Abstract
The safety of at grade road intersections is a relevant issue with social, economic, and environmental implications. It is related to the behavior of a driver approaching an intersection that, in its turn, is affected by kinematic and physiological variables. This study proposes a model to calculate the intersection operation time (IOT) for typical non-signalized 4-leg and 3-leg (or T-leg) urban intersections. Data available in the literature have been considered in order to identify the points of interest and assess the number and the time of a driver’s eye fixation on them. When approaching an intersection, the probability of glancing in a particular area changes with the distance to the yield or stop line; for this reason, a probabilistic approach was used to model the phenomenon. All possible maneuvers have been considered: left turning, right turning, and through-movement. The proposed model allowed an objective comparison between time spent by drivers for various maneuvers and layout conditions, and identification of the critical conditions. Indeed, significant differences in terms of IOT were found: they could lead to modification of the traffic management considering different needs of road users, traffic demand, and geometrical and functional constraints.
Highlights
An at-grade intersection is the area shared by two or more roads that join or cross each other [1]
The aim of this paper is to propose an analytical model to calculate intersection operation time (IOT) for urban at-grade road intersections, considering the different phases of admitted maneuvers and the time needed for the analysis, decision, and operation performed by drivers
The 4-leg intersection is named X1, while the T-leg scenarios in Figure 6b–d is named as T1, T2, and T3, respectively
Summary
An at-grade intersection is the area shared by two or more roads that join or cross each other [1]. The intersection operation time (IOT) is the time duration of a vehicle on a specific intersection from the approach to the complete execution of the maneuver: it is composed of approaching, waiting, and decision times. The controller of the system is the driver him/herself: he/she must analyze the intersection area (and the evolving scenario) while approaching it and change his/her movements according to the surrounding environment, in order to perform the operation needed to complete the maneuver. They collect sight and hearing outside input data and afterwards make a decision; during this time, drivers have to avoid collision with other vehicles or road users. IOT is a crucial parameter for measuring the intersection efficiency [7]
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