Kirolos Haleem

Examining Traffic Crash Injury Severity at Unsignalized Intersections

INTRODUCTION This study presents multiple approaches to the analysis of crash injury severity at three- and four-legged unsignalized intersections in the state of Florida from 2003 until 2006. An extensive data collection process was conducted for this study. METHOD The dataset used in the analysis included 2,043 unsignalized intersections in six counties in the state of Florida. For the scope of this study, there were three approaches explored. The first approach dealt with the five injury levels, and an ordered probit model was fitted. The second approach was an aggregated one, and dealt with only the severe versus non-severe crash levels, and a binary probit model was used. The third approach dealt with fitting a nested logit model. Results from the three fitted approaches were shown and discussed, and a comparison between the three approaches was shown. RESULTS Several important factors affecting crash severity at unsignalized intersections were identified. These include the traffic volume on the major approach, and the number of through lanes on the minor approach (surrogate measure for traffic volume), and among the geometric factors, the upstream and downstream distance to the nearest signalized intersection, left and right shoulder width, number of left turn movements on the minor approach, and number of right and left turn lanes on the major approach. As for driver factors, young and very young at-fault drivers were associated with the least fatal probability compared to other age groups. IMPACT ON INDUSTRY The analysis identified some countermeasures to reduce injury severity at unsignalized intersections. The spatial covariates showed the importance of including safety awareness campaigns for speeding enforcement. Also, having a 90-degree intersection design is the most appropriate safety design for reducing severity. Moreover, the assurance of marking stop lines at unsignalized intersections is very essential.

Effect of driver’s age and side of impact on crash severity along urban freeways: A mixed logit approach

INTRODUCTION This study identifies geometric, traffic, environmental, vehicle-related, and driver-related predictors of crash injury severity on urban freeways. METHOD The study takes advantage of the mixed logit models ability to account for unobserved effects that are difficult to quantify and may affect the model estimation, such as the drivers reaction at the time of crash. Crashes of 5 years occurring on 89 urban freeway segments throughout the state of Florida in the United States were used. Examples of severity predictors explored include traffic volume, distance of the crash to the nearest ramp, and detailed drivers age, vehicle types, and sides of impact. To show how the parameter estimates could vary, a binary logit model was compared with the mixed logit model. RESULTS It was found that the at-fault drivers age, traffic volume, distance of the crash to the nearest ramp, vehicle type, side of impact, and percentage of trucks significantly influence severity on urban freeways. Additionally, young at-fault drivers were associated with a significant severity risk increase relative to other age groups. It was also observed that some variables in the binary logit model yielded illogic estimates due to ignoring the random variation of the estimation. Since the at-fault drivers age and side of impact were significant random parameters in the mixed logit model, an in-depth investigation was performed. It was noticed that back, left, and right impacts had the highest risk among middle-aged drivers, followed by young drivers, very young drivers, and finally, old and very old drivers. IMPACT ON INDUSTRY To reduce side impacts due to lane changing, two primary strategies can be recommended. The first strategy is to conduct campaigns to convey the hazardous effect of changing lanes at higher speeds. The second is to devise in-vehicle side crash avoidance systems to alert drivers of a potential crash risk. CONCLUSIONS The study provided a promising approach to screening the predictors before fitting the mixed logit model using the random forest technique. Furthermore, potential countermeasures were proposed to reduce the severity of impacts.

Analyzing pedestrian crash injury severity at signalized and non-signalized locations

This study identifies and compares the significant factors affecting pedestrian crash injury severity at signalized and unsignalized intersections. The factors explored include geometric predictors (e.g., presence and type of crosswalk and presence of pedestrian refuge area), traffic predictors (e.g., annual average daily traffic (AADT), speed limit, and percentage of trucks), road user variables (e.g., pedestrian age and pedestrian maneuver before crash), environmental predictors (e.g., weather and lighting conditions), and vehicle-related predictors (e.g., vehicle type). The analysis was conducted using the mixed logit model, which allows the parameter estimates to randomly vary across the observations. The study used three years of pedestrian crash data from Florida. Police reports were reviewed in detail to have a better understanding of how each pedestrian crash occurred. Additionally, information that is unavailable in the crash records, such as at-fault road user and pedestrian maneuver, was collected. At signalized intersections, higher AADT, speed limit, and percentage of trucks; very old pedestrians; at-fault pedestrians; rainy weather; and dark lighting condition were associated with higher pedestrian severity risk. For example, a one-percent higher truck percentage increases the probability of severe injuries by 1.37%. A one-mile-per-hour higher speed limit increases the probability of severe injuries by 1.22%. At unsignalized intersections, pedestrian walking along roadway, middle and very old pedestrians, at-fault pedestrians, vans, dark lighting condition, and higher speed limit were associated with higher pedestrian severity risk. On the other hand, standard crosswalks were associated with 1.36% reduction in pedestrian severe injuries. Several countermeasures to reduce pedestrian injury severity are recommended.

Using a reliability process to reduce uncertainty in predicting crashes at unsignalized intersections

The negative binomial (NB) model has been used extensively by traffic safety analysts as a crash prediction model, because it can accommodate the over-dispersion criterion usually exhibited in crash count data. However, the NB model is still a probabilistic model that may benefit from updating the parameters of the covariates to better predict crash frequencies at intersections. The objective of this paper is to examine the effect of updating the parameters of the covariates in the fitted NB model using a Bayesian updating reliability method to more accurately predict crash frequencies at 3-legged and 4-legged unsignalized intersections. For this purpose, data from 433 unsignalized intersections in Orange County, Florida were collected and used in the analysis. Four Bayesian-structure models were examined: (1) a non-informative prior with a log-gamma likelihood function, (2) a non-informative prior with an NB likelihood function, (3) an informative prior with an NB likelihood function, and (4) an informative prior with a log-gamma likelihood function. Standard measures of model effectiveness, such as the Akaike information criterion (AIC), mean absolute deviance (MAD), mean square prediction error (MSPE) and overall prediction accuracy, were used to compare the NB and Bayesian model predictions. Considering only the best estimates of the model parameters (ignoring uncertainty), both the NB and Bayesian models yielded favorable results. However, when considering the standard errors for the fitted parameters as a surrogate measure for measuring uncertainty, the Bayesian methods yielded more promising results. The full Bayesian updating framework using the log-gamma likelihood function for updating parameter estimates of the NB probabilistic models resulted in the least standard error values.

Identifying traditional and nontraditional predictors of crash injury severity on major urban roadways

Objective: This study identifies and compares the factors that contribute to injury severity on urban freeways and arterials and recommends potential countermeasures to enhance the safety of both facilities. The study makes use of an extensive data set from the State of Florida in the United States. To obtain a more complete picture, this study explores both traditional and nontraditional severity predictors. Some traditional predictors include traffic volume, speed limit, and road surface condition. The nontraditional predictors are comprised of those rarely explored in previous severity studies, including crash distance to the nearest ramp location, detailed vehicle types, and lighting and weather conditions. Methods: The analysis was conducted using the ordered and binary probit models, which are well suited for the inherently ordered property of injury severity. Results: An important finding is the significance of the distance of crash to the nearest ramp junction/access point, for which the increase in the distance yielded a severity increase at both facilities. Other significant factors included traffic volume, speed limit, at-fault drivers age, road surface condition, alcohol and drug involvement, and left and right shoulder widths. In comparing both facilities, sport utility vehicles (SUVs) and pickup trucks showed a fatality/severity increase on freeways and a decrease on arterials. Furthermore, the detailed list of variables such as crash time provided pertinent severity trend information that showed that, compared to the other periods, the afternoon peak period had the highest reduction in fatality/severity. Conclusions: Both probit models succeeded in identifying significant severity predictors for each facility. The binary probit model outperformed the ordered probit model based on the higher elasticities (marginal effects) for the fatality/severity probability change, as well as the goodness of fit. As such, this study provides the guidelines for assessing the impact of important roadway and traffic characteristics on crash injury severity along freeways and arterials.

Multiple Applications of Multivariate Adaptive Regression Splines Technique to Predict Rear-End Crashes at Unsignalized Intersections

Crash prediction models are used extensively in highway safety analysis. This paper discusses a recently developed data-mining technique to predict motor vehicle crashes: the multivariate adaptive regression splines (MARS) technique. MARS shows promising predictive power and does not suffer from a black-box limitation. Negative binomial (NB) and MARS models were fitted and compared with the use of extensive data collected on unsignalized intersections in Florida. Two models were estimated for rear-end crash frequency at three-and four-legged unsignalized intersections. Treatment of crash frequency as a continuous response variable to fit a MARS model was also examined by normalizing crash frequency with the natural logarithm of the annual average daily traffic. The combination of MARS with a machine learning technique (random forest) was explored and discussed. The significant factors that affected rear-end crashes were traffic volume on the major road, upstream and downstream distances to the nearest signalized intersection, median type on the major approach, land use at the intersections influence area, and geographic location within the state. The study showed that MARS could predict crashes almost like the traditional NB models, and its goodness-of-fit performance was encouraging. The use of MARS to predict continuous response variables yielded more favorable results than its use to predict discrete response variables. The generated MARS models showed the most promising results after the covariates were screened by using random forest.

Clustering-based roadway segment division for the identification of high-crash locations

This article introduces a clustering approach to roadway segment division, in place of the traditional fixed-length and variable-length division methods, to improve the calibration of safety performance functions (SPFs) for the purpose of identifying high-crash locations. The clustering approach helps to reduce crash heterogeneity for within-group elements by grouping roadway segments with similar crash distributions into homogeneous groups. For comparison purpose, all three segment division methods were applied to a 142.6-kilometer (88.6-mile) stretch of freeway on Interstate 95 that spans three counties in southern Florida in the United States. Using 5 years of crash data occurring on segments generated from each of the three division methods, the corresponding SPFs were calibrated using the negative binomial model. The calibrated SPFs were then used in the empirical Bayes approach of identifying high-crash locations. The results showed that clustering method produced a much better-fitted SPF than that produced by using the traditional division methods. Furthermore, the site screening for high-crash locations on segments divided by the clustering method improved upon the shortcomings of that using the existing sliding window method.

Full Versus Simple Safety Performance Functions: Comparison Based on Urban Four-Lane Freeway Interchange Influence Areas in Florida

The empirical Bayes approach adopted in the Highway Safety Manual and the SafetyAnalyst software application require the use of safety performance functions (SPFs). SafetyAnalyst adopts a form of SPF, known as the simple SPF, that relates crash experience to traffic volume only. It is a flow-only model that is calibrated by using all sites irrespective of their base geometric conditions. Full SPFs, however, relate crash occurrence to roadway geometric characteristics in addition to traffic characteristics. This study compared the simple SPFs provided in SafetyAnalyst with full SPFs in two safety applications: crash prediction performance and identification of high-crash locations. To compare the prediction performance, the simple and full SPFs were estimated with data collected on urban four-lane freeway interchange influence areas in Florida. Models were estimated for both total crashes and fatal and injury crashes. The mean absolute deviance and the mean square prediction error were used to assess and compare the prediction performance of the two models, and the variation in ranking the high-crash locations with each model was also examined. The results showed that the two models yielded very similar performance of crash prediction and network screening. This empirical result supports the use of the flow-only SPF model adopted in SafetyAnalyst, whose development requires much less effort compared with that for full SPFs.

Investigating risk factors of traffic casualties at private highway-railroad grade crossings in the United States.

Private highway-railroad grade crossings (HRGCs) are intersections of highways and railroads on roadways that are not maintained by a public authority. Since no public authority maintains private HRGCs, fatal and injury crashes at these locations are of concern. However, no study has been conducted at private HRGCs to identify the safety issues that might exist and how to alleviate them. This study identifies the significant predictors of traffic casualties (including both injuries and fatalities) at private HRGCs in the U.S. using six years of nationwide crashes from 2009 to 2014. Two levels of injury severity were considered, injury (including fatalities and injuries) and no injury. The study investigates multiple predictors, e.g., temporal crash characteristics, geometry, railroad, traffic, vehicle, and environment. The study applies both the mixed logit and binary logit models. The mixed logit model was found to outperform the binary logit model. The mixed logit model revealed that drivers who did not stop, railroad equipment that struck highway users, higher train speeds, non-presence of advance warning signs, concrete road surface type, and cloudy weather were associated with an increase in injuries and fatalities. For example, a one-mile-per-hour higher train speed increases the probability of fatality by 22%. On the contrary, male drivers, PM peak periods, and presence of warning devices at both approaches were associated with a fatality reduction. Potential strategies are recommended to alleviate injuries and fatalities at private HRGCs.

Safety Performance of G4 (1S) W-Beam Guardrails versus Cable Median Barriers on Florida's Freeways

Different roadside safety hardware including guardrails and cable barriers have been installed on freeways to prevent run-off-the-road and median crossover crashes. This article compares the safety performance of G4 (1S) strong-post W-beam guardrails and cable barriers installed in the medians on freeways in Florida. The comparison is based on the percentages of barrier and median crossovers by vehicle type and crash severity. A crash is categorized as barrier crossover crash if the errant vehicle crosses the barrier during the crash. If after crossing the barrier the errant vehicle clears the median and traverses into the opposite travel lanes, it becomes a median crossover crash. To obtain a more precise measurement of barrier performance, this study considers only crashes involving vehicles hitting a barrier. A total of 678.9 miles of freeways with G4 (1S) W-beam guardrails and 101.0 miles of freeways with cable barriers were identified. Police reports of 8,536 crashes from years 2006 to 2010 at these locations were reviewed. Z-test and odds ratio were used to compare the safety performance of cable median barriers and guardrails. Overall, guardrails performed slightly better than cable barriers in terms of barrier and median crossover crashes. However, cable median barriers were found to result in fewer severe injury crashes.