Impact of real-time weather conditions on crash injury severity in Kentucky using the correlated random parameters logit model with heterogeneity in means.

The present study investigated the impact of real-time weather (air temperature, relative humidity, precipitation, wind speed, and solar radiation) on crash injury severity. Recent crash data (January 2016 to April 2021) on Interstate-75 in the state of Kentucky were merged with real-time weather information (retrieved from Kentucky Mesonet stations) at the 1-hour level. The severity index "SI" (i.

Examining crash injury severity and barrier-hit outcomes from cable barriers and strong-post guardrails on Alabama's interstate highways.

Introduction: This study investigates the impact of several risk factors (i.e., roadway, driver, vehicle, environmental, and barrier-specific characteristics) on the injury severity resulting from barrier-related crashes and also on barrier-hit outcomes (i.

Developing Bicycle-Vehicle Crash-Specific Safety Performance Functions in Alabama Using Different Techniques.

This study develops bicycle-vehicle safety performance functions (SPFs) for five facilities in the Highway Safety Manual (HSM). These are urban two-lane undivided segments (U2U), urban four-lane divided/undivided segments (U4DU), rural two-lane undivided segments (R2U), urban four-leg and three-leg signalized intersections (USG), and urban four-leg and three-leg stop-controlled intersections (UST). Two modeling techniques were explored, the Conway-Maxwell-Poisson (COM-Poisson) model (to accommodate bicycle-vehicle crash under-dispersion) and a machine learning technique, the multivariate adaptive regression splines (MARS).

Identifying and comparing the injury severity risk factors on rural freeways in different states in the United States.

This study identifies and compares those risk factors affecting crash injuries and fatalities on rural freeways in Montana and West Virginia in the United States using the mixed logit model. Three-year crashes on rural freeway segments in both states are used. Higher annual average daily traffic (AADT) was associated with a reduction in injuries/fatalities in both states, with higher reduction in West Virginia (40%) than in Montana (25%).

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.

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.

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.

Contributing factors of crash injury severity at public highway-railroad grade crossings in the U.S.

Introduction: The Moving Ahead for Progress in the 21st Century (MAP-21) includes a separate program that supports safety improvements to reduce the number of fatalities and injuries at public highway-railroad grade crossings (HRGCs). This study identifies the significant factors affecting crash injury severity at public HRGCs in the United States.

Method: Crashes from 2009 through 2013 on 5,528 public HRGCs, extracted from the Federal Railroad Administration database, were used in the analysis.

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 model's ability to account for unobserved effects that are difficult to quantify and may affect the model estimation, such as the driver's 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.

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

Crash modification factors (CMFs) are used to measure the safety impacts of changes in specific geometric characteristics. Their development has gained much interest following the adoption of CMFs by the recently released Highway Safety Manual (HSM) and SafetyAnalyst tool in the United States. This paper describes a study to develop CMFs for interchange influence areas on urban freeways in the state of Florida.

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.

Analyzing angle crashes at unsignalized intersections using machine learning techniques.

A recently developed machine learning technique, multivariate adaptive regression splines (MARS), is introduced in this study to predict vehicles' angle crashes. MARS has a promising prediction power, and does not suffer from interpretation complexity. Negative Binomial (NB) and MARS models were fitted and compared using extensive data collected on unsignalized intersections in Florida.

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.

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.