Development Of Safety Performance Functions Research Articles

Calibration and development of safety performance functions for two-way stop-control intersections on rural two-lane highways in Louisiana

The first edition of the Highway Safety Manual (HSM) contains a simplistic version of the crash prediction model for two-way stop-controlled intersections (TWSC) on rural two-lane highways. This model considers AADT on major and minor roads, with the base conditions defined as no intersection skewness, no turning lanes, and no lighting. A crash modification factor (CMF) will be applied if an intersection has conditions different from the base condition. However, the HSM model does not take account of curvature. It is well known that curved TWSC intersections are less safe than non-curved ones, particularly on rural two-lane roadways. This paper presents the development of crash prediction models incorporating intersection geometrics for TWSC intersections on rural two-lane highways in Louisiana. Then, it compares the results from the developed model with the calibrated HSM model. The negative binomial model was used with 5126 TWSC intersections verified one by one, including both three- and four-leg intersections from all parishes (counties). The estimation results indicate that AADT, curve radius, and intersection skewness angle significantly impact expected crash frequency for both three- and four-leg intersections. This research utilizes cumulative residual plots, mean absolute error, and root mean square error for comparative analysis of HSM models, HSM models with calibration, and Louisiana-specific models. The results show that Louisiana-specific SPFs outperformed the calibrated SPFs with superior reliability. Calibration factors of 0.58 for three-leg intersections and 0.46 for four-leg intersections are estimated, suggesting that the original HSM model overpredicts crashes in Louisiana.

Crash analysis and development of safety performance functions for Florida roads in the framework of the context classification system

Introduction: Safety performance functions (SPF) are employed to predict crash counts at the different roadway elements. Several SPFs were developed for the various roadway elements based on different classifications such as functional classification and area type. Since a more detailed classification of roadway elements leads to more accurate crash predictions, multiple states have developed new classification systems to classify roads based on a comprehensive classification. In Florida, the new roadway context classification system incorporates geographic, demographic, and road characteristics information. Method: In this study, SPFs were developed in the framework of the FDOT roadway context classification system at three levels of modeling, context classification (CC-SPFs), area type (AT-SPFs), and statewide (SW-SPF) levels. Crash and traffic data from 2015-2019 were obtained. Road characteristics and road environment information have also been gathered along Florida roads for the SPF development. Results: The developed SPFs showed that there are several variables that influence the frequency of crashes, such as annual average daily traffic (AADT), signalized intersections and access point densities, speed limit, and shoulder width. However, there are other variables that did not have an influence in crash occurrence such as concrete surface and the presence of bicycle slots. CC-SPFs had the best performance among others. Moreover, network screening to determine the most problematic road segments has been accomplished. The results of the network screening indicated that the most problematic roads in Florida are the suburban commercial and the urban general roads. Practical Applications: This research provides a solid reference for decision-makers regarding crash prediction and safety improvement along Florida roads.

Cable Median Barrier Effect on Commercial Vehicle Crossover Crashes

In the United States (U.S.) the annual number of commercial motor vehicle (CMV) crashes has been on an upward trajectory since 2009. In 2016, CMV crashes accounted for 11.8% of all fatal crashes in the U.S., and in Kentucky, between 2009 and 2016, the number of CMV crashes rose 27%. Of particular concern to state departments of transportation have been crossover crashes involving CMVs. These occur when a vehicle leaves its intended path and veers into the path of oncoming traffic, typically resulting in head-on or sideswipe opposite direction collisions. While some researchers have found that installing cable median barriers can mitigate crossover crashes involving CMVs, no definitive conclusions have been reached. To move toward a resolution of this question, this study leveraged analysis by a panel of experts and the development of safety performance functions and crash modification factors to gauge how cable median barriers can influence the number and severity of crossover CMV crashes on Kentucky interstate routes. Expert panelists contended that cable median barriers will improve safety, a conclusion substantiated by statistical modeling. Despite the study’s limited scope, it appears that installing cable median barriers can prevent or mitigate CMV crashes.

Segmentation effect on developing safety performance functions for rural arterial roads in Egypt

This paper presents the development of safety performance functions (SPFs) for major rural arterial roads in Egypt. Four segmentation methods were used for the SPFs development: (1) constant section length of one kilometer (S1), because crash data are reported for every kilometer; (2) homogenous sections (S2), following the highway safety manual (HSM) recommendation; (3) variable sections with respect to the presence of curvatures (S3); and (4) variable sections with respect to the presence of both curvatures and U-turns (S4). The generalized linear modeling technique was used for SPFs development using the stepwise procedure, with/without considering time effect (i.e. year-to-year variation). The Akaike information criterion, Pearson product-moment correlation coefficient, and mean prediction bias along with the cumulative residual (CURE) plots are used to evaluate the prediction accuracy of the proposed models. The segmentation method was found to affect the prediction accuracy of the calibrated SPFs. For total crashes, the calibrated SPF model based on the S3 segmentation method with the time-trend model form is superior to SPFs calibrated based on other segmentation methods. While S1 and S4 segmentation methods without the time-trend model form produced the best SPFs for “fatal and injury” crashes and property damage only crashes, respectively. In addition, the results showed that by increasing each of the pavement, shoulder, and median widths, the probability of crash occurrence is likely to decrease. In addition, the presences of either horizontal curves and/or accesses are most likely to reduce the probability of crash occurrence.

A conceptual framework for development of safety performance functions for rural roads in Indonesia

Road safety policies provide important guidance for actions to enhance road safety, but often absence an extensive conceptual framework, system theory, and evidence-based study and practice. One effort to enhance road safety is to decrease the number of road accidents. By knowing the accident data at a location, transportation planners can conduct analysis so that they can provide recommendations for improving road safety. However, the prediction model in Indonesia in some studies is more local purposes. Meanwhile, the prediction model used in various countries needs to be adapted to the conditions of Indonesia. The objective of this paper is to integrate all components that contribute to accidents. The work identifies tools that are most and least widely used for components. This framework also provides guidance for developing safety performance functions, what the model is like, what the variables are and what conditions need to be considered according to Indonesian conditions. It is expected that the developed framework can be applied to improve the development of safety performance functions for rural roads in Indonesia.

Development of Safety Performance Functions for Restricted Crossing U-Turn Intersections

AbstractConventional intersection designs are known to be problematic and unreliable when handling the complexity associated with the heavy traffic volume and travel demand on today’s roadways. Alt...

Development of Safety Performance Functions and Level of Service of Safety on National Roads Using Traffic Big Data

Development of Safety Performance Functions and Level of Service of Safety on National Roads Using Traffic Big Data

Examining the Safety Performance and Injury Severity Characteristics of Rural County Roadways

Much of the earlier work on rural safety focused on state-maintained roadways and little is known about the safety performance of low-volume county-maintained roads. This study involved the estimation of safety performance for rural county roadways (paved and gravel). This was accomplished through the development of safety performance functions (SPFs) to estimate the number of annual crashes at a given highway segment, crash modification factors to determine the impacts associated with various roadway and geometric characteristics, and severity distribution functions (SDFs) to predict the crash severity. County road segment data were collected across a sample of 30 counties representing all regions of Michigan. Because of the overwhelming proportion of deer crashes, only non-deer-related crashes were considered. To minimize the influence of variability among counties, the random effect negative binomial model was used to develop SPFs. In addition, a multinomial logit model was used to develop SDFs. Paved county roadways showed approximately double the crash occurrence rate of typical state-maintained two-lane rural highways, and gravel roadways showed a substantially greater crash occurrence rate than paved county roadways across the equivalent range of traffic volumes. The economic analysis showed that it is beneficial to pave a gravel road when the traffic volume is greater than 600 vehicles per day. The random effect variable is significant in all the calibrated models, which shows that there is a considerable variability among counties that cannot be captured with the available variables. Not considering the random effects will result in biased estimation of crashes.

Freeway Truck Traffic Safety in Wyoming: Crash Characteristics and Prediction Models

The State of Wyoming experiences a high percentage of truck traffic along all its highways, especially Interstate 80 (I-80). The increased interactions between trucks and other vehicles have raised many operational and safety concerns. This paper presents a safety analysis and a development of safety performance functions (SPFs) along I-80, with a focus on truck crashes. Nine years of historical crash data in Wyoming (2008–2016) were used to observe the involvement of light, medium, and heavy trucks in crashes. Analysis of the major contributory factors showed that 54% of the total truck-related crashes occurred during icy road conditions and about 46% during snowy weather conditions, and approximately 45% involved driving too fast and driving in improper lane. The analysis also included segments with horizontal curves and vertical grades and their impacts on truck crashes. The crash rate analysis showed higher truck crash rate compared with total crash rate considering equal vehicle miles traveled as exposure. A zero-inflated negative binomial model was applied to develop Wyoming-specific SPFs for various truck crash types. The effects of traffic, road geometry characteristics, and weather parameters influencing different truck-related crashes were quantified from these models. Downgrades and steep upgrade sections were found to increase truck-related crashes. The number of rainy days per year was found to be a significant variable affecting truck-related crashes. On the other hand, the presence of climbing lanes has significant safety benefits.

Safety Performance Functions for Low-Volume Rural Stop-Controlled Intersections

This study involved the development of safety performance functions for rural, low-volume, minor road stop-controlled intersections in Michigan. Facility types included three-leg stop-controlled (3ST) and four-leg stop-controlled (4ST) intersections under state or county jurisdiction and were sampled from each of Michigan’s 83 counties. To isolate lower-volume rural intersections, major roadway traffic volumes were limited to the range of 400–2,000 vehicles per day (vpd). Data were compiled from several sources for 2,023 intersections statewide. These data included traffic crashes, volumes, roadway classification, geometry, cross-sectional features, and other site characteristics covering the period of 2011–2015. Random effects negative binomial regression models were specified for each stop-controlled intersection type considering factors such as driveway density, lighting presence, turn lane presence, and intersection skew, in addition to volume. To account for the unobserved heterogeneity between counties, mixed effects negative binomial models with a county-specific random effect were utilized. Furthermore, unobserved temporal effects were controlled through the use of a year-specific random effect. Separate models were developed for fatal/injury crashes, property damage crashes, and select target crash types. The analysis found that skew angles of greater than five degrees led to significantly greater crash occurrence for both 3ST and 4ST intersections, while greater than two driveways near the intersection led to significantly greater angle crashes at 4ST intersections. Other factors were found to have little impact on crash occurrence. Comparison with the Highway Safety Manual (HSM) base models showed that the HSM models over-predict crashes on 4ST intersections and 3ST intersections with volumes between 1,200 and 2,000 vpd.

Development of safety performance functions (SPFs) to analyse and predict aircraft loss of separation in accordance with the characteristics of the airspace

The occurrence of Losses Of Separation between aircraft in an airspace that could lead to Mid Air Collisions (MAC) is of major concern to Air Traffic Management. This paper uses an innovative approach to develop Safety Performance Functions to predict the occurrence of losses of separation. This will improve the safety conditions under which airlines operate. A data driven approach is used to characterise the Losses Of Separation between aircraft as count data with an excess of zeros and over dispersion. Subsequently, the relationships between the number of aircraft conflicts in a particular route segment and the airspace design and traffic flow characteristics are modelled using Zero-inflated models. Based on the characteristics of the route segment, the distribution that most closely matches observations of the number of conflicts in airspace segments is a Zero-inflated negative binomial probability distribution. It also takes account of the large amount of null values that characterise safety occurrences in aviation.

The Development of Safety Performance Functions for Roundabouts in Amman, Jordan

It is now well established that crash occurrences at roadway segments or intersections are associated with a large variety of factors. Safety Performance Functions (SPF) are statistical models developed to predict crash frequencies for various design variables. In Jordan, almost half of all road crashes occur at intersections, mostly in urban areas. There is a significant number of roundabouts throughout Amman which is increasing without evaluating their safety performance. To assess safety benefits of this kind of intersections, transportation professionals need the powerful statistical tool; the SPF. This study aims to develop SPFs for roundabouts in Amman. The models consider the crash frequency, traffic volume and geometric features of all the studied 20 roundabouts. The developed SPFs were statistically significant (R^2 = 0.91) .The findings of the study revealed that crash frequency has a strong relation with the AADT, roundabout entry angle-degrees, entry path radius, splitter radius, pedestrian crossing structure, inscribed diameter, central diameter, circulating width, entry width, number of circle legs. The developed SPFs are evaluated through a comparison with others from developed countries.

Development of Safety Performance Functions: Incorporating Unobserved Heterogeneity and Functional Form Analysis

To improve transportation safety, this study applies Highway Safety Manual (HSM) procedures to roadways while accounting for unobserved heterogeneity and exploring alternative functional forms for Safety Performance Functions (SPFs). Specifically, several functional forms are considered in Poisson and Poisson-gamma modeling frameworks. Using 5 years (2011–2015) of crash, traffic, and road inventory data for two-way, two-lane roads in Tennessee, fixed- and random-parameter count data models are calibrated. The models account for important methodological concerns of unobserved heterogeneity and omitted variable bias. With a validation dataset, the calibrated and uncalibrated HSM SPFs and eight new Tennessee-specific SPFs are compared for prediction accuracy. The results show that the statewide calibration factor is 2.48, suggesting rural two-lane, two-way road segment crashes are at least 1.48 times greater than what HSM SPF predicts. Significant variation in four different regions in Tennessee is observed with calibration factors ranging between 2.02 and 2.77. Among all the SPFs considered, fully specified Tennessee-specific random parameter Poisson SPF outperformed all competing SPFs in predicting out-of-sample crashes on these road segments. The best-fit random parameter SPF specification for crash frequency includes the following variables: annual average daily traffic, segment length, shoulder width, lane width, speed limit, and the presence of passing lanes. Significant heterogeneity is observed in the effects of traffic exposure-related variables on crash frequency. The study shows how heterogeneity-based models can be specified and used by practitioners for obtaining accurate crash predictions.

Development of safety performance functions and crash modification factors for expressway ramps

The ramp is a typical section of expressway interchanges where design elements such as the grade and curvature complicatedly changes along the ramp. Dynamic changes in the design elements of ramps may seriously influence driving behaviors, resulting in more crashes on ramps than other freeway facilities. Therefore, there is a strong need to improve traffic safety of freeway ramps through in-depth studies regarding the effects of freeway design elements on freeway crashes. In this study, the crash data of 2007–2009 for expressway ramps, data associated with various ramp design elements, and the annual average daily traffic were collected. Using the data, three negative binomial regression models for the Safety Performance Functions (SPFs) were developed for individual ramp types, including direct, semi-direct, and loop types. Based on the SPFs, Crash Modification Factors (CMFs) were developed for selected design elements. For the validation of developed SPFs and CMFs, the frequency of traffic crash estimated using the SPFs and CMFs was compared with the actual frequency of traffic crashes. As a result, traffic crash prediction using the SPFs and CMFs was found out to be robust in general. The results of this study are expected to contribute to improving traffic safety on freeway ramps by quantifying the effects of diverse decision makings regarding ramp design elements on crashes.

Development of safety performance functions for Spanish two-lane rural highways on flat terrain

Over decades safety performance functions (SPF) have been developed as a tool for traffic safety in order to estimate the number of crashes in a specific road section. Despite the steady progression of methodological innovations in the crash analysis field, many fundamental issues have not been completely addressed. For instance: Is it better to use parsimonious or fully specified models? How should the goodness-of-fit of the models be assessed? Is it better to use a general model for the entire sample or specific models based on sample stratifications? This paper investigates the above issues by means of several SPFs developed using negative binomial regression models for two-lane rural highways in Spain. The models were based on crash data gathered over a 5-year period, using a broad number of explanatory variables related to exposure, geometry, design consistency and roadside features. Results show that the principle of parsimony could be too restrictive and that it provided simplistic models. Most previous studies apply conventional measurements (i.e., R2, BIC, AIC, etc.) to assess the goodness-of-fit of models. Seldom do studies apply cumulative residual (CURE) analysis as a tool for model evaluation. This paper shows that CURE plots are essential tools for calibrating SPF, while also providing information for possible sample stratification. Previous authors suggest that sample segmentation increases the model accuracy. The results presented here confirm that finding, and show that the number of significant variables in the final models increases with sample stratification. This paper point out that fully models based on sample segmentation and on CURE may provide more useful insights about traffic crashes than general parsimonious models when developing SPF.

Light Rail Crossing Safety Performance Functions

This paper presents the development of safety performance functions (SPFs) for light rail crossings or roadways. The paper also develops an empirical Bayes method for adjusting the initial crash estimates from the SPF to account for the actual crash experiences at light rail crossings. The validity of the light rail crossing SPFs is compared with that of the U.S. Department of Transportation (DOT) crash prediction models. It is found that SPFs specific to light rail crossings provide improvements to crash estimates that are statistically significant compared with that of the U.S. DOT crash prediction models.

Development of Safety Performance Function Based on Expressway Alignment Homogeneous Section

ABSTRACTIn the past, expressways focused on mobility. However, the paradigm of expressways fuction today has been changed from fast expressways to safe expressways as people’s quality of living and consciousness level heightened. In 2012, 3,550 traffic accidents occurred on expressways and 371 people died. The fatality rate of traffic accidents on expressways is almost twice that on general national roads. This study devel oped accident forecast models (saf ety performance functions) based on the number of traffic acc idents and traffic volumes on six major lines on expressways. It is difficult to forecast safety performance functions for each expres sway line because the lines and the scales of expressways are different from each other; therefore, integrated safety performance functio ns of six lines were determined first, and the coefficients, which can correct the traffic accidents on each line, were calculated. It is believed that this study will contribute in the safer management of expressways by being used as basic information in the establishment of traffic safety strategies for each expressway line in prevention of traffic accidents. Moreover, more studies would be required in the future, which would suggest reliable accident forecasts by calculating correction coefficients by line through integrated models by groups dependent on the characteristics of each line.Key words : Traffic safety, Accident, Safety performance function, Negative binomial regression model, Adjustment factor초 록과거 고속도로는 이동성 중심의 기능을 주로 담당하였으나, 삶의 질 향상과 의식 수준의 향상에 따라 ‘빠른 고속도로’에서 ‘안전한 고속도로’로 고속도로 기능의 패러다임이 변화하고 있다. 고속도로 교통사고는 2012년 기준 3,550건이 발생하였으며, 371명이 교통사고로 사망한 것으로 집계되었고, 치사율은 일반국도의 약 2배에 달하는 것으로 보고되고 있다. 본 연구에서는 고속도로 주요 6개 노선을 대상으로 교통사고건수와 교통량 기반의 사고예측모형(안전성능함수)을 개발하였다. 안전성능함수는 각 노선별로 도로의 선형 및 규모 등이 달라 정확하게 각 노선을 예측하는데 어려움이 있을 것으로 판단되어, 6개 노선의 통합 안전성능함수를 구축하고, 각 노선별로 교통사고를 보정할 수 있는 계수를 산출하였다. 본 연구의 결과는 향후 교통사고 예방을 위한 노선별 교통안전 전략수립의 기초자료로 활용함으로써 보다 안전한 고속도로 관리에 기여할 것으로 판단된다. 향후에는 각 노선별 특성에 따른 그룹별 통합모형을 통한 노선별 보정계수를 산출하여 신뢰성 있는 사고 예측값을 제시하는 연구가 수행되어야 할 것이다.검색어 : 교통안전 , 교통사고, 안전성능함수음이항 회귀모형, 보정계수Transportation Engineering

Modelling highway safety with data from chinese freeways

This paper presents a study on the development of safety performance function for freeways. Applying well-established statistical methods, we evaluated all variables that may affect freeway safety and selected the most signifi cant ones in the model. A variable analysis unit was utilized in this study to overcome the diffi culties in obtaining accurate crash and highway attribute data, as well as to improve the modelling quality. The results of this study provide much needed tools for freeway safety analysis.

Calibration and Development of Safety Performance Functions for Alabama

One critical component of the Highway Safety Manual (HSM) statistical methods is the safety performance function (SPF). SPFs are essentially regression models that correlate quantitatively the expected number of crashes with traffic exposure and geometric characteristics of the road. As part of a project performed by the University of Alabama to facilitate implementation of the new HSM procedures in the state, this study aims to evaluate the applicability of HSM predictive methods to Alabama data and to develop state-specific statistical models for two facility types: two-lane, two-way rural roads and four-lane divided highways. This study first calibrates HSM base SPFs by using two approaches: the method recommended by the HSM and a newly proposed approach that treats the estimation of calibration factors as a special case of a negative binomial regression. In addition, new forms of state-specific SPFs are further investigated by using Poisson-gamma regression techniques. Four new functional forms are studied in this project. The prediction capabilities of the two calibrated models and the four newly developed state-specific SPFs are evaluated with a validation data set. Five performance measures are considered for model evaluation. The study is able to identify a particular state-specific SPF that fits the Alabama data well and outperforms other models, including the calibrated SPFs. The best model describes the mean crash frequency as a function of annual average daily traffic, segment length, lane width, year, and speed limit. The study finds that the HSM-recommended method for calibration factor estimation also performs well.

A method to account for outliers in the development of safety performance functions

Accident data sets can include some unusual data points that are not typical of the rest of the data. The presence of these data points (usually termed outliers) can have a significant impact on the estimates of the parameters of safety performance functions (SPFs). Few studies have considered outliers analysis in the development of SPFs. In these studies, the practice has been to identify and then exclude outliers from further analysis. This paper introduces alternative mixture models based on the multivariate Poisson lognormal (MVPLN) regression. The proposed approach presents outlier resistance modeling techniques that provide robust safety inferences by down-weighting the outlying observations rather than rejecting them. The first proposed model is a scale-mixture model that is obtained by replacing the normal distribution in the Poisson-lognormal hierarchy by the Student t distribution, which has heavier tails. The second model is a two-component mixture (contaminated normal model) where it is assumed that most of the observations come from a basic distribution, whereas the remaining few outliers arise from an alternative distribution that has a larger variance. The results indicate that the estimates of the extra-Poisson variation parameters were considerably smaller under the mixture models leading to higher precision. Also, both mixture models have identified the same set of outliers. In terms of goodness-of-fit, both mixture models have outperformed the MVPLN. The outlier rejecting MVPLN model provided a superior fit in terms of a much smaller DIC and standard deviations for the parameter estimates. However, this approach tends to underestimate uncertainty by producing too small standard deviations for the parameter estimates, which may lead to incorrect conclusions. It is recommended that the proposed outlier resistance modeling techniques be used unless the exclusion of the outlying observations can be justified because of data related reasons (e.g., data collection errors).