more efficient intersections in terms of performance. The model is formulated for gap acceptance of right-turn movements from minor roads by logistic regression. The critical gap, the minimum time interval deemed safe by drivers, is crucial for estimating intersection capacity and associated risk toward planning prospective traffic management measures. By analyzing critical gaps for different vehicle types, estimating the probability of gap acceptance, and developing predictive models, this research aims to enhance traffic flow and safety at unsignalized intersections. The findings will inform urban traffic planners, contributing to enhancing the design and overall traffic efficiency of an unsignalized intersection.