Developing Safety Performance Functions for Railway Grade Crossings: A Case Study of Canada

2015 ◽  
Author(s):  
Shahram Heydari ◽  
Liping Fu
Keyword(s):  
Weibull Model ◽  
Safety Performance ◽  
Bayesian Hierarchical ◽  
Factors Affecting ◽  
Stop Sign ◽  
Technological Advances ◽  
Safety Performance Functions ◽  
Flashing Light ◽  
Grade Crossings ◽  
Safety Features

Although accident frequencies at railway grade crossings have shown a decreasing trend over the last two decades (partly due to implemented safety improvements and technological advances), safety at grade crossings is still a major concern since crossing accidents are usually associated with devastating consequences. This paper investigates the effect of various site attributes on railway crossing safety outcomes using recent Canada wide data from a 6-year period (2008–2013). The new data sets allow adjusting previous accident models according to latest circumstances (e.g., vehicles’ improved safety features) affecting safety dynamics at crossings. Employing Bayesian hierarchical models including the non-conventional Poisson-Weibull model, different safety performance functions were separately developed for crossings with the following major warning systems: (1) flashing light and bell (FLB), (2) flashing light, bell, and gate (FLBG), (3) standard reflectorized crossing sign (SRCS), and (4) standard reflectorized crossing sign and stop sign (SRCS & STOP). Among other findings, the results indicated that traffic exposure (product of train and vehicle), number of lanes, whistle prohibition, train speed, and road speed were the most important factors affecting accident frequencies at Canadian railway crossings. It should be also noted that safety performance functions vary, in terms of independent variables and their associated coefficients, between the aforementioned warning devices.

Simple Index to Assess the Calibration Quality of Safety Performance Functions Based on Multiple Goodness-of-Fit Metrics

2021 ◽  
pp. 036119812110088
Author(s):  
Raul E. Avelar ◽  
Karen Dixon ◽  
Boniphace Kutela ◽  
Sam Klump ◽  
Beth Wemple ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Synthetic Data ◽  
Calibration Procedure ◽  
Safety Performance ◽  
Absolute Deviation ◽  
Data Set ◽  
Safety Database ◽  
Simple Index ◽  
Safety Performance Functions

The calibration of safety performance functions (SPFs) is a mechanism included in the Highway Safety Manual (HSM) to adjust SPFs in the HSM for use in intended jurisdictions. Critically, the quality of the calibration procedure must be assessed before using the calibrated SPFs. Multiple resources to aid practitioners in calibrating SPFs have been developed in the years following the publication of the HSM 1st edition. Similarly, the literature suggests multiple ways to assess the goodness-of-fit (GOF) of a calibrated SPF to a data set from a given jurisdiction. This paper uses the calibration results of multiple intersection SPFs to a large Mississippi safety database to examine the relations between multiple GOF metrics. The goal is to develop a sensible single index that leverages the joint information from multiple GOF metrics to assess overall quality of calibration. A factor analysis applied to the calibration results revealed three underlying factors explaining 76% of the variability in the data. From these results, the authors developed an index and performed a sensitivity analysis. The key metrics were found to be, in descending order: the deviation of the cumulative residual (CURE) plot from the 95% confidence area, the mean absolute deviation, the modified R-squared, and the value of the calibration factor. This paper also presents comparisons between the index and alternative scoring strategies, as well as an effort to verify the results using synthetic data. The developed index is recommended to comprehensively assess the quality of the calibrated intersection SPFs.

Regional Perspective of Safety Performance Functions and Their Application to Florida Intersections in Suburban Residential and Urban General Context Classification Categories

2021 ◽  
pp. 036119812110084
Author(s):  
Ghalia Gamaleldin ◽  
Haitham Al-Deek ◽  
Adrian Sandt ◽  
John McCombs ◽  
Alan El-Urfali
Keyword(s):  
Negative Binomial ◽  
Regression Tree ◽  
Influential Factors ◽  
Safety Performance ◽  
General Context ◽  
Boosted Regression Tree ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Intersection Types ◽  
The Individual

Safety performance functions (SPFs) are essential tools to help agencies predict crashes and understand influential factors. Florida Department of Transportation (FDOT) has implemented a context classification system which classifies intersections into eight context categories rather than the three classifications used in the Highway Safety Manual (HSM). Using this system, regional SPFs could be developed for 32 intersection types (unsignalized and signalized 3-leg and 4-leg for each category) rather than the 10 HSM intersection types. In this paper, eight individual intersection group SPFs were developed for the C3R-Suburban Residential and C4-Urban General categories and compared with full SPFs for these categories. These comparisons illustrate the unique and regional insights that agencies can gain by developing these individual SPFs. Poisson, negative binomial, zero-inflated, and boosted regression tree models were developed for each studied group as appropriate, with the best model selected for each group based on model interpretability and five performance measures. Additionally, a linear regression model was built to predict minor roadway traffic volumes for intersections which were missing these volumes. The full C3R and C4 SPFs contained four and six significant variables, respectively, while the individual intersection group SPFs in these categories contained six and nine variables. Factors such as major median, intersection angle, and FDOT District 7 regional variable were absent from the full SPFs. By developing individual intersection group SPFs with regional factors, agencies can better understand the factors and regional differences which affect crashes in their jurisdictions and identify effective treatments.

scholarly journals The variability of urban safety performance functions for different road elements: an Italian case study

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Paolo Intini ◽  
Nicola Berloco ◽  
Gabriele Cavalluzzi ◽  
Dominique Lord ◽  
Vittorio Ranieri ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Prediction Models ◽  
Safety Performance ◽  
Common Trends ◽  
Main Research ◽  
Pavement Markings ◽  
Italian Case ◽  
Sight Distance ◽  
Safety Performance Functions ◽  
Urban Safety

Abstract Background Urban safety performance functions are used to predict crash frequencies, mostly based on Negative Binomial (NB) count models. They could be differentiated for considering homogeneous subsets of segments/intersections and different predictors. Materials and methods The main research questions concerned: a) finding the best possible subsets for segments and intersections for safety modelling, by discussing the related problems and inquiring into the variability of predictors within the subsets; b) comparing the modelling results with the existing literature to highlight common trends and/or main differences; c) assessing the importance of additional crash predictors, besides traditional variables. In the context of a National research project, traffic volumes, geometric, control and additional variables were collected for road segments and intersections in the City of Bari, Italy, with 1500 fatal+injury related crashes (2012–2016). Six NB models were developed for: one/two-way homogeneous segments, three/four-legged, signalized/unsignalized intersections. Results Crash predictors greatly vary within the different subsets considered. The effect of vertical signs on minor roads/driveways, critical sight distance, cycle crossings, pavement/markings maintenance was specifically discussed. Some common trends but also differences in both types and effect of crash predictors were found by comparing results with literature. Conclusion The disaggregation of urban crash prediction models by considering different subsets of segments and intersections helps in revealing the specific influence of some predictors. Local characteristics may influence the relationships between well-established crash predictors and crash frequencies. A significant part of the urban crash frequency variability remains unexplained, thus encouraging research on this topic.

Understanding Gap Acceptance Behavior at Unsignalized Intersections using Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110071
Author(s):  
Yingfeng (Eric) Li ◽  
Haiyan Hao ◽  
Ronald B. Gibbons ◽  
Alejandra Medina
Keyword(s):  
Road Traffic ◽  
Wait Time ◽  
Study Data ◽  
Gap Acceptance ◽  
Factors Affecting ◽  
Unsignalized Intersections ◽  
Research Activities ◽  
Stop Sign ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Even though drivers disregarding a stop sign is widely considered a major contributing factor for crashes at unsignalized intersections, an equally important problem that leads to severe crashes at such locations is misjudgment of gaps. This paper presents the results of an effort to fully understand gap acceptance behavior at unsignalized intersections using SHPR2 Naturalistic Driving Study data. The paper focuses on the findings of two research activities: the identification of critical gaps for common traffic/roadway scenarios at unsignalized intersections, and the investigation of significant factors affecting driver gap acceptance behaviors at such intersections. The study used multiple statistical and machine learning methods, allowing a comprehensive understanding of gap acceptance behavior while demonstrating the advantages of each method. Overall, the study showed an average critical gap of 5.25 s for right-turn and 6.19 s for left-turn movements. Although a variety of factors affected gap acceptance behaviors, gap size, wait time, major-road traffic volume, and how frequently the driver drives annually were examples of the most significant.

Factors affecting safety performance on construction sites

1999 ◽  
Vol 17 (5) ◽  
pp. 309-315 ◽  
Author(s):  
Edwin Sawacha ◽  
Shamil Naoum ◽  
Daniel Fong
Keyword(s):  
Safety Performance ◽  
Construction Sites ◽  
Factors Affecting

Differences in the Performance of Safety Performance Functions Estimated for Total Crash Count and for Crash Count by Crash Type

2009 ◽  
Vol 2102 (1) ◽  
pp. 115-123 ◽  
Author(s):  
Thomas Jonsson ◽  
Craig Lyon ◽  
John N. Ivan ◽  
Simon P. Washington ◽  
Ida van Schalkwyk ◽  
...  
Keyword(s):  
Safety Performance ◽  
Safety Performance Functions

Measuring the Causes of Saudi Arabian Construction Accidents: Management and Concerns

2020 ◽  
Vol 10 (2) ◽  
pp. 108-14
Author(s):  
Majed M Moosa ◽  
Leo P. Oriet ◽  
Abdulrahman M Khamaj
Keyword(s):  
Saudi Arabia ◽  
Saudi Arabian ◽  
Safety Performance ◽  
Construction Sites ◽  
New Employees ◽  
Factors Affecting ◽  
Safety Issues ◽  
Construction Companies ◽  
Level Data ◽  
Construction Accidents

Introduction: Research indicate that construction site accidents are a global concern, and rates are rapidly increasing. In developing countries such as Saudi Arabia, safety issues are frequently ignored, and little is known about their causes. Objectives: This study aimed to shed light on factors causing accidents in Saudi Arabian construction companies. Methods: An online detailed survey, using Google Form, of accident features was distributed randomly to potential employees in 35 construction companies in Saudi Arabia, where one of the top administrators or safety officers were required to respond to the survey. It was conducted from 1st June to 31st August, 2013. The safety practices and perceptions of accident causes were assessed. Results: The response rate was 63%. Over half of the surveyed organizations encountered all of the selected accident types. While 19 (86%) of the construction companies maintained the equipment regularly, 15 (68%) had regular maintenance staff and 13 (59%) inspected the equipment before use. Although 18 (82%) of the workers were supplied with personal protective equipment (PPE), only 12 (55%) emphasized its use and offered site orientation for new employees.  In the last part of the survey, respondents were requested to rate 25 factors affecting safety performance at the construction sites on a scale of 1 to 5, with 5 being the most important. The three most important factors of poor safety performance were the firm's top leaders, a lack of training, and the reckless operation of equipment. Conclusion: Changing attitudes of surrounding safety culture have the potential to significantly improve safety outcomes in the Saudi Arabian construction industry. Two Saudi Arabian corporations, Saudi Aramco and Saudi Chevron Petrochemical provide a positive model for increasing construction safety in the country, but there is a paucity of industry-level data. Further scholarly attention is strongly indicated.

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