Implementation of Highway Safety Manual Freeway Ramp Segment Calibration and the Lessons Learned

2021 ◽  
Author(s):  
Imalka C. Matarage ◽  
Sunanda Dissanayake
Keyword(s):  
Highway Safety ◽  
Lessons Learned ◽  
Highway Safety Manual

New Intersection Crash Prediction Models for the Second Edition of the Highway Safety Manual

2021 ◽  
pp. 036119812110398
Author(s):  
Darren J. Torbic ◽  
Daniel Cook ◽  
Joseph Grotheer ◽  
Richard Porter ◽  
Jeffrey Gooch ◽  
...  
Keyword(s):  
Traffic Control ◽  
Prediction Models ◽  
Highway Safety ◽  
Crash Severity ◽  
Crash Prediction ◽  
Part C ◽  
Highway Safety Manual ◽  
Rural And Urban ◽  
Diamond Interchanges ◽  
Crash Prediction Models

The objective of this research was to develop new intersection crash prediction models for consideration in the second edition of the Highway Safety Manual (HSM), consistent with existing methods in HSM Part C and comprehensive in their ability to address a wide range of intersection configurations and traffic control types in rural and urban areas. The focus of the research was on developing safety performance functions (SPFs) for intersection configurations and traffic control types not currently addressed in HSM Part C. SPFs were developed for the following general intersection configurations and traffic control types: rural and urban all-way stop-controlled intersections; rural three-leg intersections with signal control; intersections on high-speed urban and suburban arterials (i.e., arterials with speed limits greater than or equal to 50 mph); urban five-leg intersections with signal control; three-leg intersections where the through movements make turning maneuvers at the intersections; crossroad ramp terminals at single-point diamond interchanges; and crossroad ramp terminals at tight diamond interchanges. Development of severity distribution functions (SDFs) for use in combination with SPFs to estimate crash severity as a function of geometric design elements and traffic control features was explored; but owing to challenges and inconsistencies in developing and interpreting the SDFs, it was recommended for the second edition of the HSM that crash severity for the new intersection configurations and traffic control types be addressed in a manner consistent with existing methods in Chapters 10, 11, and 12 of the first edition, without use of SDFs.

scholarly journals Comparison of Different Road Segmentation Methods

2019 ◽  
Vol 31 (2) ◽  
pp. 163-172
Author(s):  
Maen Qaseem Ghadi ◽  
Árpád Török
Keyword(s):  
Road Safety ◽  
Road Network ◽  
Highway Safety ◽  
Segmentation Method ◽  
Constant Length ◽  
Highway Safety Manual ◽  
Segmentation Methods ◽  
Average Annual Daily Traffic ◽  
Clustering Approach ◽  
Road Segmentation

In road safety, the process of organizing road infrastructurenetwork data into homogenous entities is called segmentation.Segmenting a road network is considered thefirst and most important step in developing a safety performancefunction (SPF). This article aims to study the benefitof a newly developed network segmentation method which is based on the generation of accident groups applying K-means clustering approach. K-means algorithm has been used to identify the structure of homogeneous accident groups. According to the main assumption of the proposed clustering method, the risk of accidents is strongly influenced by the spatial interdependence and traffic attributes of the accidents. The performance of K-means clustering was compared with four other segmentation methods applying constant average annual daily traffic segments, constant length segments, related curvature characteristics and a multivariable method suggested by the Highway Safety Manual (HSM). The SPF was used to evaluate the performance of the five segmentation methods in predicting accident frequency. K-means clustering-based segmentation method has been proved to be more flexible and accurate than the other models in identifying homogeneous infrastructure segments with similar safety characteristics.

Developing Crash Modification Factors for Horizontal Curves on Rural Two-Lane Undivided Highways Using a Cross-Sectional Study

2017 ◽  
Vol 2636 (1) ◽  
pp. 53-61 ◽  
Author(s):  
Lingtao Wu ◽  
Dominique Lord ◽  
Srinivas Reddy Geedipally
Keyword(s):  
Regression Models ◽  
Cross Sectional Study ◽  
Highway Safety ◽  
Sectional Study ◽  
Cross Sectional ◽  
Highway Safety Manual ◽  
Horizontal Curves ◽  
Before And After ◽  
Crash Modification Factors ◽  
Curve Radius

Horizontal curves have been identified as experiencing more crashes than tangent sections on roadways, especially on rural two-lane highways. The first edition of the Highway Safety Manual provides crash modification functions (CM functions) for curves on rural two-lane highways. The CM functions proposed in the manual may suffer from both outdated data and analysis technique. Before-and-after studies are usually the preferred method for estimating the safety effects of treatments. Unfortunately, this method is not feasible for curves. Previous studies have frequently used regression models for developing CM functions for horizontal curves. As recently documented in the literature, some potential problems exist with using regression models to develop crash modification factors. This research utilized a cross-sectional study to develop curvature CM functions. Curves located on Texas rural two-lane undivided highways were divided into a number of bins based on the curve radius. Safety was predicted with the assumption that these curves had been tangents. The observed number of crashes that occurred on the curves was compared with the dummy tangents and for different bins. The results showed that the horizontal curve radius has a significant role in the risk of a crash. From these results, a new CM function was developed. The prediction performance of the Highway Safety Manual CM function was compared with the new CM function in this study and another function that was recently proposed in the literature. It was found that the new CM function documented in this study outperformed both.

scholarly journals Application of Highway Safety Manual to Italian Divided Multilane Highways

2012 ◽  
Vol 53 ◽  
pp. 910-919 ◽  
Author(s):  
Salvatore Cafiso ◽  
Giacomo Di Silvestro ◽  
Giovanni Di Guardo
Keyword(s):  
Highway Safety ◽  
Highway Safety Manual

Calibration of the Highway Safety ManualGiven Safety Performance Functions for RuralMultilane Segments and Intersections in Kansas

2017 ◽  
Author(s):  
Syeda Rubaiyat Aziz ◽  
Sunanda Dissanayake
Keyword(s):  
Safety Evaluation ◽  
Highway Safety ◽  
Safety Performance ◽  
Regression Coefficients ◽  
Local Conditions ◽  
Crash Prediction ◽  
Highway Safety Manual ◽  
Satisfactory Level ◽  
Safety Performance Functions ◽  
Predictive Methods

The Highway Safety Manual (HSM) provides models and methodologies for safety evaluation and prediction of safety performance of various types of roadways. However, predictive methods in the HSM are of limited use if they are not calibrated for local conditions. In this study, calibration procedures given in the HSM were followed for rural segments and intersections in Kansas. Results indicated that HSM overpredicts fatal and injury crashes and underpredicts total crashes on rural multilane roadway segments in Kansas. Therefore, existing safety performance functions (SPFs) must be adjusted for Kansas conditions, in order to increase accuracy of crash prediction. This study examined a way to adjust HSM calibration procedures by development of new regression coefficients for existing HSM-given SPF. Final calibration factors obtained through modified SPFs indicated significant improvement in crash prediction for rural multilane segments in Kansas. Additionally, obtained calibration factors indicated that the HSM is capable of predicting crashes at intersections at satisfactory level.

Highway Safety Manual Training Materials

2012 ◽  
Author(s):  
Karen K. Dixon ◽  
Fei Xie ◽  
Neil Kopper ◽  
Yanfen Zhou ◽  
Ida van Schalkwyk ◽  
...  
Keyword(s):  
Highway Safety ◽  
Highway Safety Manual ◽  
Training Materials
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