Development of a statistically-based methodology for analyzing automatic safety treatments at isolated high-speed signalized intersections
Journal Title: Archives of Transport - Year 2017, Vol 44, Issue 4
Abstract
Crashes at isolated rural intersections, particularly those involving vehicles traveling perpendicularly to each other, are especially dangerous due to the high speeds involved. Consequently, transportation agencies are interested in reducing the occurrence of this crash type. Many engineering treatments exist to improve safety at isolated, high-speed, signalized intersections. Intuitively, it is critical to know which safety treatments are the most effective for a given set of selection criteria at a particular intersection. Without a well-defined decision making methodology, it is difficult to decide which safety countermeasure, or set of countermeasures, is the best option. Additionally, because of the large number of possible intersection configurations, traffic volumes, and vehicle types, it would be impossible to develop a set of guidelines that could be applied to all signalized intersections. Therefore, a methodology was developed in in this paper whereby common countermeasures could be modeled and analyzed prior to being implemented in the field. Due to the dynamic and stochastic nature of the problem, the choice was made to employ microsimulation tools, such as VISSIM, to analyze the studied countermeasures. A calibrated and validated microsimulation model of a signalized intersection was used to model two common safety countermeasures. The methodology was demonstrated on a test site located just outside of Lincoln, Nebraska. The model was calibrated to the distribution of observed speeds collected at the test site. It was concluded that the methodology could be used for the preliminary analysis of safety treatments based on select safety and operational measures of effectiveness.
Authors and Affiliations
Remigiusz M. Wojtal, Laurence R. Rilett
Keywords
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- EP ID EP239495
- DOI 10.5604/01.3001.0010.6163
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