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.     

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.     

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.     

Assessing the safety effects of multiple roadside treatments using parametric and nonparametric approaches

This study evaluates the safety effectiveness of multiple roadside elements on roadway segments by estimating crash modification factors (CMFs) using the cross-sectional method. To consider the nonlinearity in crash predictors, the study develops generalized nonlinear models (GNMs) and multivariate adaptive regression splines (MARS) models. The MARS is one of the promising data mining techniques due to its ability to consider the interaction impact of more than one variables and nonlinearity of predictors simultaneously. The CMFs were developed for four roadside elements (driveway density, poles density, distance to poles, and distance to trees) and combined safety effects of multiple treatments were interpreted by the interaction terms from the MARS models. Five years of crash data from 2008 to 2012 were collected for rural undivided four-lane roadways in Florida for different crash types and severity levels. The results show that the safety effects decrease as density of driveways and roadside poles increase. The estimated CMFs also indicate that increasing distance to roadside poles and trees reduces crashes. The study demonstrates that the GNMs show slightly better model fitness than negative binomial (NB) models. Moreover, the MARS models outperformed NB and GNM models due to its strength to reflect the nonlinearity of crash predictors and interaction impacts among variables under different ranges. Therefore, it can be recommended that the CMFs are estimated using MARS when there are nonlinear relationships between crash rate and roadway characteristics, and interaction impacts among multiple treatments.     

Prioritizing Highway Safety Manual’s crash prediction variables using boosted regression trees

The Highway Safety Manual (HSM) recommends using the empirical Bayes (EB) method with locally derived calibration factors to predict an agency’s safety performance. However, the data needs for deriving these local calibration factors are significant, requiring very detailed roadway characteristics information. Many of the data variables identified in the HSM are currently unavailable in the states’ databases. Moreover, the process of collecting and maintaining all the HSM data variables is cost-prohibitive. Prioritization of the variables based on their impact on crash predictions would, therefore, help to identify influential variables for which data could be collected and maintained for continued updates. This study aims to determine the impact of each independent variable identified in the HSM on crash predictions. A relatively recent data mining approach called boosted regression trees (BRT) is used to investigate the association between the variables and crash predictions. The BRT method can effectively handle different types of predictor variables, identify very complex and non-linear association among variables, and compute variable importance. Five years of crash data from 2008 to 2012 on two urban and suburban facility types, two-lane undivided arterials and four-lane divided arterials, were analyzed for estimating the influence of variables on crash predictions. Variables were found to exhibit non-linear and sometimes complex relationship to predicted crash counts. In addition, only a few variables were found to explain most of the variation in the crash data.     

Application of Poisson random effect models for highway network screening

In recent years, Bayesian random effect models that account for the temporal and spatial correlations of crash data became popular in traffic safety research. This study employs random effect Poisson Log-Normal models for crash risk hotspot identification. Both the temporal and spatial correlations of crash data were considered. Potential for Safety Improvement (PSI) were adopted as a measure of the crash risk. Using the fatal and injury crashes that occurred on urban 4-lane divided arterials from 2006 to 2009 in the Central Florida area, the random effect approaches were compared to the traditional Empirical Bayesian (EB) method and the conventional Bayesian Poisson Log-Normal model. A series of method examination tests were conducted to evaluate the performance of different approaches. These tests include the previously developed site consistence test, method consistence test, total rank difference test, and the modified total score test, as well as the newly proposed total safety performance measure difference test. Results show that the Bayesian Poisson model accounting for both temporal and spatial random effects (PTSRE) outperforms the model that with only temporal random effect, and both are superior to the conventional Poisson Log-Normal model (PLN) and the EB model in the fitting of crash data. Additionally, the method evaluation tests indicate that the PTSRE model is significantly superior to the PLN model and the EB model in consistently identifying hotspots during successive time periods. The results suggest that the PTSRE model is a superior alternative for road site crash risk hotspot identification.     

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.     

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.     

Empirical Bayes before-after safety studies: lessons learned from two decades of experience and future directions

The empirical Bayes (EB) methodology has been applied for over 20 years now in conducting statistically defendable before-after studies of the safety effect of treatments applied to roadway sites. The appeal of the methodology is that it corrects for regression to the mean and traffic volume and other changes not due to the measure. There is, therefore, a natural tendency to put a stamp of approval on any study that uses this methodology, and to assume that the results can then be used in specifying crash modification factors for use in developing treatments for hazardous locations, or in designing new roads using tools such as the interactive highway safety design model (IHSDM). At the other extreme are skeptics who suggest that the increased sophistication and data needs of the EB methodology are not worth the effort since alternative, less complex methods can produce equally valid results. The primary objective of this paper is to capitalize on experience gained from two decades of conducting EB studies around the world to illustrate that the EB methodology, if properly undertaken, produces results that could be substantially different and less biased than those from more conventional types of studies. A secondary objective is to emphasize that caution is needed in assessing the validity of studies undertaken with the EB methodology and in using these results for providing crash modification factors. To this end, a number of issues that are critical to the proper conduct and interpretation of EB evaluations are raised and illustrated based on lessons learned from recent experience with these studies. These include: amalgamating the effects on different crash types; the specification of the reference/comparison groups; and accounting for traffic volume changes. Current and future directions, including the improvements offered by a full Bayes approach, are discussed.     

Full Bayesian evaluation of the safety effects of reducing the posted speed limit in urban residential area

Full Bayesian (FB) before–after evaluation is a newer approach than the empirical Bayesian (EB) evaluation in traffic safety research. While a number of earlier studies have conducted univariate and multivariate FB before–after safety evaluations and compared the results with the EB method, often contradictory conclusions have been drawn. To this end, the objectives of the current study were to (i) perform a before–after safety evaluation using both the univariate and multivariate FB methods in order to enhance our understanding of these methodologies, (ii) perform the EB evaluation and compare the results with those of the FB methods and (iii) apply the FB and EB methods to evaluate the safety effects of reducing the urban residential posted speed limit (PSL) for policy recommendation. In addition to three years of crash data for both the before and after periods, traffic volume, road geometry and other relevant data for both the treated and reference sites were collected and used. According to the model goodness-of-fit criteria, the current study found that the multivariate FB model for crash severities outperformed the univariate FB models. Moreover, in terms of statistical significance of the safety effects, the EB and FB methods led to opposite conclusions when the safety effects were relatively small with high standard deviation. Therefore, caution should be taken in drawing conclusions from the EB method. Based on the FB method, the PSL reduction was found effective in reducing crashes of all severities and thus is recommended for improving safety on urban residential collector roads.     

Application of the Conway-Maxwell-Poisson generalized linear model for analyzing motor vehicle crashes

This paper documents the application of the Conway–Maxwell–Poisson (COM-Poisson) generalized linear model (GLM) for modeling motor vehicle crashes. The COM-Poisson distribution, originally developed in 1962, has recently been re-introduced by statisticians for analyzing count data subjected to over- and under-dispersion. This innovative distribution is an extension of the Poisson distribution. The objectives of this study were to evaluate the application of the COM-Poisson GLM for analyzing motor vehicle crashes and compare the results with the traditional negative binomial (NB) model. The comparison analysis was carried out using the most common functional forms employed by transportation safety analysts, which link crashes to the entering flows at intersections or on segments. To accomplish the objectives of the study, several NB and COM-Poisson GLMs were developed and compared using two datasets. The first dataset contained crash data collected at signalized four-legged intersections in Toronto, Ont. The second dataset included data collected for rural four-lane divided and undivided highways in Texas. Several methods were used to assess the statistical fit and predictive performance of the models. The results of this study show that COM-Poisson GLMs perform as well as NB models in terms of GOF statistics and predictive performance. Given the fact the COM-Poisson distribution can also handle under-dispersed data (while the NB distribution cannot or has difficulties converging), which have sometimes been observed in crash databases, the COM-Poisson GLM offers a better alternative over the NB model for modeling motor vehicle crashes, especially given the important limitations recently documented in the safety literature about the latter type of model.     

Comparison of empirical Bayes and full Bayes approaches for before-after road safety evaluations

The empirical Bayes (EB) approach has now gained wide acceptance among researchers as the much preferred one for the before-after evaluation of road safety treatments. In this approach, the before period crash experience at treated sites is used in conjunction with a crash prediction model for untreated reference sites to estimate the expected number of crashes that would have occurred without treatment. This estimate is compared to the count of crashes observed after treatment to evaluate the effect of the treatment. This procedure accounts for regression-to-the-mean effects that result from the natural tendency to select for treatment those sites with high observed crash frequencies. Of late, a fully Bayesian (FB) approach has been suggested as a useful, though complex alternative to the empirical Bayes approach in that it is believed to require less data for untreated reference sites, it better accounts for uncertainty in data used, and it provides more detailed causal inferences and more flexibility in selecting crash count distributions. This paper adds to the literature on comparing the two Bayesian approaches through empirical applications. The main application is an evaluation of the conversion of road segments from a four-lane to a three-lane cross-section with two-way left-turn lanes (also known as road diets). For completeness, the paper also summarizes the results of an earlier application pertaining to the evaluation of conversion of rural intersections from unsignalized to signalized control. For both applications, the estimated safety effects from the two approaches are comparable.     

A comparative analysis of hotspot identification methods

The identification of crash hotspots is the first step of the highway safety management process. Errors in hotspot identification may result in the inefficient use of resources for safety improvements and may reduce the global effectiveness of the safety management process. Despite the importance of using effective hotspot identification (HSID) methods, only a few researchers have compared the performance of various methods. In this research, seven commonly applied HSID methods were compared against four robust and informative quantitative evaluation criteria. The following HSID methods were compared: crash frequency (CF), equivalent property damage only (EPDO) crash frequency, crash rate (CR), proportion method (P), empirical Bayes estimate of total-crash frequency (EB), empirical Bayes estimate of severe-crash frequency (EBs), and potential for improvement (PFI). The HSID methods were compared using the site consistency test, the method consistency test, the total rank differences test, and the total score test. These tests evaluate each HSID method's performance in a variety of areas, such as efficiency in identifying sites that show consistently poor safety performance, reliability in identifying the same hotspots in subsequent time periods, and ranking consistency. To evaluate the HSID methods, five years of crash data from the Italian motorway A16 were used.The quantitative evaluation tests showed that the EB method performs better than the other HSID methods. Test results highlight that the EB method is the most consistent and reliable method for identifying priority investigation locations. The EB expected frequency of total-crashes (EB) performed better than the EB expected frequency of severe-crashes (EBs), although the results differed only slightly when the number of identified hotspots increased. The CF method performed better than other HSID methods with more appealing theoretical arguments. In particular, the CF method performed better than the CR method. This result is quite alarming, since many agencies use the CR method. The PFI and EPDO methods were largely inconsistent. The proportion method performed worst in all of the tests. Overall, these results are consistent with the results of previous studies.The identification of engineering countermeasures that may reduce crashes was successful in all of the hotspots identified with the EB method; this finding shows that the identified hotspots can also be corrected.The advantages associated with the EB method were based on crash data from one Italian motorway, and the relative performances of HSID methods may change when using other crash data. However, the study results are very significant and are consistent with earlier findings. To further clarify the benefits of the EB method, this study should be replicated in other countries. Nevertheless, the study results, combined with previous research results, strongly suggest that the EB method should be the standard in the identification of hotspots.     

A full Bayes before-after study accounting for temporal and spatial effects: Evaluating the safety impact of new signal installations

Recently, important advances in road safety statistics have been brought about by methods able to address issues other than the choice of the best error structure for modeling crash data. In particular, accounting for spatial and temporal interdependence, i.e., the notion that the collision occurrence of a site or unit times depend on those of others, has become an important issue that needs further research.Overall, autoregressive models can be used for this purpose as they can specify that the output variable depends on its own previous values and on a stochastic term. Spatial effects have been investigated and applied mostly in the context of developing safety performance functions (SPFs) to relate crash occurrence to highway characteristics. Hence, there is a need for studies that attempt to estimate the effectiveness of safety countermeasures by including the spatial interdependence of road sites within the context of an observational before-after (BA) study. Moreover, the combination of temporal dynamics and spatial effects on crash frequency has not been explored in depth for SPF development.Therefore, the main goal of this research was to carry out a BA study accounting for spatial effects and temporal dynamics in evaluating the effectiveness of a road safety treatment. The countermeasure analyzed was the installation of traffic signals at unsignalized urban/suburban intersections in British Columbia (Canada). The full Bayes approach was selected as the statistical framework to develop the models.The results demonstrated that zone variation was a major component of total crash variability and that spatial effects were alleviated by clustering intersections together. Finally, the methodology used also allowed estimation of the treatment’s effectiveness in the form of crash modification factors and functions with time trends.     

To illuminate or not to illuminate: Roadway lighting as it affects traffic safety at intersections

A two-pronged effort to quantify the impact of lighting on traffic safety is presented. In the statistical approach, the effects of lighting on crash frequency for different intersection types in Minnesota were assessed using count regression models. The models included many geometric and traffic control variables to estimate the association between lighting and nighttime and daytime crashes and the resulting night-to-day crash ratios. Overall, the presence of roadway intersection lighting was found to be associated with an approximately 12% lower night-to-day crash ratio than unlighted intersections. In the parallel analytical approach, visual performance analyses based on roadway intersection lighting practices in Minnesota were made for the same intersection types investigated in the statistical approach. The results of both approaches were convergent, suggesting that visual performance improvements from roadway lighting could serve as input for predicting improvements in crash frequency. A provisional transfer function allows transportation engineers to evaluate alternative lighting systems in the design phase so selections based on expected benefits and costs can be made.     

Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway

While rural freeways generally have lower crash rates, interactions between driver behavior, traffic and geometric characteristics, and adverse weather conditions may increase the crash risk along some freeway sections. This paper examines the safety effects of roadway geometrics on crash occurrence along a freeway section that features mountainous terrain and adverse weather. Starting from preliminary exploration using Poisson models, Bayesian hierarchical models with spatial and random effects were developed to efficiently model the crash frequencies on road segments on the 20-mile freeway section of study. Crash data for 6 years (2000–2005), roadway geometry, traffic characteristics and weather information in addition to the effect of steep slopes and adverse weather of snow and dry seasons, were used in the investigation. Estimation of the model coefficients indicates that roadway geometry is significantly associated with crash risk; segments with steep downgrades were found to drastically increase the crash risk. Moreover, this crash risk could be significantly increased during snow season compared to dry season as a confounding effect between grades and pavement condition. Moreover, sites with higher degree of curvature, wider medians and an increase of the number of lanes appear to be associated with lower crash rate. Finally, a Bayesian ranking technique was implemented to rank the hazard levels of the roadway segments; the results confirmed that segments with steep downgrades are more crash prone along the study section.     

A fully Bayesian multivariate approach to before-after safety evaluation

This paper presents a fully Bayesian multivariate approach to before-after safety evaluation. Although empirical Bayes (EB) methods have been widely accepted as statistically defensible safety evaluation tools in observational before-after studies for more than a decade, EB has some limitations such that it requires a development and calibration of reliable safety performance functions (SPFs) and the uncertainty in the EB safety effectiveness estimates may be underestimated when a fairly large reference group is not available. This is because uncertainty (standard errors) of the estimated regression coefficients and dispersion parameter in SPFs is not reflected in the final safety effectiveness estimate of EB.Fully Bayesian (FB) methodologies in safety evaluation are emerging as the state-of-the-art methods that have a potential to overcome the limitations of EB in that uncertainty in regression parameters in the FB approach is propagated throughout the model and carries through to the final safety effectiveness estimate. Nonetheless, there have not yet been many applications of fully Bayesian methods in before-after studies. Part of reasons is the lack of documentation for a step-by-step FB implementation procedure for practitioners as well as an increased complexity in computation. As opposed to the EB methods of which steps are well-documented in the literature for practitioners, the steps for implementing before-after FB evaluations have not yet been clearly established, especially in more general settings such as a before-after study with a comparison group/comparison groups. The objectives of this paper are two-fold: (1) to develop a fully Bayesian multivariate approach jointly modeling crash counts of different types or severity levels for a before-after evaluation with a comparison group/comparison groups and (2) to establish a step-by-step procedure for implementing the FB methods for a before-after evaluation with a comparison group/comparison groups.The fully Bayesian multivariate approach introduced in this paper has additional advantages over the corresponding univariate approaches (whether classical or Bayesian) in that the multivariate approach can recover the underlying correlation structure of the multivariate crash counts and can also lead to a more precise safety effectiveness estimate by taking into account correlations among different crash severities or types for estimation of the expected number of crashes. The new method is illustrated with the multivariate crash count data obtained from expressways in Korea for 13 years to assess the safety effectiveness of decreasing the posted speed limit.     

Safety evaluation of multilane arterials in Florida

Resurfacing is one of the more common construction activities on highways. While its effect on riding quality on any type of roadway is obviously positive; its impact on safety as measured in terms of crashes is far from obvious. This study examines the safety effects of the resurfacing projects on multilane arterials with partially limited access. Empirical Bayes method, which is one of the most accepted approaches for conducting before-after evaluations, has been used to assess the safety effects of the resurfacing projects. Safety effects are estimated not only in terms of all crashes but also rear-end as well as severe crashes (crashes involving incapacitating and fatal injuries). The safety performance functions (SPFs) used in this study are negative binomial crash frequency estimation models that use the information on ADT, length of the segments, speed limit and number of lanes. These SPFs are segregated by crash groups (all, rear-end, and severe), length of the segments being evaluated, and land use (urban, suburban, and rural). The results of the analysis show that the resulting changes in safety following resurfacing projects vary widely. Evaluating additional improvements carried out with resurfacing activities showed that all (other than sidewalk improvements for total crashes) of them consistently led to improvements in safety of multilane arterial sections. It leads to the inference that it may be a good idea to take up additional improvements if it is cost effective to do them along with resurfacing. It was also found that the addition of turning lanes (left and/or right) and paving shoulders were two improvements associated with a project's relative performance in terms of reduction in rear-end crashes.     

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway

In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature.     

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)).