Modeling injury severity of crashes involving golf carts: A case study of The Villages, Florida.

OBJECTIVE: Crashes involving golf carts (GCs) have been on an increasing trend in recent years, particularly in the United States. This study focuses on analyzing GC crashes in the Florida community known as The Villages, one of the largest GC-oriented communities in the nation and worldwide. The objective was to evaluate the injury severity of crashes involving GCs in a retirement community where GCs are a common mode of transportation. METHODS: The ordinal logistic regression (OLR) and Decision Tree Ensemble (DTE) models were used to analyze the injury severity of 616 GC-related crashes. Models' accuracy parameters were used to check their reliability. RESULTS: The analysis revealed that GC crash severity is influenced by various factors. Factors found to be significant by the OLR model in determining injury severity include ejection of one or more occupants from the GC, the extent of damage to the GC, GC speed prior to the crash, roadway characteristics (including divided roadways, traffic control devices, paved shoulders, and T-intersections), and roll-over incidents. The OLR model demonstrated an overall accuracy of approximately 71% in predicting injury severity. The DTE model performed better, with an overall accuracy of 78%. The OLR model's findings were supported by the DTE model, which identified estimated GC speed, occupant(s) ejection from the GC, estimated GC vehicle damage, intersection type, and type of shoulder as the most important factors influencing GC crash severity. CONCLUSIONS: Understanding these factors is vital for transportation agencies to develop effective strategies to reduce the severity of GC crashes, ensuring the safety of GC users. This study provides recommendations to transportation agencies on measures to improve the safety of GCs.

Impacts of COVID-19 on the Operational Performance of Express Lanes and General-Purpose Lanes.

The COVID-19 pandemic outbreak brought significant changes in the travel behavior and operational characteristics of transportation systems. Express lanes (ELs) are among the transportation facilities that are affected by this pandemic. These facilities are built adjacent to existing general-purpose lanes (GPLs), providing drivers additional lanes that are dynamically priced in response to changing traffic conditions. This research investigated the impacts of COVID-19 on the operational performance of ELs and GPLs based on field data from a 5.5 mi corridor on I-95 in Miami, Florida, U.S. The traffic flow parameters, which include speed, traffic flow, and occupancy, were used to describe the traffic conditions before and during COVID-19 (i.e., March-June 2019 and March-June 2020, respectively). The travel time reliability measures, coefficient of variation of travel time, and planning time index, were used to measure user satisfaction. These metrics were derived from a multivariate Bayesian additive regression model that was developed to calibrate the traffic conditions on the study corridor. Overall, the model results indicated that both ELs and GPLs have less variation in travel time, thus making the travel time more reliable during COVID-19 than before. This may be attributed to the decline in the traffic volume observed during the pandemic. The results further showed that COVID-19 had more impact on the GPLs compared with the ELs. The results from this research could assist transportation agencies in understanding the impacts of the COVID-19 pandemic on ELs and GPLs in relation to traffic operations.

What are the leading causes of fatal and severe injury crashes involving older pedestrian? Evidence from Bayesian network model.

INTRODUCTION: Identifying factors contributing to the risk of older pedestrian fatal/severe injuries, along with their possible interdependency, is the first step towards improving safety. Several previous studies focused on identifying the influence of individual factors while ignoring their interdependencies. This study investigated the leading risk factors associated with older pedestrian fatalities/severe injuries by identifying the interdependency relationship among variables. METHOD: A Bayesian Logistic Regression (BLR) model was developed to identify significant factors influencing pedestrian fatalities and severe injuries, followed by a Bayesian Network (BN) model to reveal the interdependency relationship among the statistically significant variables and crash severity. Furthermore, the probabilistic inference was conducted to identify the leading cause of fatal and severe injuries involving older pedestrians. The models were developed with data from 913 pedestrian crashes involving older pedestrians at signalized intersections in Florida from 2016 through 2018. RESULTS: Vehicle maneuver, lighting condition, road type, and shoulder type were directly associated with older pedestrian fatality/severe injury. Vehicle maneuver (going straight ahead) was the most significant factor in influencing the severity of crashes involving older pedestrians. The interdependency of vehicle moving straight, nighttime condition, and two-way divided roadway with curbed shoulders was associated with the highest likelihood of fatal and severe-injury crashes involving older pedestrians. CONCLUSIONS: The Bayesian Network revealed the interdependency between variables associated with fatal and severe injury-crashes involving older pedestrians. The interdependency relationship with the highest likelihood to cause fatalities/severe-injuries comprised factors with the significant individual contribution to the severity of crashes involving older pedestrians. Practical applications: The interdependencies among variables identified in this research could help devise targeted engineering, education, and enforcement strategies that could potentially have a greater effect on improving the safety of older pedestrians.

Evaluating the effect of ramp metering on freeway safety using real-time traffic data.

Ramp metering relieves traffic congestion, reduces delay, and maintains the capacity flow on freeways. Due to its operational mechanism, ramp metering can also improve freeway safety. While the operational benefits of ramp metering have extensively been quantified, research on its safety effects is sparse. This study focused on evaluating the effects of ramp metering on the safety performance of the freeway mainline. It developed a crash risk prediction model for segments downstream of the entrance ramps when ramp metering is activated. The study was based on a corridor with system-wide ramp metering along I-95 in Miami, Florida. Real-time traffic, crash, and ramp metering operations data collected from 2016 to 2018 were used in the analysis. The study adopted a matched crash and non-crash case approach to evaluate the crash risk when ramp meters were activated and deactivated. A penalized logistic regression model was developed using a bootstrap resampling technique to estimate the effects of ramp metering activation and select important variables that could predict crash risk when ramp meters were activated. Results indicated that ramp metering improves safety along the freeway corridor by reducing the crash risk downstream of the entrance ramps. During ramp metering activation, the crash risk on segments downstream of the entrance ramps 5 min later can be predicted using the difference in the average lane speeds between upstream and downstream detectors, the average traffic volume in the lanes at the downstream and upstream detectors, and the coefficient of variation of speed between lanes in the upstream detectors. Also, the coefficient of variation of occupancy downstream could predict the crash risk 15 min later. The study results could be used by transportation agencies when evaluating the deployment of ramp meters. Moreover, the developed crash risk prediction model could be used in real-time to help agencies identify the increased crash risk and provide appropriate warning information to the upstream traffic.

Evaluating the impact of Road Rangers in preventing secondary crashes.

Many transportation agencies utilize freeway service patrols (FSPs) to quickly identify and respond to incidents. The objectives of FSP are to minimize the incident duration and increase safety at the incident scene. The current research explored the safety benefits of Florida's FSP program known as Road Rangers - harnessed from lowering the likelihood of secondary crashes (SCs) - compared to other responding agencies. The analysis was done on 6088 incidents that occurred on freeways in Jacksonville, Florida, from 2015 through 2017. Since SCs were not explicitly identified in the SunGuide® incident database, the study adopted a data-driven technique that used BlueToad® speed data to identify them. Once SCs were identified, a model was developed to identify factors influencing their occurrence. Factors such as an increase in equivalent hourly traffic volume, incident impact duration, and the percent of lanes closed significantly increased the likelihood of a SC. Besides, moderate/severe incidents, crash events, weekdays, peak hours, shoulder blockage, and incidents involving towing showed a high likelihood of resulting in a SC. Of practical importance, the model results revealed that a minute increase in incident impact duration increased the SC probability by 1.2 percent, with other factors held constant. Based on a 16-minutes decrease in incident impact duration, the Road Rangers program could lessen the probability of SCs by 21 percent, compared to other agencies. These findings could be beneficial to incident managers, responders, and researchers in evaluating the program's performance.

A before-after full bayes multivariate intervention model to estimate the safety effectiveness of red light cameras.

OBJECTIVE: Intersection-related crashes account for approximately 40% of all crashes and tend to be more severe. Red-light running (RLR) crashes are most severe as almost half of these crashes result in injuries and fatalities. To reduce RLR crashes, agencies have been deploying red light cameras (RLCs). The main objective of this study was to evaluate the safety effectiveness of RLCs in the City of Miami Beach, Florida. METHOD: The full Bayes (FB) approach was conducted based on five treatment intersections with six RLCs and 14 comparison intersections without RLCs. The analysis focused on target crash types, including rear-end, sideswipe, and angle/left-turn/right-turn crashes, and crash severity. RESULTS: The FB analysis indicated a significant sudden drop in all types of target crashes immediately after the installation of RLCs. Compared to the before-period, the after-period experienced: fewer angle/left-turn/right-turn crashes, fewer sideswipe crashes, and more rear-end crashes. The sideswipe and angle/left-turn/right-turn crashes dropped immediately after the installation of RLCs and then continued to increase, but they were still lower than the before- period. The rear-end crashes dropped immediately after the installation of RLCs and then continued to increase, but they increased at a steeper rate. Major and minor approaches AADT, higher speed limit, longer amber time, length of pedestrian crosswalk, and number of driveways within the intersection influence area increased the frequency of total target, PDO, and FI crashes. Intersections with all-red interval more than two seconds, major approach with more than two through lanes, and minor approach with more than one through lane, on the contrary, resulted in a fewer number of the total target, PDO, and FI crashes. The treatment indicator showed that treatment intersections experienced fewer FI, angle/left-turn/right-turn, and sideswipe crashes and more total, PDO, and rear-end crashes compared to the non-treatment intersections. CONCLUSION: This study provides reliable estimates of the safety effectiveness of RLCs since it accounts for uncertainties in the data, regression-to-the-mean, and spillover effects.

Modeling Wrong-way Driving (WWD) crash severity on arterials in Florida.

Wrong-way Driving (WWD) is the movement of a vehicle in a direction opposite to the one designated for travel. WWD studies and mitigation strategies have exclusively been focused on limited-access facilities. However, it has been established that WWD crashes on arterial corridors are also severe and relatively more common. As such, this study focused on determining factors influencing the severity of WWD crashes on arterials. The analysis was based on five years of WWD crashes (2012-2016) that occurred on state-maintained arterial corridors in Florida. Police reports of 2,879 crashes flagged as "wrong-way" were downloaded and individually reviewed. The manual review of the police reports revealed that of the 2,879 flagged WWD crashes, only 1,890 (i.e., 65.6 %) occurred as a result of a vehicle traveling the wrong way. The Bayesian partial proportional odds (PPO) model was used to establish the relationship between the severity of these WWD crashes and different driver attributes, temporal factors, and roadway characteristics. The following variables were significant at the 90 % Bayesian Credible Interval (BCI): day of the week, lighting condition, presence of work zone, crash location, age and gender of the wrong-way driver, airbag deployment, alcohol use, posted speed limit, speed ratio (i.e., driver's speed over the posted speed limit), and the manner of collision. Based on the model results, specific countermeasures on Education, Engineering, Enforcement, and Emergency response are discussed. Potential Transportation Systems Management and Operations (TSM&O) strategies for WWD detection systems on arterials to minimize WWD frequency and severity are also proposed.

Cognitive, Graphomotor, and Psychosocial Challenges in Pediatric Autoimmune Neuropsychiatric Disorders Associated With Streptococcal Infections (PANDAS).

OBJECTIVES: Pediatric autoimmune neuropsychiatric disorders associated with streptococcal infection (PANDAS) is characterized by the sudden onset of obsessive-compulsive disorder (OCD) and other neurobehavioral symptoms following group A streptococcal infection. The cardinal neuropsychiatric symptoms are believed to reflect an aberrant autoimmune or inflammatory response that may selectively disrupt basal ganglia function. The investigators examined whether neuropsychological skills associated with frontostriatal networks (executive functions and motor skills) are affected in patients with PANDAS following resolution of acute symptoms and the degree to which there are persistent social, emotional, and academic difficulties. METHODS: Twenty-seven patients ages 6-14 years (mean age=9.63 years [SD=1.78]; male, N=22) completed neuropsychological testing as part of routine clinical care. Performances on measures of intellectual ability, executive function, motor skills, and academic skills are reported, as well as parent-reported emotional, behavioral, and social skills. RESULTS: On neuropsychological measures, patients exhibited average intellectual functioning with relative and mild difficulties in skills supporting cognitive efficiency, including attentional regulation, inhibitory control, and processing speed. Dexterity was normal but graphomotor skills were reduced. Core reading, math, and writing skills were within expectations, but reading and math fluency were reduced, and the majority of patients received special education services or accommodations. Parents reported high levels of concern about anxiety, depression, inattention, hyperactivity, and social skills. CONCLUSIONS: These findings indicated relative difficulties with aspects of executive and motor functions. Although evaluations were performed following the resolution of acute symptoms, ongoing and significant academic difficulties and emotional, behavioral, and social concerns were targets for clinical intervention and support.

Severity analysis of crashes on express lane facilities using support vector machine model trained by firefly algorithm.

OBJECTIVE: Express lanes (ELs) provide an alternative way for improving the capacity of the existing freeway network without considerably expanding the roadway footprint. Although much research has been done to explore factors contributing to crashes on these facilities, there is not much discussion on factors influencing their injury severity. This study explored factors influencing the injury severity of crashes on EL facilities. METHOD: A Support Vector Machine (SVM) model trained by the Firefly Algorithm was used to identify factors influencing the injury severity of crashes on EL facilities. The analysis was based on three years of crash data (2012-2014) from four EL facilities in California, totaling 61 miles. RESULTS: The results indicated that the following factors increased the probability of an injury or a fatality: concrete barriers, high average annual daily traffic, rolling or mountainous terrain, weekend, adverse road surface condition, and nighttime condition. Moreover, wide right and left shoulder widths decreased the probability of having an injury or a fatality. CONCLUSIONS: The results provide insights into the influence of different geometric characteristics and crash-related factors on the severity of crashes on EL facilities. The study findings may assist agencies to better understand the impacts of factors contributing to injury and fatal crashes on EL facilities and implement strategies to reduce the severity of these crashes.

Isolated Cervical Dystonia: Management and Barriers to Care.

Background: Cervical dystonia (CD) is a rare disorder, and health care providers might be unfamiliar with its presentation, thus leading to delay in the initial diagnosis. The lack of awareness displays the need to highlight the clinical features and treatment in cervical dystonia. In our cohort, we have identified an earlier age of onset in men, despite an overall preponderance of affected women. Objective: We aim to identify the prevalence, age of onset, spread, and treatment modalities of CD in the population. We also highlight the barriers which patients encounter related to diagnosis, follow-up, and treatment. Methods: We reviewed 149 CD patients who attended specialized Dystonia Clinics over a 14-year period. Dystonia severity was rated using the Burke-Fahn-Marsden (BFM), Tsui, and Toronto Western Spasmodic Torticollis Rating Scales (TWSTRS). Mood and quality of life were assessed using Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), and 36-Item Short Form Health Survey (SF-36). Results: CD patients were majority White (91.3%) and more commonly female (75.8%). Men had an earlier median age of onset, 40.5 years (p = 0.044). BAI revealed a mean score of 7.2 (±6.4, n = 50) indicating minimal anxiety while BDI revealed a mean score of 7.30 (±7.6, n = 50) indicating minimal depression. The only SF-36 subscales associated with CD severity were physical functioning (p = 0.040) pain (p = 0.008) and general health (p = 0.014). Conclusion: There appear to be gender differences in both the prevalence and age of onset of the disease. There was a 3-fold higher incidence in women than in men. CD patients of both sexes experience barriers to care, which can be reflected in their quality of life and time-to-diagnosis. In addition, males were less likely to experience an objective benefit with botulinum toxin treatment and more likely to discontinue care. Greater awareness of CD by health care providers is important to reduce the time-to-diagnosis.

Cognitive Complaints in Motor Functional Neurological (Conversion) Disorders: A Focused Review and Clinical Perspective.

Functional neurological (conversion) disorder (FND) is a neuropsychiatric condition characterized by sensorimotor symptoms exhibiting features incompatible with other neurologic diseases. Individuals with motor FND (mFND) typically present with limb weakness, nonepileptic seizures, and/or abnormal movements. However, this population also frequently reports clouded thinking, inattention, and memory difficulties. Cognitive complaints in individuals with mFND are important to evaluate as they may negatively impact quality of life and impede treatment engagement. We provide a narrative review of the neuropsychological testing literature detailing neurocognitive profiles of individuals with mFND. We also present three illustrative clinical cases at the intersection of mFND and cognitive concerns. Several studies and our case examples highlight that generally normal cognitive performance can be observed concurrently with subjective cognitive complaints in some individuals with mFND; this mismatch may be a possible "rule-in" sign of functional cognitive symptoms. Other studies have reported impairments in attention, memory, language, visuospatial, and executive functioning in individuals with mFND. These impairments could be related to medical-psychiatric comorbidities, psychotropic medication side effects, and intrinsic disease mechanisms. When evaluating individuals with mFND and their cognitive complaints, clinicians can use performance validity test and psychopathology findings to help them interpret the neuropsychological test results. Perceptual mismatches between intact objective cognitive performance and subjective cognitive complaints may reflect a negative attentional bias for cognitive abilities that can be targeted with cognitive retraining and cognitive behavioral therapy. Neuropsychological evaluations may provide a useful adjunctive tool clinicians can use to help assess individuals with mFND and cognitive concerns.

Sensory Processing Difficulties in Functional Neurological Disorder: A Possible Predisposing Vulnerability?

BACKGROUND: Functional neurological disorder (FND) is a prevalent neuropsychiatric condition characterized by sensorimotor difficulties. Patients with FND at times report that sensory experiences trigger and/or exacerbate their symptoms. Sensory processing difficulties are also commonly reported in other psychiatric disorders frequently comorbid in FND, suggesting that contextualizing sensory profiles in FND within a biopsychosocial model may be clinically relevant. OBJECTIVE: To address this literature gap, we conducted a retrospective cohort study to examine sensory processing patterns and their relationship to other neuropsychiatric characteristics in patients with FND. METHODS: A retrospective chart review design was used to investigate sensory processing patterns, established with the Adolescent/Adult Sensory Profile self-report questionnaire, in 44 patients with FND. Univariate analyses of cross-sectional screening tests followed by multivariate linear regression analyses were performed to identify clinical factors associated with sensory processing scores in the FND cohort. RESULTS: Compared to normative data, most patients with FND reported sensory processing tendencies toward low registration, sensory sensitivity, and sensation avoiding. In multivariate regression analyses, the presence of a lifetime anxiety disorder independently predicted elevated low registration scores, while female gender and number of current medications independently predicted increased sensory sensitivity scores. In uncorrected univariate analyses only, individuals with psychogenic nonepileptic seizures were more likely to report increased sensory sensitivity and elevated low registration. CONCLUSION: These preliminary findings support sensory processing difficulties in some patients with FND. Prospective and large sample size studies are needed to investigate relationships between sensory processing profiles and neuropsychiatric comorbidities, FND subtypes, and treatment outcomes.

Application of the Poisson-Tweedie distribution in analyzing crash frequency data.

This paper describes a study that applies the Poisson-Tweedie distribution in developing crash frequency models. The Poisson-Tweedie distribution offers a unified framework to model overdispersed, underdispersed, zero-inflated, spatial, and longitudinal count data, as well as multiple response variables of similar or mixed types. The form of its variance function is simple, and can be specified as the mean added to the product of dispersion and mean raised to the power P. The flexibility of the Poisson-Tweedie distribution lies in the domain of P, which includes positive real number values. Special cases of the Poisson-Tweedie distribution models include the linear form of the negative binomial (NB1) model with P equal to 1.0, the geometric Poisson (GeoP) model with P equal to 1.5, the quadratic form of the negative binomial (NB2) model with P equal to 2.0, and the Poisson Inverse Gaussian (PIG) model with P equal to 3.0. A series of models were developed in this study using the Poisson-Tweedie distribution without any restrictions on the value of the power parameter as well as with specific values of the power parameter representing NB1, GeoP, NB2, and PIG models. The effects of fixed and varying dispersion parameters (i.e., dispersion as a function of covariates) on the variance and expected crash frequency estimates were also examined. Three years (2012-2014) of crash data from urban three-leg stop-controlled intersections and urban four-leg signalized intersections in the state of Florida were used to develop the models. The Poisson-Tweedie models or the GeoP models were found to perform better when the dispersion parameter was constant or fixed. With the varying dispersion parameter, the NB2 and PIG models were found to perform better, with both performing equally well. Also, the fixed dispersion parameter values were found to be smaller in the models with a higher value of the power parameter. The variation across the models in their estimates of weight factor, expected crash frequency, and potential for safety improvement of hazardous sites based on the empirical Bayes method was also discussed.

Briquet syndrome revisited: implications for functional neurological disorder.

With the creation of the Somatic Symptom and Related Disorders category of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition in 2013, the functional neurological (symptom) disorder diagnostic criteria underwent transformative changes. These included an emphasis on 'rule-in' physical examination signs/semiological features guiding diagnosis and the removal of a required proximal psychological stressor to be linked to symptoms. In addition, the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition somatization disorder, somatoform pain disorder and undifferentiated somatoform disorder conditions were eliminated and collapsed into the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition somatic symptom disorder diagnosis. With somatic symptom disorder, emphasis was placed on a cognitive-behavioural (psychological) formulation as the basis for diagnosis in individuals reporting distressing bodily symptoms such as pain and/or fatigue; the need for bodily symptoms to be 'medically unexplained' was removed, and the overall utility of this diagnostic criteria remains debated. A consequence of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition restructuring is that the diagnosis of somatization disorder that encompassed individuals with functional neurological (sensorimotor) symptoms and prominent other bodily symptoms, including pain, was eliminated. This change negatively impacts clinical and research efforts because many patients with functional neurological disorder experience pain, supporting that the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition would benefit from an integrated diagnosis at this intersection. We seek to revisit this with modifications, particularly since pain (and a Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition somatization disorder comorbidity, more specifically) is associated with poor clinical prognosis in functional neurological disorder. As a first step, we systematically reviewed the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition somatization disorder literature to detail epidemiologic, healthcare utilization, demographic, diagnostic, medical and psychiatric comorbidity, psychosocial, neurobiological and treatment data. Thereafter, we propose a preliminary revision to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition allowing for the specifier functional neurological disorder 'with prominent pain'. To meet this criterion, core functional neurological symptoms (e.g. limb weakness, gait difficulties, seizures, non-dermatomal sensory loss and/or blindness) would have 'rule-in' signs and pain (>6 months) impairing social and/or occupational functioning would also be present. Two optional secondary specifiers assist in characterizing individuals with cognitive-behavioural (psychological) features recognized to amplify or perpetuate pain and documenting if there is a pain-related comorbidity. The specifier of 'with prominent pain' is etiologically neutral, while secondary specifiers provide additional clarification. We advocate for a similar approach to contextualize fatigue and mixed somatic symptoms in functional neurological disorder. While this preliminary proposal requires prospective data and additional discussion, these revisions offer the potential benefit to readily identify important functional neurological disorder subgroups-resulting in diagnostic, treatment and pathophysiology implications.

Severity of motorcycle crashes in Dar es Salaam, Tanzania.

OBJECTIVE: Motorcycles are a common mode of transportation in low- and middle-income countries. Tanzania, in particular, has experienced an increased use of motorcycles in the last decade. In Dar es Salaam, motorcycles provide door-to-door travel and often operate where more conventional services are uneconomical or physically impossible to maneuver. Although motorcycles play a crucial role in improving mobility in the city, they have several safety issues. This study focuses on identifying factors influencing the severity of motorcycle crashes. METHOD: A multinomial logit analysis was conducted to identify factors influencing the severity of motorcycle crashes in Dar es Salaam, Tanzania. The severity categories were fatal, severe injury, minor injury, and property damage only (PDO). The analysis was based on a total of 784 motorcycle crashes that occurred from 2013 to 2016. RESULTS: The following factors were found to increase the probability of a fatality: Speeding, driving under the influence, head-on impact, presence of horizontal curves, reckless riding, off-peak hours, violations, and riding without a helmet. The results indicate that crashes occurring on weekdays, during peak hours, at intersections, involving a rear-end impact, in daylight, on street roads, and under clear weather conditions decrease the probability of a fatality. However, minor injury and PDO crashes were found to be associated with crashes occurring during peak hours, at intersections, and on street roads, as well as failure to yield right-of-way. CONCLUSIONS: Several countermeasures are recommended based on the study findings. The recommended countermeasures focus on the holistic safety improvement strategies constituting the three Es of highway safety, namely, engineering, education, and enforcement.

Estimation of bicycle crash modification factors (CMFs) on urban facilities using zero inflated negative binomial models.

The objective of this study was to develop crash modification factors (CMFs) for bicycle crashes for different roadway segment and intersection facility types in urban areas. The study used four years (2011-2014) of crash data from Florida to quantify the safety impacts of roadway and traffic characteristics, bicycle infrastructure, and bicycle activity data on bicycle crashes. A cross-sectional analysis using Generalized Linear Model (GLM) approach with Zero Inflated Negative Binomial (ZINB) distribution was adopted to develop the relevant regression models in this study. Lane width, speed limit, and grass in the median were observed to have positive impacts on reducing bicycle crashes. On the contrary, presence of sidewalk and sidewalk barrier were found to increase the bicycle crash probabilities. Increased bicycle activity was found to reduce the bicycle crash probabilities on segments, while increased bicycle activity resulted in higher bicycle crash probabilities at intersections. Bus stops were found to increase the bicycle crash probabilities at intersections, whereas, protected signal control had a positive impact on bicycle safety. This research provides a greater insight into how various characteristics affect bicycle safety, a topic that is seldom considered by researchers and practitioners.

Likelihood estimation of secondary crashes using Bayesian complementary log-log model.

Secondary crashes (SCs) occur within the spatial and temporal impact range of a primary incident. They are non-recurring events and are major contributors to increased traffic delay, and reduced safety, particularly in urban areas. However, the limited knowledge on the nature of SCs has largely impeded their mitigation strategies. The primary objective of this study was to develop a reliable SC risk prediction model using real-time traffic flow conditions. The study data were collected on a 35-mile I-95 freeway section for three years in Jacksonville, Florida. SCs were identified based on travel speed data archived by the Bluetooth detectors. Bayesian random effect complementary log-log model was used to link the probability of SCs with real-time traffic flow characteristics, primary incident characteristics, environmental conditions, and geometric characteristics. Random forests technique was used to select the important variables. The results indicated that the following variables significantly affect the likelihood of SCs: average occupancy, incident severity, percent of lanes closed, incident type, incident clearance duration, incident impact duration, and incident occurrence time. The study results have the potential to proactively prevent SCs.

Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models.

The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag's model and the Leroux's model, in crash prediction. The Besag's models, which differ from the Leroux's models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies.

A Bayesian procedure for evaluating the frequency of calibration factor updates in highway safety manual (HSM) applications.

The Highway Safety Manual (HSM) presents statistical models to quantitatively estimate an agency's safety performance. The models were developed using data from only a few U.S. states. To account for the effects of the local attributes and temporal factors on crash occurrence, agencies are required to calibrate the HSM-default models for crash predictions. The manual suggests updating calibration factors every two to three years, or preferably on an annual basis. Given that the calibration process involves substantial time, effort, and resources, a comprehensive analysis of the required calibration factor update frequency is valuable to the agencies. Accordingly, the objective of this study is to evaluate the HSM's recommendation and determine the required frequency of calibration factor updates. A robust Bayesian estimation procedure is used to assess the variation between calibration factors computed annually, biennially, and triennially using data collected from over 2400 miles of segments and over 700 intersections on urban and suburban facilities in Florida. Bayesian model yields a posterior distribution of the model parameters that give credible information to infer whether the difference between calibration factors computed at specified intervals is credibly different from the null value which represents unaltered calibration factors between the comparison years or in other words, zero difference. The concept of the null value is extended to include the range of values that are practically equivalent to zero. Bayesian inference shows that calibration factors based on total crash frequency are required to be updated every two years in cases where the variations between calibration factors are not greater than 0.01. When the variations are between 0.01 and 0.05, calibration factors based on total crash frequency could be updated every three years.

Safety performance evaluation of cable median barriers on freeways in Florida.

OBJECTIVE: This article aims to evaluate the safety performance of cable median barriers on freeways in Florida. METHOD: The safety performance evaluation was based on the percentages of barrier and median crossovers by vehicle type, crash severity, and cable median barrier type (Trinity Cable Safety System [CASS] and Gibraltar system). Twenty-three locations with cable median barriers totaling about 101 miles were identified. Police reports of 6,524 crashes from years 2005-2010 at these locations were reviewed to verify and obtain detailed crash information. A total of 549 crashes were determined to be barrier related (i.e., crashes involving vehicles hitting the cable median barrier) and were reviewed in further detail to identify crossover crashes and the manner in which the vehicles crossed the barriers; that is, by either overriding, underriding, or penetrating the barriers. RESULTS: Overall, 2.6% of vehicles that hit the cable median barrier crossed the median and traversed into the opposite travel lane. Overall, 98.1% of cars and 95.5% of light trucks that hit the barrier were prevented from crossing the median. In other words, 1.9% of cars and 4.5% of light trucks that hit the barrier had crossed the median and encroached on the opposite travel lanes. There is no significant difference in the performance of cable median barrier for cars versus light trucks in terms of crossover crashes. In terms of severity, overrides were more severe compared to underrides and penetrations. The statistics showed that the CASS and Gibraltar systems performed similarly in terms of crossover crashes. However, the Gibraltar system experienced a higher proportion of penetrations compared to the CASS system. The CASS system resulted in a slightly higher percentage of moderate and minor injury crashes compared to the Gibraltar system. CONCLUSIONS: Cable median barriers are successful in preventing median crossover crashes; 97.4% of the cable median barrier crashes were prevented from crossing over the median. Of all of the vehicles that hit the barrier, 83.6% were either redirected or contained by the cable barrier system. Barrier crossover crashes were found to be more severe compared to barrier noncrossover crashes. In addition, overrides were found to be more severe compared to underrides and penetrations.