scholarly journals Estimating the Safety Performance Function for Urban Unsignalized Four-Legged One-Way Intersections in Palermo, Italy

2014 ◽  
Vol 60 (1) ◽  
pp. 41-54 ◽  
Author(s):  
O. Giuffrè ◽  
A. Granà ◽  
T. Giuffrè ◽  
R. Marino ◽  
S. Marino
Keyword(s):  
Predictive Model ◽  
Traffic Flow ◽  
Generalized Estimating Equations ◽  
Geometric Design ◽  
Safety Performance ◽  
Correlation Matrices ◽  
Performance Function ◽  
Safety Performance Function ◽  
Unsignalized Intersections ◽  
Working Correlation

Abstract Starting from consideration that urban intersections are sites with promise for safety and operational improvements, the paper describes the steps taken to develop a crash predictive model for estimating the safety performance of urban unsignalized intersections located in Palermo, Italy. The focus is on unsignalized four-legged one-way intersections widespread in Italian downtowns. The sample considered in the study consist of 92 intersections in Palermo, Italy. For the study were collected crashes occurred in the sites during the years 2006-2012, geometric design and functional characteristics and traffic flow. Results showed that data were overdispersed and NB1 distributed. In order to account for the correlation within responses Generalized Estimating Equations (GEE) were used under different working correlation matrices.

Two Problems of Averaging Arising in the Estimation of the Relationship Between Accidents and Traffic Flow

1998 ◽  
Vol 1635 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Abraham Mensah ◽  
Ezra Hauer
Keyword(s):  
Traffic Flow ◽  
Annual Average ◽  
Performance Function ◽  
Safety Performance Function ◽  
Time Period ◽  
Annual Average Daily Traffic ◽  
Averaging Problem ◽  
Long Time ◽  
The Relationship ◽  
The Ideal

A function linking the expected accident frequency to traffic flow is called a safety performance function (SPF). SPFs are estimated from data for various facilities and accident types. Typically, accident counts over a period of a year or more, and estimates of average flow for such periods, serve as data. The ideal is for SPFs to represent cause-effect regularities. However, because accident counts are for a long time period and because average flows are used, two issues of averaging arise. First, the cause-effect relationship is between accidents and the flows prevailing near the time of accident occurrence. Therefore, ideally, these should be the argument of the SPF. In practice, however, either because of lack of detail or difficulties of estimation, average flows are used for estimation. The question is what problems arise when average flows, such as annual average daily traffic, instead of the flows at the time of the accident are used as the argument of the SPF. This is the argument averaging problem. Second, there are at least two (daytime and nighttime) and perhaps many more cause-effect SPFs that prevail in the course of a year. Ideally, each relationship should be estimated separately. The question is what problems arise if one joint SPF is estimated when two or more separate functions should have been used. This is the function averaging problem. After analysis, how to account and how to correct for the argument averaging problem are shown. At this time, avoiding the function averaging problem by estimating daytime and nighttime SPFs separately can be the only recommendation.

How the choice of safety performance function affects the identification of important crash prediction variables

2016 ◽  
Vol 88 ◽  
pp. 1-8 ◽  
Author(s):  
Ketong Wang ◽  
Jenna K. Simandl ◽  
Michael D. Porter ◽  
Andrew J. Graettinger ◽  
Randy K. Smith
Keyword(s):  
Safety Performance ◽  
Performance Function ◽  
Crash Prediction ◽  
Safety Performance Function

scholarly journals Rural two-lane two-way three-leg and four-leg stop-controlled intersections: predicting road safety effects

2017 ◽  
Vol 12 (2) ◽  
pp. 117-126 ◽  
Author(s):  
Salvatore Antonio Biancardo ◽  
Francesca Russo ◽  
Daiva Žilionienė ◽  
Weibin Zhang
Keyword(s):  
Estimating Equation ◽  
Safety Performance ◽  
Crash Frequency ◽  
Performance Function ◽  
Left Turn ◽  
Explanatory Variables ◽  
Crash Data ◽  
Safety Performance Function ◽  
Lane Width ◽  
Residuals Analysis

The study focused on grade-level rural two-lane two-way three-leg and two-lane two-way four-leg stop-controlled intersections located in the flat area with a vertical grade of less than 5%. The goal is to calibrate one Safety Performance Function at these intersections by implementing a Generalized Estimating Equation with a binomial distribution and compare to the results with yearly expected crash frequencies by using models mainly refered to the scientific literature. The crash data involved 77 two-lane two-way intersections, of which 25 two-lane two-way three-leg intersections are without a left-turn lane (47 with left-turn lane), 5 two-lane two-way four-leg intersections without a left-turn lane (6 with a left-turn lane). No a right-turn lane is present on the major roads. Explanatory variables used in the Safety Performance Function are the presence or absence of a left-turn lane, mean lane width including approach lane and a left-turn lane width on the major road per travel direction, the number of legs, and the Total Annual Average Daily Traffic entering the intersection. The reliability of the Safety Performance Function was assessed using residuals analysis. A graphic outcome of the Safety Performance Function application has been plotted to easily assess a yearly expected crash frequency by varying the Average Annual Daily Traffic, the number of legs, and the presence or absence of a left-turn lane. The presence of a left-turn lane significantly reduces the yearly expected crash frequency values at intersections.

scholarly journals Prediction of motorcyclist traffic crashes in Cartagena (Colombia): development of a safety performance function

2021 ◽  
Author(s):  
Holman Ospina-Mateus ◽  
Leonardo Augusto Quintana Jiménez ◽  
Francisco J. Lopez-Valdes ◽  
Shib Sankar Sana
Keyword(s):  
Road Safety ◽  
Traffic Accidents ◽  
Negative Binomial ◽  
Road Traffic ◽  
Negative Binomial Regression ◽  
Safety Performance ◽  
Traffic Crashes ◽  
Performance Function ◽  
Safety Performance Function ◽  
Significant Factors

Motorcyclists account for more than 380,000 deaths annually worldwide from road traffic accidents. Motorcyclists are the most vulnerable road users worldwide to road safety (28% of global fatalities), together with cyclists and pedestrians. Approximately 80% of deaths are from low- or middle-income countries. Colombia has a rate of 9.7 deaths per 100,000 inhabitants, which places it 10th in the world. Motorcycles in Colombia correspond to 57% of the fleet and generate an average of 51% of fatalities per year. This study aims to identify significant factors of the environment, traffic volume, and infrastructure to predict the number of accidents per year focused only on motorcyclists. The prediction model used a negative binomial regression for the definition of a Safety Performance Function (SPF) for motorcyclists. In the second stage, Bayes' empirical approach is implemented to identify motorcycle crash-prone road sections. The study is applied in Cartagena, one of the capital cities with more traffic crashes and motorcyclists dedicated to informal transportation (motorcycle taxi riders) in Colombia. The data of 2,884 motorcycle crashes between 2016 and 2017 are analyzed. The proposed model identifies that crashes of motorcyclists per kilometer have significant factors such as the average volume of daily motorcyclist traffic, the number of accesses (intersections) per kilometer, commercial areas, and the type of road and it identifies 55 critical accident-prone sections. The research evidences coherent and consistent results with previous studies and requires effective countermeasures for the benefit of road safety for motorcyclists.

scholarly journals Development of a Safety Performance Function for Expressway Tollgates

2015 ◽  
Vol 33 (1) ◽  
pp. 81-89
Author(s):  
Taehun Lee ◽  
Ho-Chan Kwak ◽  
Dong-Kyu Kim ◽  
Seung-Young Kho
Keyword(s):  
Safety Performance ◽  
Performance Function ◽  
Safety Performance Function
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