Development of crash modification factors for changing lane width on roadway segments using generalized nonlinear models

This study evaluates the effectiveness of changing lane width in reducing crashes on roadway segments. To consider nonlinear relationships between crash rate and lane width, the study develops generalized nonlinear models (GNMs) using 3-years crash records and road geometry data collected for all roadway segments in Florida. The study also estimates various crash modification factors (CMFs) for different ranges of lane width based on the results of the GNMs. It was found that the crash rate was highest for 12-ft lane and lower for the lane width less than or greater than 12 ft. GNMs can extrapolate this nonlinear continuous effect of lane width and estimate the CMFs for any lane width, not only selected lane widths, unlike generalized linear models (GLMs) with categorical variables. The CMFs estimated using GNMs reflect that crashes are less likely to occur for narrower lanes if the lane width is less than 12 ft whereas crashes are less likely to occur for wider lanes if the lane width is greater than 12 ft. However, these effects varied with the posted speed limits as the effect of interaction between lane width and speed limit was significant. The estimated CMFs show that crashes are less likely to occur for lane widths less than 12 ft than the lane widths greater than 12 ft if the speed limit is higher than or equal to 40 mph. It was also found from the CMFs that crashes at higher severity levels (KABC and KAB) are less likely to occur for lane widths greater or less than 12 ft compared to 12-ft lane. The study demonstrates that the CMFs estimated using GNMs clearly reflect variations in crashes with lane width, which cannot be captured by the CMFs estimated using GLMs. Thus, it is recommended that if the relationship between crash rate and lane width is nonlinear, the CMFs are estimated using GNMs.     

Finite mixture modeling approach for developing crash modification factors in highway safety analysis

This study aimed to investigate the relative performance of two models (negative binomial (NB) model and two-component finite mixture of negative binomial models (FMNB-2)) in terms of developing crash modification factors (CMFs). Crash data on rural multilane divided highways in California and Texas were modeled with the two models, and crash modification functions (CMFunctions) were derived. The resultant CMFunction estimated from the FMNB-2 model showed several good properties over that from the NB model. First, the safety effect of a covariate was better reflected by the CMFunction developed using the FMNB-2 model, since the model takes into account the differential responsiveness of crash frequency to the covariate. Second, the CMFunction derived from the FMNB-2 model is able to capture nonlinear relationships between covariate and safety. Finally, following the same concept as those for NB models, the combined CMFs of multiple treatments were estimated using the FMNB-2 model. The results indicated that they are not the simple multiplicative of single ones (i.e., their safety effects are not independent under FMNB-2 models). Adjustment Factors (AFs) were then developed. It is revealed that current Highway Safety Manual’s method could over- or under-estimate the combined CMFs under particular combination of covariates. Safety analysts are encouraged to consider using the FMNB-2 models for developing CMFs and AFs.     

Development of adjustment functions to assess combined safety effects of multiple treatments on rural two-lane roadways

Numerous studies have attempted to evaluate the safety effectiveness of specific single treatment on roadways by estimating crash modification factors (CMFs). However, there is a need to also assess safety effects of multiple treatments since multiple treatments are usually simultaneously applied to roadways. Due to the lack of sufficient CMFs of multiple treatments, the Highway Safety Manual (HSM) provides combining method for multiple CMFs. However, it is cautioned in the HSM and related sources that combined safety effect of multiple CMFs may be over or under estimated. Moreover, the literature did not evaluate the accuracy of the combining method using CMFs obtained from the same study area. Thus, the main objectives of this research are: (1) to estimate CMFs and crash modification functions (CM Functions) for two single treatments (shoulder rumble strips, widening (1–9 ft) shoulder width) and combination (installing shoulder rumble strips + widening shoulder width) using the observational before–after with empirical Bayes (EB) method and (2) to develop adjustment factors and functions to assess combined safety effects of multiple treatments based on the accuracy of the combined CMFs for multiple treatments estimated by the existing combining method. Data was collected for rural two-lane roadways in Florida and Florida-specific safety performance functions (SPFs) were estimated for different crash types and severities. The CM Functions and adjustment functions were developed using linear and nonlinear regression models. The results of before–after with EB method show that the two single treatments and combination are effective in reducing total and SVROR (single vehicle run-off roadway) crashes. The results indicate that the treatments were more safety effective for the roadway segments with narrower original shoulder width in the before period. It was found that although the CMFs for multiple treatments (i.e., combination of two single treatments) were generally lower than CMFs for single treatments, they were getting similar to the roadway segments with wider shoulder width. The findings indicate that the combined safety effects of multiple treatments using HSM combining method are mostly over-estimated and the accuracy of HSM combining method vary based on crash types and severity levels. Therefore, it is recommended to develop and apply the adjustment factors and functions to predict the safety effects of multiple treatments when the HSM combining method is used.     

Using multivariate adaptive regression splines (MARS) to develop crash modification factors for urban freeway interchange influence areas

Crash modification factors (CMFs) are used to measure the safety impacts of changes in specific geometric characteristics. Their development has gained much interest following the adoption of CMFs by the recently released Highway Safety Manual (HSM) and SafetyAnalyst tool in the United States. This paper describes a study to develop CMFs for interchange influence areas on urban freeways in the state of Florida. Despite the very different traffic and geometric conditions that exist in interchange influence areas, most previous studies have not separated them from the rest of the freeway system in their analyses. In this study, a promising data mining method known as multivariate adaptive regression splines (MARS) was applied to develop CMFs for median width and inside and outside shoulder widths for “total” and “fatal and injury” (FI) crashes. In addition, CMFs were also developed for the two most frequent crash types, i.e., rear-end and sideswipe. MARS is characterized by its ability to accommodate the nonlinearity in crash predictors and to allow the impact of more than one geometric variable to be simultaneously considered. The methodology further implements crash predictions from the model to identify changes in geometric design features. Four years of crashes from 2007 to 2010 were used in the analysis and the results showed that MARS's prediction capability and goodness-of-fit statistics outperformed those of the negative binomial model. The influential variables identified included the outside and inside shoulder widths, median width, lane width, traffic volume, and shoulder type. It was deduced that a 2-ft increase in the outside and inside shoulders (from 10 ft to 12 ft) reduces FI crashes by 10% and 33%, respectively. Further, a 42-ft reduction in the median width (from 64 ft to 22 ft) increases the rear-end, total, and FI crashes by 473%, 263%, and 223%, respectively.     

Analyzing angle crashes at unsignalized intersections using machine learning techniques

A recently developed machine learning technique, multivariate adaptive regression splines (MARS), is introduced in this study to predict vehicles’ angle crashes. MARS has a promising prediction power, and does not suffer from interpretation complexity. Negative Binomial (NB) and MARS models were fitted and compared using extensive data collected on unsignalized intersections in Florida. Two models were estimated for angle crash frequency at 3- and 4-legged unsignalized intersections. Treating crash frequency as a continuous response variable for fitting a MARS model was also examined by considering the natural logarithm of the crash frequency. Finally, combining MARS with another machine learning technique (random forest) was explored and discussed. The fitted NB angle crash models showed several significant factors that contribute to angle crash occurrence at unsignalized intersections such as, traffic volume on the major road, the upstream distance to the nearest signalized intersection, the distance between successive unsignalized intersections, median type on the major approach, percentage of trucks on the major approach, size of the intersection and the geographic location within the state. Based on the mean square prediction error (MSPE) assessment criterion, MARS outperformed the corresponding NB models. Also, using MARS for predicting continuous response variables yielded more favorable results than predicting discrete response variables. The generated MARS models showed the most promising results after screening the covariates using random forest. Based on the results of this study, MARS is recommended as an efficient technique for predicting crashes at unsignalized intersections (angle crashes in this study).     

Empirical calibration of a roadside hazardousness index for Spanish two-lane rural roads

Crash records and roadside data from Spanish two-lane rural roads were analyzed to study the effect of roadside configuration on safety. Four indicators were used to characterize the main roadside features that have an influence on the consequences of roadway departures: roadside slope, non-traversable obstacles distance from the roadway edge, safety barrier installation, and alignment. Based on the analysis of the effect of roadside configuration on the frequency and severity of run-off-road injury crashes, a categorical roadside hazardousness scale was defined. Cluster analysis was applied to group the combinations of the four indicators into categories with homogeneous effects on run-off-road injury crashes frequency and severity. As a result a 5-level Roadside Hazardousness Index (RHI) was defined. RHI can be used as reference to normalize the collection of roadside safety related information. The index can also be used as variable for inclusion of roadside condition information in multivariate crash prediction models.     

Developing crash modification functions to assess safety effects of adding bike lanes for urban arterials with different roadway and socio-economic characteristics

Although many researchers have estimated crash modification factors (CMFs) for specific treatments (or countermeasures), there is a lack of studies that explored the heterogeneous effects of roadway characteristics on crash frequency among treated sites. Generally, the CMF estimated by before–after studies represents overall safety effects of the treatment in a fixed value. However, as each treated site has different roadway characteristics, there is a need to assess the variation of CMFs among the treated sites with different roadway characteristics through crash modification functions (CMFunctions). The main objective of this research is to determine relationships between the safety effects of adding a bike lane and the roadway characteristics through (1) evaluation of CMFs for adding a bike lane using observational before–after with empirical Bayes (EB) and cross-sectional methods, and (2) development of simple and full CMFunctions which are describe the CMF in a function of roadway characteristics of the sites. Data was collected for urban arterials in Florida, and the Florida-specific full SPFs were developed. Moreover, socio-economic parameters were collected and included in CMFunctions and SPFs (1) to capture the effects of the variables that represent volume of bicyclists and (2) to identify general relationship between the CMFs and these characteristics. In order to achieve better performance of CMFunctions, data mining techniques were used.     

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

Across the nation, researchers and transportation engineers are developing safety performance functions (SPFs) to predict crash rates and develop crash modification factors to improve traffic safety at roadway segments and intersections. Generalized linear models (GLMs), such as Poisson or negative binomial regression, are most commonly used to develop SPFs with annual average daily traffic as the primary roadway characteristic to predict crashes. However, while more complex to interpret, data mining models such as boosted regression trees have improved upon GLMs crash prediction performance due to their ability to handle more data characteristics, accommodate non-linearities, and include interaction effects between the characteristics.An intersection data inventory of 36 safety relevant parameters for three- and four-legged non-signalized intersections along state routes in Alabama was used to study the importance of intersection characteristics on crash rate and the interaction effects between key characteristics. Four different SPFs were investigated and compared: Poisson regression, negative binomial regression, regularized generalized linear model, and boosted regression trees. The models did not agree on which intersection characteristics were most related to the crash rate. The boosted regression tree model significantly outperformed the other models and identified several intersection characteristics as having strong interaction effects.     

Landscaping of highway medians and roadway safety at unsignalized intersections

Well-planted and maintained landscaping can help reduce driving stress, provide better visual quality, and decrease over speeding, thus improving roadway safety. Florida Department of Transportation (FDOT) Standard Index (SI-546) is one of the more demanding standards in the U.S. for landscaping design criteria at highway medians near intersections. The purposes of this study were to (1) empirically evaluate the safety results of SI-546 at unsignalized intersections and (2) quantify the impacts of geometrics, traffic, and landscaping design features on total crashes and injury plus fatal crashes. The studied unsignalized intersections were divided into (1) those without median trees near intersections, (2) those with median trees near intersections that were compliant with SI-546, and (3) those with median trees near intersections that were non-compliant with SI-546. A total of 72 intersections were selected, for which five-year crash data from 2006–2010 were collected.The sites that were compliant with SI-546 showed the best safety performance in terms of the lowest crash counts and crash rates. Four crash predictive models—two for total crashes and two for injury crashes—were developed. The results indicated that improperly planted and maintained median trees near highway intersections can increase the total number of crashes and injury plus fatal crashes at a 90% confidence level; no significant difference could be found in crash rates between sites that were compliant with SI-546 and sites without trees. All other conditions remaining the same, an intersection with trees that was not compliant with SI-546 had 63% more crashes and almost doubled injury plus fatal crashes than those at intersections without trees. The study indicates that appropriate landscaping in highway medians near intersections can be an engineering technology that not only improves roadway environmental quality but also maintains intersection safety.     

Exploration and comparison of crash modification factors for multiple treatments on rural multilane roadways

As multiple treatments (or countermeasures) are simultaneously applied to roadways, there is a need to assess their combined safety effects. Due to a lack of empirical crash modification factors (CMFs) for multiple treatments, the Highway Safety Manual (HSM) and other related studies developed various methods of combining multiple CMFs for single treatments. However, the literature did not evaluate the accuracy of these methods using CMFs obtained from the same study area. Thus, the main objectives of this research are: (1) develop CMFs for two single treatments (shoulder rumble strips, widening shoulder width) and one combined treatment (shoulder rumble strips + widening shoulder width) using before–after and cross-sectional methods and (2) evaluate the accuracy of the combined CMFs for multiple treatments estimated by the existing methods based on actual evaluated combined CMFs. Data was collected for rural multi-lane highways in Florida and four safety performance functions (SPFs) were estimated using 360 reference sites for two crash types (All crashes and Single Vehicle Run-off Roadway (SVROR) crashes) and two severity levels (all severity (KABCO) and injury (KABC)).     

Assessment of safety effects for widening urban roadways in developing crash modification functions using nonlinearizing link functions

Since a crash modification factor (CMF) represents the overall safety performance of specific treatments in a single fixed value, there is a need to explore the variation of CMFs with different roadway characteristics among treated sites over time. Therefore, in this study, we (1) evaluate the safety performance of a sample of urban four-lane roadway segments that have been widened with one through lane in each direction and (2) determine the relationship between the safety effects and different roadway characteristics over time. Observational before–after analysis with the empirical Bayes (EB) method was assessed in this study to evaluate the safety effects of widening urban four-lane roadways to six-lanes. Moreover, the nonlinearizing link functions were utilized to achieve better performance of crash modification functions (CMFunctions). The CMFunctions were developed using a Bayesian regression method including the estimated nonlinearizing link function to incorporate the changes in safety effects of the treatment over time. Data was collected for urban arterials in Florida, and the Florida-specific full SPFs were developed and used for EB estimation.     

Identify sequence of events likely to result in severe crash outcomes

The current practice of crash characterization in highway engineering reduces multiple dimensions of crash contributing factors and their relative sequential connections, crash sequences, into broad definitions, resulting in crash categories such as head-on, sideswipe, rear-end, angle, and fixed-object. As a result, crashes that are classified in the same category may contain many different crash sequences. This makes it difficult to develop effective countermeasures because these crash categorizations are based on the outcomes rather than the preceding events. Consequently, the efficacy of a countermeasure designed for a specific type of crash may not be appropriate due to different pre-crash sequences. This research seeks to explore the use of event sequence to characterize crashes. Additionally, this research seeks to identify crash sequences that are likely to result in severe crash outcomes so that researchers can develop effective countermeasures to reduce severe crashes. This study utilizes the sequence of events from roadway departure crashes in the Fatality Analysis Reporting System (FARS), and converts the information to form a new categorization called “crash sequences.” The similarity distance between each pair of crash sequences were calculated using the Optimal Matching approach. Cluster analysis was applied to group crash sequences that are etiologically similar in terms of the similarity distance. A hybrid model was constructed to mitigate the potential sample selection bias of FARS data, which is biased toward more severe crashes. The major findings include: (1) in terms of a roadway departure crash, the crash sequences that are most likely to result in high crash severity include a vehicle that first crosses the median or centerline, runs-off-road on the left, and then collides with a roadside fixed-object; (2) seat-belt and airbag usage reduces the probability of dying in a roadway departure crash by 90%; and (3) occupants who are seated on the side of the vehicle that experience a direct impact are 2.6 times more likely to die in a roadway departure crash than those not seated on the same side of the vehicle where the impact occurs.     

Modeling crash spatial heterogeneity: Random parameter versus geographically weighting

The widely adopted techniques for regional crash modeling include the negative binomial model (NB) and Bayesian negative binomial model with conditional autoregressive prior (CAR). The outputs from both models consist of a set of fixed global parameter estimates. However, the impacts of predicting variables on crash counts might not be stationary over space. This study intended to quantitatively investigate this spatial heterogeneity in regional safety modeling using two advanced approaches, i.e., random parameter negative binomial model (RPNB) and semi-parametric geographically weighted Poisson regression model (S-GWPR).     

Predictors of safety belt use among crash-involved drivers and front seat passengers: adjusting for over-reporting

Police-reported crash data are rarely used to investigate safety belt use and its predictors, even though these data have a number of advantages over data collected in roadside surveys. It has been widely recognized that motorists tend to over-report their safety belt use to police when mandatory belt use becomes law. In this paper, we use a logistic regression model that allows for misclassification errors in outcome variable to examine predictors of safety belt use among crash-involved drivers and front seat passengers. Our analysis shows significant associations between occupant characteristics, driving circumstances, and safety belt use. Alcohol involvement has the strongest negative association with safety belt use, but this association would be considerably underestimated without adjusting for the over-reporting of safety belt use in police-reported crash data. The adjusted belt use rate among front seat occupants with at least nonincapacitating injuries is about 81%, compared to 90% in police-reported crash data.     

Fatality risk in motorcycle collisions with roadside objects in the United States

Motorcycle crashes with roadside objects often involve more than one impact event: typically involving a collision with the ground and another object. The objective of this study was to determine the fatality risk in these roadside object collisions when compared with crashes only involving a collision with the ground. The roadside objects analyzed included guardrails, concrete barriers, signs, utility poles, and trees. The Fatality Analysis Reporting System (FARS) database was used in conjunction with the General Estimates System (GES) to analyze fatality risk for motorcycle crashes from 2004 to 2008. The analysis was based upon over 3600 fatal motorcycle crashes with roadside objects. Collisions with roadside objects were found to have a higher fatality risk than collisions with either the ground or another motor vehicle. Based on the most harmful event reported in the crash, motorcycle collisions with guardrail were 7 times more likely to be fatal than collisions with the ground, and collisions with trees were almost 15 times more likely to be fatal than collisions with the ground. Additionally, the roadside object was reported as the most harmful event in the majority of the crashes in fatal two-event crashes involving a roadside object and a collision with the ground, with the exception of collisions with signage. From these analyses it was concluded that collisions with fixed objects are more harmful to motorcyclists than collisions with the ground.     

Exploring the transferability of safety performance functions

Safety performance functions (SPFs), by predicting the number of crashes on roadway facilities, have been a vital tool in the highway safety area. The SPFs are typically applied for identifying hot spots in network screening and evaluating the effectiveness of road safety countermeasures. The Highway Safety Manual (HSM) provides a series of SPFs for several crash types by various roadway facilities. The SPFs, provided in the HSM, were developed using data from multiple states. In regions without local jurisdiction based SPFs it is common practice to adopt national SPFs for crash prediction. There has been little research to examine the viability of such national level models for local jurisdictions. Towards understanding the influence of SPF transferability, we examine the rural divided multilane highway models from Florida, Ohio, and California. Traffic, roadway geometry and crash data from the three states are employed to estimate single-state SPFs, two-state SPFs and three-state SPFs. The SPFs are estimated using the negative binomial model formulation for several crash types and severities. To evaluate transferability of models, we estimate a transfer index that allows us to understand which models transfer adequately to other regions. The results indicate that models from Florida and California seem to be more transferable compared to models from Ohio. More importantly, we observe that the transfer index increases when we used pooled data (from two or three states). Finally, to assist in model transferability, we propose a Modified Empirical Bayes (MEB) measure that provides segment specific calibration factors for transferring SPFs to local jurisdictions. The proposed measure is shown to outperform the HSM calibration factor for transferring SPFs.     

Methodology to develop crash modification functions for road safety treatments with fully specified and hierarchical models

Crash modification factors (CMFs) for road safety treatments are developed as multiplicative factors that are used to reflect the expected changes in safety performance associated with changes in highway design and/or the traffic control features. However, current CMFs have methodological drawbacks. For example, variability with application circumstance is not well understood, and, as important, correlation is not addressed when several CMFs are applied multiplicatively. These issues can be addressed by developing safety performance functions (SPFs) with components of crash modification functions (CM-Functions), an approach that includes all CMF related variables, along with others, while capturing quantitative and other effects of factors and accounting for cross-factor correlations. CM-Functions can capture the safety impact of factors through a continuous and quantitative approach, avoiding the problematic categorical analysis that is often used to capture CMF variability. There are two formulations to develop such SPFs with CM-Function components – fully specified models and hierarchical models. Based on sample datasets from two Canadian cities, both approaches are investigated in this paper. While both model formulations yielded promising results and reasonable CM-Functions, the hierarchical model was found to be more suitable in retaining homogeneity of first-level SPFs, while addressing CM-Functions in sub-level modeling. In addition, hierarchical models better capture the correlations between different impact factors.     

A study of the interaction between a guardrail post and soil during quasi-static and dynamic loading

A roadside guardrail system is anchored in gravel beside a roadway to eliminate the risk of fatal accidents during off-road crashes and collisions with hazardous roadside objects. The desired safety behaviour is ensured not only by the guardrail structure itself, but also by the interaction between the gravel and the guardrail post. The interaction of gravel with a Sigma-post of a standard Swedish guardrail was studied in experiments and numerical analysis. The aim was to measure the strength of the single post embedded in gravel and use the data to validate a computer model for the investigation of the soil–post interaction. A quasi-static and dynamic test series were designed and carried out. Two corridors were formed by the test data for the quasi-static and dynamic loading conditions, respectively. A parametric study was subsequently conducted to investigate the influence of the gravel stiffness on the soil–post interaction through computer simulations using LS-DYNA. The numerical results showed that the LS-DYNA soil and concrete model and the Cowper–Symonds steel model effectively captured the soil–post interaction since the calculated strength of the post agreed with the corridors of the test data. The input parameters for the soil and concrete material model were recommended for roadside gravel in crash analyses.     

Spatial analysis of fatal and injury crashes in Pennsylvania

Using injury and fatal crash data for Pennsylvania for 1996-2000, full Bayes (FB) hierarchical models (with spatial and temporal effects and space-time interactions) are compared to traditional negative binomial (NB) estimates of annual county-level crash frequency. Covariates include socio-demographics, weather conditions, transportation infrastructure and amount of travel. FB hierarchical models are generally consistent with the NB estimates. Counties with a higher percentage of the population under poverty level, higher percentage of their population in age groups 0-14, 15-24, and over 64 and those with increased road mileage and road density have significantly increased crash risk. Total precipitation is significant and positive in the NB models, but not significant with FB. Spatial correlation, time trend, and space-time interactions are significant in the FB injury crash models. County-level FB models reveal the existence of spatial correlation in crash data and provide a mechanism to quantify, and reduce the effect of, this correlation. Addressing spatial correlation is likely to be even more important in road segment and intersection-level crash models, where spatial correlation is likely to be even more pronounced.