Accident Modification Factors Research Articles

Safety assessment of Czech motorways and national roads

PurposeCzech motorways and national roads form the primary road network, which is critical in terms of safety. To be able to rationally manage network safety in both planning and operation stages, quality network-wide data and tools are needed. While such tools already exist in some countries, their transferability is limited. Authors therefore collected data and used it to develop tools, which allowed conducting state-of-the-art road safety impact assessment and network safety ranking in the Czech conditions. In addition to primary road network, focus was widened to include also secondary roads, in order to enable assessment of impacts on adjacent road network.MethodsAccident, road and traffic data was collected, using not only existing databases, but also including own collection of traffic volumes on motorway interchanges. Data was used to develop the tools, based on accident prediction models and accident modification factors.ResultsThe final accident prediction models and accident modification factors enabled conducting road safety impact assessment, for which simple on-line tool was also developed. For network safety ranking, accident prediction models were applied according to the Empirical Bayes method, in order to determine potential for safety improvement of the studied road network elements, with the final priority list visualized in an on-line map. Both outputs are shortly presented in the paper.ConclusionsData and sample size limitations lead to some compromises in modelling, such as using fixed proportions of observed accident severities or omitted variables. Nevertheless, the study established the practical framework for both road safety impact assessment and network safety ranking. It may serve as an example for other member countries, which also lack their local tools. Follow-up studies may focus on future model updating and improvements, as well as development of local accident modification factors.

Assessing Transferability of Highway Safety Manual Crash Prediction Models to Data from Italy

For decades, crashes have been studied as discrete events with the focus on the circumstances of the crash. This type of analysis has been used to identify the characteristics of roadway features associated with higher crash experience, but other factors, such as traffic volumes, driver characteristics, land use, and environmental conditions, are also needed to explain or describe crash events. The Highway Safety Manual (HSM) provides a predictive method to estimate the expected average crash frequency of a site in given geometric and geographic conditions over a specific period for a specific annual average daily traffic volume. The study presented here investigated whether the modeling results closely matched the crash records. The HSM algorithms were used to assess transferability as a whole. The results suggested that implementing the HSM techniques should foster the development of local safety performance functions and accident modification factors. Calibration preserved the original HSM model form and the relationship between independent variables and crashes. To adjust the base predicted crash frequency to meet the current conditions, the accident modification factor calculations for lane width, horizontal curves, and vertical grades were made. Crash types (head-on and side collisions, single-vehicle crashes, and rear-end collisions) were investigated on the basis of the vertical grade and the curvature indicator. The estimated model provides planners and designers with a tool better able to target and select countermeasures to address these specific aspects and results in improved project selection and improved safety.

지방부 도로구간의 사고수정계수 개발에 관한 연구

PURPOSES : Although numerous researches have been studied to reveal accident causations for road intersections, there are still many research gaps for road segments. It is mainly because of difficulty of data and lack of analytical method. This study aims to study accident causations for rural road segments and develop accident modification factors for safety evaluation. The accident modification factors can be used to improve road safety. METHODS : Methods for developing AMF are diverse. This study developed AMFs using accident prediction models and selected explanatory variables from the accident models. In order to select final AMFs, three different methods were applied in the study. RESULTS : As a result of the study, many AMFs such as horizontal curves or vertical curves were developed and explained the meanings of the results. CONCLUSIONS : This study introduced meaningful methods for developing significant AMFs and also showed several AMFs. It is expected that traffic or road engineers will be able to use the AMFs to improve road segment safety.

Determination of accident modification factors for the median bus lanes on urban arterials

This paper presents the results of a study which examined accident modification factors for median bus lanes on urban arterials. Although taken for granted that median bus lanes will boost bus ridership and relieve traffic congestion, the full utilization of this useful design is not undertaken because highway engineers often are concerned with the increase of crashes in median bus lanes. However, the installation of the median bus lanes does not always lead to an increase in crashes and, to demonstrate that this argument is correct, this research proposes the use of accident modification factors. The following tasks were carried out: first, aliterature review of the major geometric elements of median bus lanes was undertaken. Second, both existing crash and geometric information data were gathered. Third, a statistical analysis was made to examine the effects of crash occurrences on major geometric elements. In addition, the crash models resulting from this analysis were subsequently applied in developing accident modification factors. A verification process was also carried out. As a result, it was found thataccident modification factors for median bus lanes could be successfully developed, and that crash occurrences were very sensitive to the widths of the bus lane, the travel lanes and the shoulders, in that order. The findings from this research should be useful in comparing several safety design alternatives in the future.

The Relationship between Freeway Safety and Geometric Design Elements

This paper mainly quantifies the safety effects of ramp spacing, and other geometric design elements of highway facilities. Negative Binomial (NB) regression models have been applied to estimate of different accident types, including total crashes, fatal-plus-injury crashes, single-vehicle crashes and multiple-vehicle crashes. Data for model estimation was collected in 112 freeway segments where each entrance ramp is followed by an exit ramp on I-5 in the Washington State, US. Three years (2005-2007) of freeway crashes has been examined by the NB model. The modeling results have been geared into the development of accident modification factors (AMFs) for ramp spacing that can be used safety prediction of freeways. Furthermore, the results also suggest some geometric designs are recommended to improve freeway safety

Effects of Ramp Spacing on Freeway Mainline Crashes

This research applies Negative Binomial regression models to investigate safety effects of ramp spacing. Data for model estimation was collected in 112 freeway segments where each entrance ramp is followed by an exit ramp. Three years (2005-2007) of freeway crash data were examined by the NB model in this study. The modeling results suggest that the frequencies of total crashes, fatal-plus-injury crashes, single-vehicle crashes and multiple-vehicle crashes increase as ramp spacing decreases, and the frequencies of total crashes and multiple-vehicle crashes increase at significant rates. The modeling result has been geared into the development of accident modification factors (AMFs) for ramp spacing that can be used safety prediction of freeways.

Development of Accident Modification Factors for Rural Frontage Road Segments in Texas Using Generalized Additive Models

The objective of this study consists of assessing the application of generalized additive models (GAMs) for estimating accident modification factors (AMFs). GAMs are a new type of models that have been recently introduced by the statistical community for modeling observed data. These models offer more flexible functional forms than traditional generalized linear models and allow for more adaptable variable interactions. As recently documented in the literature, variable interactions should be included in the development of AMFs. To accomplish the study objective, AMFs were derived from GAMs using data collected on rural frontage roads in Texas. The AMFs were then compared to the AMFs produced from a previous study using the same data set. The results of the study show that AMFs produced from GAMs are more flexible to characterize the safety effect of simultaneous changes in geometric and operational features (or variable interactions) than when independent AMFs are applied together. The results also show that GAMs indicated a nonlinear relationship between crash risk and changes in lane and shoulder widths for frontage roads in Texas.

Safety Impacts of Design Element Trade-Offs for Multilane Rural Highways

Roadway designs intend to provide a safe facility addressing mobility concerns, accommodating the physical and social environment and within financial constraints. Sometimes, trade-offs among these may be needed to deliver the desired project and designers need tools to estimate the safety implications from such decisions. The work completed here aimed to develop a set of recommendations to be used in evaluating safety implications from design element trade-offs. The effort focused on developing crash-prediction models and accident modification factors (AMFs) for multilane rural roads regarding lane width, shoulder width, and median width and type. The available data limited these models to four-lane roadways with 12-ft lanes. Separate models were developed for divided and undivided facilities as well as for single-vehicle, multivehicle, and all crashes involving both total and injury crashes. The AMF values recommended here were compared to those proposed in the Highway Safety Manual. Most of the proposed values are in accordance with previous work and demonstrate the effect of these elements on crash occurrence.

Development of comprehensive accident models for two-lane rural highways using exposure, geometry, consistency and context variables

In Europe, approximately 60% of road accident fatalities occur on two-lane rural roads. Thus, research to develop and enhance explanatory and predictive models for this road type continues to be of interest in mitigating these accidents. To this end, this paper describes a novel and extensive data collection and modeling effort to define accident models for two-lane road sections based on a unique combination of exposure, geometry, consistency and context variables directly related to the safety performance. The first part of the paper documents how these were identified for the segmentation of highways into homogeneous sections. Next, is a description of the extensive data collection effort that utilized differential cinematic GPS surveys to define the horizontal alignment variables, and road safety inspections (RSIs) to quantify the other road characteristics related to safety. The final part of the paper focuses on the calibration of models for estimating the expected number of accidents on homogeneous sections that can be characterized by constant values of the explanatory variables. Several candidate models were considered for calibration using the Generalized Linear Modeling (GLM) approach. After considering the statistical significance of the parameters related to exposure, geometry, consistency and context factors, and goodness of fit statistics, 19 models were ranked and three were selected as the recommended models. The first of the three is a base model, with length and traffic as the only predictor variables; since these variables are the only ones likely to be available network-wide, this base model can be used in an empirical Bayesian calculation to conduct network screening for ranking “sites with promise” of safety improvement. The other two models represent the best statistical fits with different combinations of significant variables related to exposure, geometry, consistency and context factors. These multiple variable models can be used, with caution, and in conjunction with results from other studies, to derive accident modification factors for these variables for design applications, and in safety assessment for smaller samples of sites for which these variables can be assembled with relative ease.

Comparison of Application of Product of Baseline Models and Accident-Modification Factors and Models with Covariates: Predicted Mean Values and Variance

Research was done to compare the application of full models (or models with several covariates) and baseline models, estimated by using data meeting specific nominal conditions combined with accident-modification factors (AMFs) for predicting motor vehicle crashes. The analysis focuses on the predicted values and associated inferences for both types of models. In the past few years, researchers have questioned the approach of multiplying baseline models with AMFs. For comparison, full and baseline models are estimated by using data collected on rural four-lane highways in Texas. AMFs describing the safety effects related to lane and shoulder width as well as the number of horizontal curves per mile are extracted from previous work. Two scenarios describing AMF values and their associated uncertainty are evaluated. The results of the study show that the product of baseline models and AMFs produces a much larger variance, hence a wider 95% predicted confidence interval, than the variance calculated using the full models. This is consistent for the two scenarios evaluated and for all levels of uncertainty associated with the AMFs. The study concludes that the full model should be used instead of the product of the baseline model and AMFs when the study objective includes variance as part of the decision-making process.

Pedestrian Safety Prediction Methodology for Urban Signalized Intersections

A coordinated effort is under way to develop a Highway Safety Manual (HSM) for use in making quantitative estimates of the safety performance of specific highway types and quantitative estimates of proposed improvements to specific highway types. The highway types being addressed in the first edition of HSM are rural two-lane highways, rural multilane highways, and urban and suburban arterials. Explicit consideration of pedestrian safety on urban and suburban arterials is considered critical to implementation of the first edition of HSM. The objective of the present research was to develop a methodology for quantifying the effects of existing site characteristics and proposed improvements on urban and suburban arterials on pedestrian safety. The pedestrian safety prediction methodology has been developed to function as a component of the overall safety prediction methodology for urban and suburban arterials proposed for the first edition of HSM. In the present research, a methodology for prediction of vehicle–pedestrian collisions at signalized intersections was developed. This methodology includes base models for three- and four-leg signalized intersections and several accident modification factors.

Evaluating the Effects of Freeway Design Elements on Safety

Increased emphasis has been placed on improving the explicit role of highway safety in making decisions on highway planning, design, and operations. This end can be achieved by quantifying the safety effects of geometric design elements for various highway facilities. The objective of this study was to investigate the safety effects of two important design elements for freeways: ramp density and horizontal curve. Data available for use in the evaluation included 324.2 centerline mi of freeways collected in Texas. Five years (1997–2001) of freeway crashes were examined. Negative binomial regression models were used to estimate the effects of independent variables on crashes. The final model for evaluation revealed that crashes on freeway segments were associated with average daily traffic, on-ramp density, degree of curvature, median width with inside shoulder, the number of lanes (for urban freeways), and whether the freeway is in an urban or rural area. Off-ramp density was not statistically significant in the model. Furthermore, the effect of on-ramp density on freeway crashes was significant for horizontal curves but not for tangent sections and indicates that freeway designers should eliminate or minimize the number of on-ramps within the horizontal curves. The statistical modeling results were geared into the development of accident modification factors for on-ramp density and horizontal curves that can be used for safety prediction of freeways.

Expected Safety Performance of Rural Signalized Intersections in South Korea

Understanding the expected safety performance of rural signalized intersections is critical for ( a) identifying high-risk sites where the observed safety performance is substantially worse than the expected safety performance, ( b) understanding influential factors associated with crashes, and ( c) predicting the future performance of sites and helping plan safety-enhancing activities. These three critical activities are routinely conducted for safety management and planning purposes in jurisdictions throughout the United States and around the world. This paper aims to develop baseline expected safety performance functions of rural signalized intersections in South Korea, which to date have not yet been established or reported in the literature. Data are examined from numerous locations within South Korea for both three-legged and four-legged configurations. The safety effects of a host of operational and geometric variables on the safety performance of these sites are also examined. In addition, supplementary tables and graphs are developed for comparing the baseline safety performance of sites with various geometric and operational features. These graphs identify how various factors are associated with safety. The expected safety prediction tables offer advantages over regression prediction equations by allowing the safety manager to isolate specific features of the intersections and examine their impact on expected safety. The examination of the expected safety performance tables through illustrated examples highlights the need to correct for regression-to-the-mean effects, emphasizes the negative impacts of multicollinearity, shows why multivariate models do not translate well to accident modification factors, and illuminates the need to examine road safety carefully and methodically. Caveats are provided on the use of the safety performance prediction graphs developed in this paper.

Use of Expert Panels in Highway Safety

Expert panels have been used extensively in the development of the Highway Safety Manual to extract research information from highway safety experts. While the panels have been used to recommend agendas for new and continuing research, their primary role has been to develop accident modification factors–-quantitative relationships between highway safety and various highway safety treatments. Because the expert panels derive quantitative information in a “qualitative” environment and because their findings can have significant impacts on highway safety investment decisions, the expert panel process should be described and critiqued. This paper is the first known written description and critique of the expert panel process and is intended to serve professionals wishing to conduct such panels.

Accident Modification Factors for Medians on Freeways and Multilane Rural Highways in Texas

With the growing demand for safer streets and highways, state and national transportation agencies are investigating the relationships between roadway characteristics and crashes. The objective of this study was to develop accident modification factors (AMFs) for median characteristics on urban and rural freeways and on rural multilane highways. Data available for use in the evaluation included 458 mi of with-barrier segments (primarily urban, with some rural sites), 359 mi of urban without-barrier segments, and 436 mi of rural without-barrier segments. A series of negative binomial regression models was used to determine the effects of independent variables on crashes. Variables considered in developing the base models included average daily traffic, left-shoulder width, barrier offset, median (with shoulder) width, and pole density. Crashes were examined in relation to median crashes for 5 years (1997 to 2001). An AMF represents the change in safety when a particular geometric design element changes in size from one value to another. In this project, the AMFs were estimated directly from the coefficients of the models. This approach for AMF development assumes that (a) each AMF is independent because the model parameters are assumed to be independent, and (b) the change in crash frequency is exponential. AMF equations were developed for urban and rural medians with rigid barriers, urban medians without barriers, and rural medians without barriers.

Procedure for Developing Accident Modification Factors from Cross-Sectional Data

This paper describes a procedure for developing accident modification factors (AMFs) by using a cross-sectional study. It is recognized that AMFs are most accurately derived from controlled experiments and observational before-after studies. However, the execution of experiments and before-after studies is not always practical or feasible. The procedure described in this paper is intended to be used in this situation. The procedure is demonstrated through the development of a curve radius AMF for rural two-lane highways.

Potential Accident Modification Factors for Driveway Density on Rural Highways

Accident modification factors (AMFs) for driveway density can be described as the incremental effects of driveway density on safety. The objective of this study was to develop AMFs for driveways on rural highways in Texas. Available for evaluation were 2,354 mi of rural two-lane highways and 402 centerline miles of rural four-lane highways. From a review of the data, it is recommended that the assumed base condition for driveway density be three driveways per mile. Negative binomial regression was used to determine the effects of independent variables on crashes. Driveway and segment crashes for 3 years (1999 to 2001) were used in the evaluations. AMF equations that consider driveway density for the segment were developed for both rural two-lane and rural four-lane highways.

Methodology for estimating the variance and confidence intervals for the estimate of the product of baseline models and AMFs

This manuscript describes a methodology for estimating the variance and 95% confidence intervals (CI) for the estimate of the product between baseline models and Accident Modification Factors (AMFs). This methodology is provided for the upcoming Highway Safety Manual (HSM) currently in development in the United States (U.S.). The methodology is separated into two parts. The first part covers the proposed approach for estimating the variance of the estimate of the product between baseline models and AMFs. The second part presents the method for estimating the variance of baseline models. Several examples are presented to illustrate the application of the methodology.

Predicting motor vehicle collisions using Bayesian neural network models: An empirical analysis

Statistical models have frequently been used in highway safety studies. They can be utilized for various purposes, including establishing relationships between variables, screening covariates and predicting values. Generalized linear models (GLM) and hierarchical Bayes models (HBM) have been the most common types of model favored by transportation safety analysts. Over the last few years, researchers have proposed the back-propagation neural network (BPNN) model for modeling the phenomenon under study. Compared to GLMs and HBMs, BPNNs have received much less attention in highway safety modeling. The reasons are attributed to the complexity for estimating this kind of model as well as the problem related to “over-fitting” the data. To circumvent the latter problem, some statisticians have proposed the use of Bayesian neural network (BNN) models. These models have been shown to perform better than BPNN models while at the same time reducing the difficulty associated with over-fitting the data. The objective of this study is to evaluate the application of BNN models for predicting motor vehicle crashes. To accomplish this objective, a series of models was estimated using data collected on rural frontage roads in Texas. Three types of models were compared: BPNN, BNN and the negative binomial (NB) regression models. The results of this study show that in general both types of neural network models perform better than the NB regression model in terms of data prediction. Although the BPNN model can occasionally provide better or approximately equivalent prediction performance compared to the BNN model, in most cases its prediction performance is worse than the BNN model. In addition, the data fitting performance of the BPNN model is consistently worse than the BNN model, which suggests that the BNN model has better generalization abilities than the BPNN model and can effectively alleviate the over-fitting problem without significantly compromising the nonlinear approximation ability. The results also show that BNNs could be used for other useful analyses in highway safety, including the development of accident modification factors and for improving the prediction capabilities for evaluating different highway design alternatives.

Optimization of Highway Safety and Operation by Using Crash Prediction Models with Accident Modification Factors

The linear programming model that is presented optimizes the crash and delay costs of highway networks by selecting safety and operational improvements with implementation costs within a constrained budget. The costs that are minimized in this study are those of crashes plus the costs of delay times. This optimization model works with crash prediction models that estimate the expected number of crashes by using base models and accident modification factors (AMFs). In these models the base model predicts the expected number of crashes for a base highway or intersection. The AMFs modify the prediction on the basis of the specific characteristics of each highway segment or intersection. Several improvement alternatives for different highways and intersections within a network would mean many improvement combinations, each with a different cost for implementation, which would cause certain crashes and delay. The proposed model uses linear optimization to find the combination of improvements with the lowest total of crash and delay costs within an acceptable range of implementation costs. The optimization model is presented in conjunction with crash prediction models borrowed from the Interactive Highway Safety Design Model software. The optimization model could be modified to work with other types of crash prediction models. A test case study containing a network of three highways and 13 intersections is presented. The corresponding optimization problem is solved with the CPLEX software. The variations of crash and delay cost savings versus improvement costs are studied, and the results are discussed.