Search

There have been efforts to restrict older trucks in many jurisdictions all over the world. The primary goal of the restrictions is to minimize greenhouse gas emissions. In addition to the environmental benefits, it is also possible that the truck age restriction could contribute to the enhancement of traffic safety. Older trucks are subject to longer travel-miles than newer trucks and tend to have higher mechanical failure rates. Extremely few studies have been done to explore the impact of trucks' age on their crash occurrence due to mechanical problems. This study aims to investigate the association between the truck crashes due to mechanical issues and the truck age. Two approaches are adopted to achieve the objective. First, a chi-square test reveals that the proportions of the mechanical failures among older trucks are higher than those among newer ones (χ [sup.2]=256.199, p, Author(s): Jaeyoung Lee (corresponding author) [1]; Suyi Mao [1]; Mohamed Abdel-Aty [2]; Yanqi Lian [1]; Lishengsha Yue [2]; Ilsoo Yun [3] 1. Introduction In the recent decades, a number of [...]

Since various freeway design features are simultaneously installed on roadways, it is important to assess their combined safety effects correctly. This study investigated associations between multiple roadway cross-section design features on freeways and traffic safety. In order to consider the interaction impact of multiple design features and nonlinearity of predictors concurrently, multivariate adaptive regression splines (MARS) models were developed for all types and freight vehicle crashes. In MARS models, a series of basis functions is applied to represent the space of predictors and the combined safety effectiveness of multiple design features can be interpreted by the interaction terms. The generalized linear regression models (GLMs) with negative binomial (NB) distribution were also evaluated for comparison purposes. The results determine that the MARS models show better model fitness than the NB models, due to its strength to reflect the nonlinearity of crash predictors and interaction impacts among variables under different ranges. Various interaction impacts among parameters under different ranges based on knot values were found from the MARS models, whereas two interaction terms were found in the NB models. The results also showed that the combined safety effects of multiple treatments from the NB models over-estimated the real combined safety effects when using the simple multiplication approach suggested by the HSM (Highway Safety Manual). Therefore, it can be recommended that the MARS is applied to evaluate the safety impacts of multiple treatments to consider both the interaction impacts among treatments and nonlinearity issues simultaneously., 1. Introduction Traffic safety has become one of the serious global concerns and many countries have taken safety plans and initiatives towards safer roadways. While roadway crashes occur overwhelmingly due [...]

• Visual surroundings on speeding in 15 US cities using real-time dash camera data. • Uses 3 million + driver view images to reveal factors influencing speeding behavior. • Uncover regional variations in speeding, emphasizing local context and the role of the driver's environment. • Identifying interventions and countermeasures based on comprehensive speeding analysis. • Investigates the interplay between time-headway and speeding. Despite awareness campaigns and legal consequences, speeding is a significant cause of road accidents and fatalities globally. To combat this issue, understanding the impact of a driver's visual surroundings is crucial in designing roadways that discourage speeding. This study investigates the influence of visual surroundings on drivers in 15 US cities using 3,407,253 driver view images from Lytx, covering 4,264 miles of roadways. By segmenting and analyzing these images along with vehicle-related variables, the study examines factors affecting speeding behavior. After filtering the images, to ensure an accurate representation of the driver's view, 1,340,035 driver view images were used for analysis. Statistical models, including hurdle beta and bivariate probit models with random driver effects as well as Machine Learning's eXtreme Gradient Boosting (XGBoost), were employed to estimate speeding behavior. The results indicate that factors within the driver's visual environment, weather conditions, and driver heterogeneity significantly impact speeding. Speeding behavior also varies across geographic locations, even within the same city, suggesting a connection between local context and speeding. The study highlights the importance of the driver's environment, showing that more open spaces encourage speeding, while areas with trees and buildings are associated with reduced speeding. Notably, this research differs from previous studies by utilizing real-time data from dash cameras, providing a dynamic and accurate representation of the driver's visual surroundings. This approach enhances the reliability of the findings and empowers transportation engineers and planners to make informed decisions when designing roadways and implementing interventions to address effectively excessive speeding. In addition to examining speeding behavior, the study also analyzes time-headway, a key factor affecting safety and risky driver behavior, to explore its relationship with speeding. The findings offer valuable insights into the factors influencing speeding and the driver's visual environment. These insights can inform efforts to create environments that discourage speeding (and close car following) and ultimately reduce severe accidents caused by excessive speed (and tailgating). [ABSTRACT FROM AUTHOR]

Highlights • The safety-in-numbers effects were investigated in an area with low pedestrian activities. • The safety-in-numbers effects were found only at 32 intersections out of 219. • Intersections with the safety-in-numbers effects have relatively higher pedestrian activities. • Intersections without the safety-in-numbers effects have extremely low pedestrian activities. Abstract In previous studies, the safety-in-numbers effect has been found, which is a phenomenon that when the number of pedestrians or cyclists increases, their crash rates decrease. The previous studies used data from highly populated areas. It is questionable that the safety-in-numbers effect is still observed in areas with a low population density and small number of pedestrians. Thus, this study aims at analyzing pedestrian crashes in a suburban area in the United States and exploring if the safety-in-numbers effect is also observed. We employ a Bayesian random-parameter Poisson-lognormal model to evaluate the safety-in-numbers effects of each intersection, which can account for the heterogeneity across the observations. The results show that the safety-in-numbers effect were found only at 32 intersections out of 219. The intersections with the safety-in-numbers effect have relatively larger pedestrian activities whereas those without the safety-in-numbers effect have extremely low pedestrian activities. It is concluded that just encouraging walking might result in serious pedestrian safety issues in a suburban area without sufficient pedestrian activities. Therefore, it is plausible to provide safe walking environment first with proven countermeasures and a people-oriented policy rather than motor-oriented. After safe walking environments are guaranteed and when people recognize that walking is safe, more people will consider walking for short-distance trips. Eventually, increased pedestrian activities will result in the safety-in-numbers effects and walking will be even further safer. [ABSTRACT FROM AUTHOR]

Road traffic crashes are a major cause of concern in Saudi Arabia. Although the general view of the design and quality of Saudi Arabia's road network meets global standards, there is not enough in-depth analysis of crash locations and contributing factors and their relationship with user characteristics or behaviors. This study aims to investigate, analyze, and examine the safety problems on a main corridor in the Eastern Province and then suggest treatments to enhance the safety and explore the crash-risk factors associated with crashes on that corridor. A multiple survey approach was used to collect data from road users and experts. The survey addresses traffic safety issues and user behaviors on the corridor. To achieve the study aims, one-way analysis, two-way contingency table analysis, and structural equation modeling were applied. The results showed a need for adequate shoulders along the corridor, and the markings, signs, and signal timing should be reevaluated. In addition, greater restrictions on the use of mobile phones while driving are needed. Also television and radio programs are recommended for traffic awareness as reported by participants. Lastly, it showed that the multiple survey approach can be very useful when crash and geometric data are not available or incomplete. [ABSTRACT FROM AUTHOR]

Highlights • An analysis framework is proposed to analyze mandatory-lane-change-related crash risk at freeway interchange merging areas with the consideration of drivers' merging behavior. • A multilevel random parameters logistic regression model is developed to investigate each driver's merging behavior in the acceleration lane. • For the behavior model, driving ability, merging location, merging speed, traffic condition are identified to have significant effect on the merging behavior. • The crash risk increases with the merging vehicle approaching the end of the acceleration lane and being forced to merge. Abstract The interchange merging area suffers a high crash risk in the freeway system, which is greatly related to the intense mandatory merging maneuvers. Ignoring such correlation may result in limited and biased conclusions and inefficient countermeasures. Recently, the availability of unmanned aerial vehicle (UAV) provides us an opportunity to collect individual vehicle's data to conduct traffic analysis at the microscopic level. Hence, this paper contributes to the literature by proposing a new framework to analyze crash risk at freeway interchange merging areas considering drivers' merging behavior. The analysis framework is conducted based on individual vehicle data from UAV videos. A multilevel random parameters logistic regression model is proposed to investigate each driver's merging behavior in the acceleration lane. The model could identify the impact of different factors related to traffic and drivers on the merging behavior. Then, the crash risk between the merging vehicle and surrounding vehicles is calculated by incorporating the time-to-collision (TTC) and the output of the estimated merging behavior's model. The results suggest that the proposed method provides more valuable insights about the crash risk at interchange merging areas by simultaneously considering the merging behavior and the safety measure. It is concluded that the merging speed, driving ability (e.g., lane change confidence, lane-keeping instability), and the merging location can affect the crash risk. These results can help traffic engineers propose efficient countermeasures to enhance the safety of the interchange merging area. The results also have implications to the design of merging areas and the advent of connected vehicles' technology. [ABSTRACT FROM AUTHOR]

Objective: Emergency medical services (EMS) play a vital role in the postcrash effort to reduce fatalities by providing first aid treatment and transportation to medical facilities. This study aims to analyze the time required for crash reporting and EMS arrival in fatal traffic crashes and to explore the effects of EMS time on the traffic injury severity.Methods: The time required for EMS reporting, arrival, and transport to hospital was calculated by location type and roadway functional classification using 2016 Fatality Analysis Reporting System (FARS) data. Subsequently, an ordered probit model was developed to identify factors contributing to injury severity considering EMS time.Results: The average time for the crash-reporting duration is 5.38 min, the reporting-scene arrival interval is 10.52 min, and the scene-hospital interval is 34.72 min. The average crash reporting and reporting-scene arrival intervals were the longest on conventional roads in rural areas and the shortest on conventional roads in urban areas. The average scene-hospital interval was longest in conventional rural areas and the shortest on freeways/expressways in urban areas. The developed random effects ordered probit model shows that prolonged reporting-scene arrival and scene-hospital intervals result in more severe injuries. The result also presents that crash type, violation, age, location, lighting condition, and alcohol/drug involvement have significant effects on injury severity.Conclusions: The key findings from this study indicate that EMS times differ according to urban/rural location and road functional classification and that reporting-scene arrival and scene-hospital intervals have significant effects on injury severity along with various factors. It is expected that the findings from this study can be used to develop effective and practical strategic plans to minimize EMS reporting, arrival time, and transport to hospital and therefore decrease the traffic injury severity. [ABSTRACT FROM AUTHOR]

There have been plenty of traffic safety studies based on average daily traffic (ADT), average hourly traffic (AHT), or microscopic traffic at 5 min intervals. Nevertheless, not enough research has compared the performance of these three types of safety studies, and seldom of previous studies have intended to find whether the results of one type of study is transferable to the other two studies. First, this study built three models: a Bayesian Poisson-lognormal model to estimate the daily crash frequency using ADT, a Bayesian Poisson-lognormal model to estimate the hourly crash frequency using AHT, and a Bayesian logistic regression model for the real-time safety analysis using microscopic traffic. The model results showed that the crash contributing factors found by different models were comparable but not the same. Four variables, i.e., the logarithm of volume, the standard deviation of speed, the logarithm of segment length, and the existence of diverge segment, were positively significant in the three models. Additionally, weaving segments experienced higher daily and hourly crash frequencies than merge and basic segments. Then, each of the ADT-based, AHT-based, and real-time models was used to estimate safety conditions at different levels: daily and hourly, meanwhile, the real-time model was also used in 5 min intervals. The results uncovered that the ADT- and AHT-based safety models performed similar in predicting daily and hourly crash frequencies, and the real-time safety model was able to provide hourly crash frequency. [ABSTRACT FROM AUTHOR]

This paper investigates the effective spatial coverage of nationwide land-based weather stations of the National Oceanic Atmospheric Administration (NOAA) for traffic crash analysis. The weather data were collected from the Quality Controlled Local Climatological Data (QCLCD) and the fatal crashes were obtained from the Fatality Analysis Reporting Systems (FARS) during the year of 2007–2014. Both QCLCD and FARS contain geographic coordinates for locations and weather condition information as a categorical variable. The spatial coverage of weather stations for the analysis was made by geoprocessing, which uses multiple buffers (i.e. radii 5, 10, 15, and 20 miles), and then was evaluated via Cohen's κ statistics, which is used to determine an agreement of weather between QCLCD and FARS within the buffer. The applicability of the weather station's data by nine climate regions was assessed by developing a series of negative binomial models. According to the estimated Cohen's κ statistics, the rain and snow weather conditions have a moderate agreement up to 20 miles. However, in the case of fog weather condition, it has a slight agreement. The statistical modeling results showed that weather stations data can be a good exposure measure for weather-related fatal crashes along with the vehicle-miles-traveled. Considering one geographical feature that approximately more than 75% of all fatal crashes are located within 20-miles radius of the weather stations in the USA, it is evident that the data from the existing weather stations can be cost-effective to develop geospatial crash risk analysis model. [ABSTRACT FROM AUTHOR]

There have been plenty of studies intended to use different methods, for example, empirical Bayes before–after methods, to get accurate estimation of CMFs. All of them have different assumptions toward crash count if there was no treatment. Additionally, another major assumption is that multiple sites share the same true CMF. Under this assumption, the CMF at an individual intersection is randomly drawn from a normally distributed population of CMFs at all intersections. Since CMFs are non-zero values, the population of all CMFs might not follow normal distributions, and even if it does, the true mean of CMFs at some intersections may be different from that at others. Therefore, a bootstrap method based on before–after empirical Bayes theory was proposed to estimate CMFs, but it did not make distributional assumptions. This bootstrap procedure has the added benefit of producing a measure of CMF stability. Furthermore, based on the bootstrapped CMF, a new CMF precision rating method was proposed to evaluate the reliability of CMFs. This study chose 29 urban four-legged intersections as treated sites, and their controls were changed from stop-controlled to signal-controlled. Meanwhile, 124 urban four-legged stop-controlled intersections were selected as reference sites. At first, different safety performance functions (SPFs) were applied to five crash categories, and it was found that each crash category had different optimal SPF form. Then, the CMFs of these five crash categories were estimated using the bootstrap empirical Bayes method. The results of the bootstrapped method showed that signalization significantly decreased Angle + Left-Turn crashes, and its CMF had the highest precision. While, the CMF for Rear-End crashes was unreliable. For KABCO, KABC, and KAB crashes, their CMFs were proved to be reliable for the majority of intersections, but the estimated effect of signalization may be not accurate at some sites. [ABSTRACT FROM AUTHOR]

This study develops various crash modification factors (CMFs) for combinations of traffic and roadway cross-sectional elements at noncurved (i.e., straight) and curved roadway sections using the cross-sectional method. Generally, a CMF estimates the expected changes in crash frequency after a single specific treatment is implemented on a roadway. The results indicate that increasing lane width, increasing shoulder width, and their combination are safety effective in reducing crash frequency in general. In particular, it was found that crash frequency decreases as the lane width increases until 3.51 m (11.5 ft) width and it increases as the lane width exceeds 3.51 m. The crash rates start to decrease again after 3.81 m (12.5 ft). The results also indicate that the developed nonlinearizing link function derived nonlinear relationship between crash rates and lane width, and this relationship was clearly reflected in the generalized nonlinear models (GNMs). Moreover, there is a significant interaction impact between lane and shoulder widths at segments with a horizontal curve whereas the interaction term is not significant at noncurved segments. It was also found that the CMFs for increasing lane and shoulder widths are decreasing as annual average daily traffic (AADT) level increases. Based on the findings from this study, it is recommended to apply the proposed methodology of utilizing the GNMs with interaction terms in the cross-sectional method to (1) estimate more accurate CMFs for single treatments and combinations of treatments and (2) reflect the nonlinearity of crash predictors and interaction impacts among variables. [ABSTRACT FROM AUTHOR]

Introduction Macro-level traffic safety analysis has been undertaken at different spatial configurations. However, clear guidelines for the appropriate zonal system selection for safety analysis are unavailable. In this study, a comparative analysis was conducted to determine the optimal zonal system for macroscopic crash modeling considering census tracts (CTs), state-wide traffic analysis zones (STAZs), and a newly developed traffic-related zone system labeled traffic analysis districts (TADs). Method Poisson lognormal models for three crash types (i.e., total, severe, and non-motorized mode crashes) are developed based on the three zonal systems without and with consideration of spatial autocorrelation. The study proposes a method to compare the modeling performance of the three types of geographic units at different spatial configurations through a grid based framework. Specifically, the study region is partitioned to grids of various sizes and the model prediction accuracy of the various macro models is considered within these grids of various sizes. Results These model comparison results for all crash types indicated that the models based on TADs consistently offer a better performance compared to the others. Besides, the models considering spatial autocorrelation outperform the ones that do not consider it. Conclusions Based on the modeling results and motivation for developing the different zonal systems, it is recommended using CTs for socio-demographic data collection, employing TAZs for transportation demand forecasting, and adopting TADs for transportation safety planning. Practical Applications The findings from this study can help practitioners select appropriate zonal systems for traffic crash modeling, which leads to develop more efficient policies to enhance transportation safety. [ABSTRACT FROM AUTHOR]

There have been great efforts to develop traffic crash prediction models for various types of facilities. The crash models have played a key role to identify crash hotspots and evaluate safety countermeasures. In recent, many macro-level crash prediction models have been developed to incorporate highway safety considerations in the long-term transportation planning process. Although the numerous macro-level studies have found that a variety of demographic and socioeconomic zonal characteristics have substantial effects on traffic safety, few studies have attempted to coalesce micro-level with macro-level data from existing geographic units for estimating crash models. In this study, the authors have developed a series of intersection crash models for total, severe, pedestrian, and bicycle crashes with macro-level data for seven spatial units. The study revealed that the total, severe, and bicycle crash models with ZIP-code tabulation area data performs the best, and the pedestrian crash models with census tract-based data outperforms the competing models. Furthermore, it was uncovered that intersection crash models can be drastically improved by only including random-effects for macro-level entities. Besides, the intersection crash models are even further enhanced by including other macro-level variables. Lastly, the pedestrian and bicycle crash modeling results imply that several macro-level variables (e.g., population density, proportions of specific age group, commuters who walk, or commuters using bicycle, etc.) can be a good surrogate exposure for those crashes. [ABSTRACT FROM AUTHOR]

This study evaluated the safety performance of pavement resurfacing projects on urban arterials in Florida using the observational before and after approaches. The safety effects of pavement resurfacing were quantified in the crash modification factors (CMFs) and estimated based on different ranges of heavy vehicle traffic volume and time changes for different severity levels. In order to evaluate the variation of CMFs over time, crash modification functions (CMFunctions) were developed using nonlinear regression and time series models. The results showed that pavement resurfacing projects decrease crash frequency and are found to be more safety effective to reduce severe crashes in general. Moreover, the results of the general relationship between the safety effects and time changes indicated that the CMFs increase over time after the resurfacing treatment. It was also found that pavement resurfacing projects for the urban roadways with higher heavy vehicle volume rate are more safety effective than the roadways with lower heavy vehicle volume rate. Based on the exploration and comparison of the developed CMFucntions, the seasonal autoregressive integrated moving average (SARIMA) and exponential functional form of the nonlinear regression models can be utilized to identify the trend of CMFs over time. [ABSTRACT FROM AUTHOR]

Due to the difficulty of obtaining accurate real-time visibility and vehicle based traffic data at the same time, there are only few research studies that addressed the impact of reduced visibility on traffic crash risk. This research was conducted based on a new visibility detection system by mounting visibility sensor arrays combined with adaptive learning modules to provide more accurate visibility detections. The vehicle-based detector, Wavetronix SmartSensor HD, was installed at the same place to collect traffic data. Reduced visibility due to fog were selected and analyzed by comparing them with clear cases to identify the differences based on several surrogate measures of safety under different visibility classes. Moreover, vehicles were divided into different types and the vehicles in different lanes were compared in order to identify whether the impact of reduced visibility due to fog on traffic crash risk varies depending on vehicle types and lanes. Log-Inverse Gaussian regression modeling was then applied to explore the relationship between time to collision and visibility together with other traffic parameters. Based on the accurate visibility and traffic data collected by the new visibility and traffic detection system, it was concluded that reduced visibility would significantly increase the traffic crash risk especially rear-end crashes and the impact on crash risk was different for different vehicle types and for different lanes. The results would be helpful to understand the change in traffic crash risk and crash contributing factors under fog conditions. We suggest implementing the algorithms in real-time and augmenting it with ITS measures such as VSL and DMS to reduce crash risk. [ABSTRACT FROM AUTHOR]

Road accidents have the highest externality costs to society and to the economy, even when compared to the externality damages associated with air emissions and oil dependency. Road safety is one of the most complicated topics, which involves many interdependencies, and so, a sufficiently thorough analysis of roadway safety will require a novel system-based approach in which the associated feedback relationships and causal effects are given appropriate consideration. The factors affecting accident frequency and severity are highly dependent on economic parameters, environmental factors and weather conditions. In this study, we try to use a system dynamics modeling approach to model the climate change-road safety-economy nexus, thereby investigating the complex interactions among these important areas by tracking how they affect each other over time. For this purpose, five sub-models are developed to model each aspect of the overall nexus and to interact with each other to simulate the overall system. As a result, this comprehensive model can provide a platform for policy makers to test the effectiveness of different policy scenarios to reduce the negative consequences of traffic accidents and improve road safety. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This research evaluates the safety effectiveness of multiple roadway cross-section elements on urban arterials for different crash types and severity levels. In order to consider the nonlinearity of predictors and obtain more reliable estimates, the generalized nonlinear models (GNMs) were developed using 5-years of crash records and roadway characteristics data for urban roadways in Florida. The generalized linear models (GLMs) were also developed to compare model performance. The cross-sectional method was used to develop crash modification factors (CMFs) for various safety treatments. The results from this paper indicated that increasing lane, bike lane, median, and shoulder widths were safety effective to reduce crash frequency. In particular, the CMFs for changes in median and shoulder widths consistently decreased as their widths increased. On the other hand, the safety effects of increasing lane and bike lane widths showed nonlinear variations. It was found that crash rates decrease as the lane width increases until 12 ft width and it increases as the lane width exceeds 12 ft. The crash rates start to decrease again after 13 ft. It was also found that crash rates decreases as the bike lane width increases until 6 ft width and it increases as the bike lane width exceeds 6 ft. This paper demonstrated that the GNMs clearly captured the nonlinear relationship between crashes and multiple roadway cross-sectional features, which cannot be reflected by the estimated CMFs from the GLMs. Moreover, the GNMs showed better model fitness than GLMs in general. Therefore, in order to estimate more accurate CMFs, the proposed methodology of utilizing the GNMs in the cross-sectional method is recommended over using conventional GLMs when there are nonlinear relationships between the crash rate and roadway characteristics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Weaving segments are potential recurrent bottlenecks which affect the efficiency and safety of expressways during peak hours. Meanwhile, they are one of the most complicated segments, since on- and off-ramp traffic merges, diverges and weaves in the limited space. One effective way to improve the safety of weaving segments is to study crash likelihood using real-time crash data with the objective of, identifying hazardous conditions and reducing the risk of crashes by Intelligent Transportation Systems (ITS) traffic control. This study presents a multilevel Bayesian logistic regression model for crashes at expressway weaving segments using crash, geometric, Microwave Vehicle Detection System (MVDS) and weather data. The results show that the mainline speed at the beginning of the weaving segments, the speed difference between the beginning and the end of weaving segment, logarithm of volume have significant impacts on the crash risk of the following 5–10 min for weaving segments. The configuration is also an important factor. Weaving segment, in which there is no need for on- or off-ramp traffic to change lane, is with high crash risk because it has more traffic interactions and higher speed differences between weaving and non-weaving traffic. Meanwhile, maximum length, which measures the distance at which weaving turbulence no longer has impact, is found to be positively related to the crash risk at the 95% confidence interval. In addition to traffic and geometric factors, wet pavement surface condition significantly increases the crash ratio by 77%. The proposed model along with ITS, e.g., ramp metering, Dynamic Message Sign (DMS), and high friction surface treatment can be used to enhance the safety of weaving segments in real-time. [ABSTRACT FROM AUTHOR]

Since a crash modification factor (CMF) represents the overall safety performance of specific treatments in a single fixed value, there is a need to explore the variation of CMFs with different roadway characteristics among treated sites over time. Therefore, in this study, we (1) evaluate the safety performance of a sample of urban four-lane roadway segments that have been widened with one through lane in each direction and (2) determine the relationship between the safety effects and different roadway characteristics over time. Observational before–after analysis with the empirical Bayes (EB) method was assessed in this study to evaluate the safety effects of widening urban four-lane roadways to six-lanes. Moreover, the nonlinearizing link functions were utilized to achieve better performance of crash modification functions (CMFunctions). The CMFunctions were developed using a Bayesian regression method including the estimated nonlinearizing link function to incorporate the changes in safety effects of the treatment over time. Data was collected for urban arterials in Florida, and the Florida-specific full SPFs were developed and used for EB estimation. The results indicated that the conversion of four-lane roadways to six-lane roadways resulted in a crash reduction of 15 percent for total crashes, and 24 percent for injury crashes on urban roadways. The results show that the safety effects vary across the sites with different roadway characteristics. In particular, LOS changes, time changes, and shoulder widths are significant parameters that affect the variation of CMFs. Moreover, it was found that narrowing shoulder and median widths to make space for an extra through lane shows a negative safety impact. It was also found that including the nonlinearizing link functions in developing CMFunctions shows more reliable estimates, if the variation of CMFs with specific parameters has a nonlinear relationship. The findings provide insights into the selection of roadway sites for adding through lanes. [ABSTRACT FROM AUTHOR]

Macroscopic traffic crash analyses have been conducted to incorporate traffic safety into long-term transportation planning. This study aims at developing a multivariate Poisson lognormal conditional autoregressive model at the macroscopic level for crashes by different transportation modes such as motor vehicle, bicycle, and pedestrian crashes. Many previous studies have shown the presence of common unobserved factors across different crash types. Thus, it was expected that adopting multivariate model structure would show a better modeling performance since it can capture shared unobserved features across various types. The multivariate model and univariate model were estimated based on traffic analysis zones (TAZs) and compared. It was found that the multivariate model significantly outperforms the univariate model. It is expected that the findings from this study can contribute to more reliable traffic crash modeling, especially when focusing on different modes. Also, variables that are found significant for each mode can be used to guide traffic safety policy decision makers to allocate resources more efficiently for the zones with higher risk of a particular transportation mode. [ABSTRACT FROM AUTHOR]

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

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. [ABSTRACT FROM AUTHOR]

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. The results of both before–after and cross-sectional methods show that adding a bike lane on urban arterials has positive safety effects (i.e., CMF < 1) for all crashes and bike crashes. It was found that adding a bike lane is more effective in reducing bike crashes than all crashes. It was also found that the CMFs vary across the sites with different roadway characteristics. In particular, annual average daily traffic (AADT), number of lanes, AADT per lane, median width, bike lane width, and lane width are significant characteristics that affect the variation in safety effects of adding a bike lane. Some socio-economic characteristics such as bike commuter rate and population density also have significant effect on the variation in CMFs. The findings suggest that full CMFunctions showed better model fit than simple CMFuncttions since they account for the heterogeneous effects of multiple roadway and socio-economic characteristics. The proposed CMFunctions provide insights into bike lane design and selection of sites for bike lane installation for reducing crashes. [ABSTRACT FROM AUTHOR]

Red light cameras may have a demonstrable impact on reducing the frequency of red light running violations; however, their effect on the overall safety at intersections is still up for debate. This paper examined the safety impacts of Red Light Cameras (RLCs) on traffic crashes at signalized intersections using the Empirical Bayes (EB) method. Data were obtained from the Florida Department of Transportation for twenty-five RLC equipped intersections in Orange County, Florida. Additional fifty intersections that remained with no photo enforcement in the vicinity of the treated sites were collected to examine the spillover effects on the same corridors. The safety evaluation was performed at three main levels; only target approaches where RLCs were installed, all approaches on RLC intersections, and non-RLC intersections located on the same travel corridors as the camera equipped intersections. Moreover, the spatial spillover effects of RLCs were also examined on an aggregate level to evaluate the safety impacts on driver behavior at a regional scale. The results from this study indicated that there was a consistent significant reduction in angle and left-turn crashes and a significant increase in rear-end crashes on target approaches, in addition, the magnitude and the direction of these effects, to a lesser degree, were found similar on the whole intersection. Similar trends in shift of crash types were spilled-over to non-RLC intersections in the proximity of the treated sites. On an aggregate county level, there was a moderate spillover benefits with a notable crash migration to the boundary of the county. [ABSTRACT FROM AUTHOR]

Highlights: [•] Residence characteristics of drivers who caused traffic crashes were investigated. [•] Bayesian Poisson-lognormal model was adopted in this study. [•] Population and commute mode were related with the number of at-fault drivers. [•] Age, commute time, and income were associated with the number of at-fault drivers. [•] Findings from the study can identify residence areas with risky drivers. [Copyright &y& Elsevier]

Minority status and low-income are two important criteria on which environmental-justice areas are being identified. These unique areal characteristics have been of particular interest to traffic safety analysts in investigating pedestrian safety planning factors in deprived areas. In this study, a pedestrian crash model was developed for these areas explicitly accounting for the spatial autocorrelations of different zonal factors commonly used in the traditional transportation planning models. It was found that the Bayesian Poisson-lognormal model with a spatial effect term performed the best compared to the models without accounting for spatial autocorrelations. [ABSTRACT FROM AUTHOR]

Highlights: [•] The results of both methods show that safety effects were higher for the roadway segments with three treatments. [•] The CMFs were more effective for the roadway segments with narrower original shoulder width in the before period. [•] Method 1, 2, 5 and 6 showed less difference between actual and estimated CMFs for different crash and injury types. [•] The findings suggest that the existing methods need to be applied for different crash types and injury levels separately. [ABSTRACT FROM AUTHOR]

Highlights: [•] A novel visible network method has been adopted to analyze crashes with real-time data. [•] Combinations of crash occurrence contributing factors can be identified with high accuracy. [•] Compared with conventional approaches, significant advantages of the proposed method have been achieved. [ABSTRACT FROM AUTHOR]

Highlights: [•] We present aggregate and disaggregate analyses for single- and multi-vehicle crashes. [•] Two different Bayesian hierarchical models were developed for crash frequency data. [•] Disaggregate models were developed for single- and multi-vehicle crashes separately. [•] Hierarchical logistic regression models were used to address unobserved heterogeneit. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: This study provides a systematic approach to investigate the different characteristics of weekday and weekend crashes. Method: Weekend crashes were defined as crashes occurring between Friday 9p.m. and Sunday 9p.m., while the other crashes were labeled as weekday crashes. In order to reveal the various features for weekday and weekend crashes, multi-level traffic safety analyses have been conducted. For the aggregate analysis, crash frequency models have been developed through Bayesian inference technique; correlation effects of weekday and weekend crash frequencies have been accounted. A multivariate Poisson model and correlated random effects Poisson model were estimated; model goodness-of-fits have been compared through DIC values. In addition to the safety performance functions, a disaggregate crash time propensity model was calibrated with Bayesian logistic regression model. Moreover, in order to account for the cross-section unobserved heterogeneity, random effects Bayesian logistic regression model was employed. Results: It was concluded that weekday crashes are more probable to happen during congested sections, while the weekend crashes mostly occur under free flow conditions. Finally, for the purpose of confirming the aforementioned conclusions, real-time crash prediction models have been developed. Random effects Bayesian logistic regression models incorporating the microscopic traffic data were developed. Results of the real-time crash prediction models are consistent with the crash time propensity analysis. Furthermore, results from these models would shed some lights on future geometric improvements and traffic management strategies to improve traffic safety. Impact on Industry: Utilizing safety performance to identify potential geometric improvements to reduce crash occurrence and monitoring real-time crash risks to pro-actively improve traffic safety. [Copyright &y& Elsevier]

Abstract: Real-time crash risk evaluation models will likely play a key role in Active Traffic Management (ATM). Models have been developed to predict crash occurrence in order to proactively improve traffic safety. Previous real-time crash risk evaluation studies mainly employed logistic regression and neural network models which have a linear functional form and over-fitting drawbacks, respectively. Moreover, these studies mostly focused on estimating the models but barely investigated the models’ predictive abilities. In this study, support vector machine (SVM), a recently proposed statistical learning model was introduced to evaluate real-time crash risk. The data has been split into a training dataset (used for developing the models) and scoring datasets (meant for assessing the models’ predictive power). Classification and regression tree (CART) model has been developed to select the most important explanatory variables and based on the results, three candidates Bayesian logistic regression models have been estimated with accounting for different levels unobserved heterogeneity. Then SVM models with different kernel functions have been developed and compared to the Bayesian logistic regression model. Model comparisons based on areas under the ROC curve (AUC) demonstrated that the SVM model with Radial-basis kernel function outperformed the others. Moreover, several extension analyses have been conducted to evaluate the effect of sample size on SVM models’ predictive capability; the importance of variable selection before developing SVM models; and the effect of the explanatory variables in the SVM models. Results indicate that (1) smaller sample size would enhance the SVM model''s classification accuracy, (2) variable selection procedure is needed prior to the SVM model estimation, and (3) explanatory variables have identical effects on crash occurrence for the SVM models and logistic regression models. [Copyright &y& Elsevier]

Abstract: The present study aims at identifying and quantifying significant factors (i.e., demographic, aberrant driving behavior) associated with young drivers’ involvement in at-fault crashes or traffic citations at the ages of 16–17 (while having the Operational License) and 18–24 years old (while having the Full License). A second objective was to investigate the main reason(s) for involvement in risky driving behavior by young drivers. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 680 young drivers in Central Florida. To achieve these goals, the structural equation modeling approach was adopted. The results revealed that aggressive violations, in-vehicle distractions and demographic characteristics were the significant factors affecting young drivers’ involvement in at-fault crashes or traffic violations at the age of 16–17. However, in-vehicle distractions, attitudes toward speeding and demographic characteristics were the significant factors affecting young drivers’ crash risk at 18–24. Additionally, the majority of participants reported that “running late” is the main reason for taking risk while driving (i.e., speeding, accept short gaps, or drive so close to the car in front) followed by “racing other cars”. Additionally, “exceed speed limits” was the main reason for receiving traffic citations at 16–17 and 18–24 age groups. Practical suggestions on how to reduce crash risk and promote safe driving among young drivers are also discussed. [Copyright &y& Elsevier]

Abstract: More researchers started using real-time traffic surveillance data, collected from loop/radar detectors (LDs), for proactive crash risk assessment. However, there is a lack of prior studies that investigated the link between real-time traffic data and crash risk of reduced visibility related (VR) crashes. Two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on Expressways and (2) which traffic data are advantageous for predicting VR crashes; LDs or AVIs. Thus, this study attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two Freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two Expressways (SR 408 and SR 417). Also, it investigates which data are better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression models using the historical crashes, LDs and AVI data. Regarding the model estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5–10min prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5–10min prior to the crash time, affected the likelihood of VR crash occurrence. The results showed that both LDs and AVI systems can be used for safety application (i.e., predicting VR crashes). It was found that up to 73% of VR crashes could be identified correctly. Argument concerning which traffic data (LDs or AVI) are better for predicting VR crashes is also provided and discussed. [Copyright &y& Elsevier]

The article presents studies on the association of analyzing Florida's access management and traffic safety to increase awareness on safety deficiencies and determine the correct countermeasure. According to a study carried out by the Florida Department of Transportation, cutting down the median openings along streets reduces crash rate. A study made by Gluck et al showed there was less 18 to 22 percent in crash rate by changing left turns from driveways with right turns followed by U-turns.

Abstract: Analysis of both the crash count and the severity of injury are required to provide the complete picture of the safety situation of any given roadway. The randomness of crashes, the one-way dependency of injury on crash occurrence and the difference in response types have typically led researchers into developing independent statistical models for crash count and severity classification. The Genetic Programming (GP) methodology adopts the concepts of evolutionary biology such as crossover and mutation in effectively giving a common heuristic approach to model the development for the two different modeling objectives. The chosen GP models have the highest hit rate for rear-end crash classification problem and the least error for function fitting (regression) problems. Higher Average Daily Traffic (ADT) is more likely to result in more crashes. Absence of on-street parking may result in diminished severity of injuries resulting from crashes as they may provide “soft” crash barrier in contrast to fixed road side objects. Graphical presentation of the frequency of crashes with varying input variables shed new light on the results and its interpretation. Higher friction coefficient of roadways result in reduced frequency of crashes during the morning peak hours, with the trend being reversed during the afternoon peak hours. Crash counts have been observed to be at a maximum at a surface width of 30ft. Sensitivity analysis results reflect that ADT is responsible for the largest variation in crash counts on urban arterials. [Copyright &y& Elsevier]

Abstract: Research on weather effects has focused on snow- or rain-related crashes. However, there is a lack of understanding of crashes that occur during fog or smoke (FS). This study presents a comprehensive examination of FS-related crashes using crash data from Florida between 2003 and 2007. A two-stage research strategy was implemented (1) to examine FS-related crash characteristics with respect to temporal distribution, influential factors and crash types and (2) to estimate the effects of various factors on injury severity given that a FS-related crash has occurred. The morning hours from December to February are the prevalent times for FS-related crashes. Compared to crashes under clear-visibility conditions, FS-related crashes tend to result in more severe injuries and involve more vehicles. Head-on and rear-end crashes are the two most common crash types in terms of crash risk and severity. These crashes were more prevalent on high-speed roads, undivided roads, roads with no sidewalks and two-lane rural roads. Moreover, FS-related crashes were more likely to occur at night without street lighting, leading to more severe injuries. [Copyright &y& Elsevier]

This study investigates the effects of the traffic flow parameters related to individual drivers' lane-changing and car-following behavior on the occurrence of sideswipe and rear-end crashes on freeways. A total of 184 sideswipe and 605 rear-end crashes on I-4 freeway in Orlando, Florida, with the loop detector data were used for the analysis. A set of the Bayesian logistic regression models were developed to estimate the likelihood of crashes in a specific lane compared to the adjacent lanes using the 5 min average and lane-by-lane traffic flow parameters. The analysis results showed that the significant traffic flow parameters affecting crash occurrence in a specific lane are distinctively different between sideswipe and rear-end crashes. The flow-related variables were significant in the sideswipe crash models whereas the speed-related variables were significant in the rear-end crash models. The results suggest that the lane-by-lane traffic flow parameters are considered as surrogate measures of lane-changing and car-following behavior that are associated with the occurrence of sideswipe and rear-end crashes. These parameters can potentially be used for real-time monitoring of the likelihood of crash occurrence by lane on instrumented freeways. [ABSTRACT FROM AUTHOR]

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

Abstract: Background: Traditional crash prediction models, such as generalized linear regression model, are incapable of taking into account multilevel data structure. Therefore they suffer from a common underlying limitation that each observation (e.g. a crash or a vehicle involvement) in the estimation procedure corresponds to an individual situation in which the residuals exhibit independence. Problem: However, this “independence” assumption may often not hold true since multilevel data structures exist extensively because of the traffic data collection and clustering process. Disregarding the possible within-group correlations may lead to production of models with unreliable parameter estimates and statistical inferences. Proposed theory: In this paper, a 5×ST-level hierarchy is proposed to represent the general framework of multilevel data structures in traffic safety, i.e. [Geographic region level−Traffic site level−Traffic crash level−Driver-vehicle unit level−Occupant level]×Spatiotemporal level. The involvement and emphasis for different sub-groups of these levels depend on different research purposes and also rely on the heterogeneity examination on crash data employed. To properly accommodate the potential cross-group heterogeneity and spatiotemporal correlation due to the multilevel data structure, a Bayesian hierarchical approach that explicitly specifies multilevel structure and reliably yields parameter estimates is introduced and recommended. Case studies: Using Bayesian hierarchical models, the results from several case studies are highlighted to show the improvements on model fitting and predictive performance over traditional models by appropriately accounting for the multilevel data structure. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: This study presents a classification tree based alternative to crash frequency analysis for analyzing crashes on mid-block segments of multilane arterials. Method: The traditional approach of modeling counts of crashes that occur over a period of time works well for intersection crashes where each intersection itself provides a well-defined unit over which to aggregate the crash data. However, in the case of mid-block segments the crash frequency based approach requires segmentation of the arterial corridor into segments of arbitrary lengths. In this study we have used random samples of time, day of week, and location (i.e., milepost) combinations and compared them with the sample of crashes from the same arterial corridor. For crash and non-crash cases, geometric design/roadside and traffic characteristics were derived based on their milepost locations. The variables used in the analysis are non-event specific and therefore more relevant for roadway safety feature improvement programs. First classification tree model is a model comparing all crashes with the non-crash data and then four groups of crashes (rear-end, lane-change related, pedestrian, and single-vehicle/off-road crashes) are separately compared to the non-crash cases. The classification tree models provide a list of significant variables as well as a measure to classify crash from non-crash cases. ADT along with time of day/day of week are significantly related to all crash types with different groups of crashes being more likely to occur at different times. Conclusions: From the classification performance of different models it was apparent that using non-event specific information may not be suitable for single vehicle/off-road crashes. Impact on Industry: The study provides the safety analysis community an additional tool to assess safety without having to aggregate the corridor crash data over arbitrary segment lengths. [ABSTRACT FROM AUTHOR]

Abstract: 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. [ABSTRACT FROM AUTHOR]

This study was conducted to examine the ability of two ramp metering strategies at reducing the real-time crash risk along a typical urban freeway. The strategies were tested using a simulated freeway network of Interstate-4 in Orlando, Fla. created in PARAMICS microsimulation. Measurements of the crash risk along the freeway were estimated using models created by the first writer which calculate rear-end and lane-change risks along the freeway using offline geometric characteristics and real-time loop detector data. The ramp metering strategies tested were the uncoordinated ALINEA algorithm and the coordinated Zone ramp metering algorithm. Additionally, two implementation methods of these algorithms were examined: the traffic-cycle realization which allows vehicles to enter the network in platoons and the one-car-per-cycle (OCPC) realization which allows a single vehicle to enter the freeway per traffic cycle. This study shows that the ALINEA and Zone algorithms both successfully reduce the real-time crash risk along the freeway. Comparing the two implementation methods, the traffic-cycle realization provides better safety and operational benefits than the OCPC realization. However, the OCPC realization provides better safety benefits when applied with the ALINEA algorithm. In general, the ALINEA algorithm performs better using shorter cycle lengths while the Zone algorithm performs best using longer cycle lengths. Comparing the two metering algorithms, the ALINEA algorithm proves to be superior to the Zone algorithm in providing the best overall safety benefits since it is more restrictive and generally allows fewer vehicles onto the network. However, at the 90% loading scenario, evidence of crash risk migration (the lowering of crash risk at one location combined with the increase at another) appears when the ALINEA algorithm is applied. This crash risk migration is a function of the specific geometry and demand patterns of the network used and is reduced by applying the Zone algorithm. This shows that while the ALINEA algorithm provides better overall results, there are cases where a less restrictive algorithm would perform better from a safety perspective. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

The article presents a study which examines the concept of intersection safety influence area to develop a reliable method of determining intersection-related traffic crashes for accurate representation of intersection's safety. It made use of a nationwide survey related to intersection safety management. The study reveals the importance of a safety influence area concept for data recording of signalized intersection crashes, and safety analysis particularly in the U.S.

Abstract: This study presents a novel approach for analysis of patterns in severe crashes that occur on mid-block segments of multilane highways with partially limited access. A within stratum matched crash vs. non-crash classification approach is adopted towards that end. Under this approach crashes serve as units of analysis and it does not require aggregation of crash data over arterial segments of arbitrary lengths. Also, the proposed approach does not use information on non-severe crashes and hence is not affected by under-reporting of the minor crashes. Random samples of time, day of week, and location (i.e., milepost) combinations were collected for multilane arterials in the state of Florida and matched with severe crashes from the corresponding corridor to form matched strata consisting of severe crash and non-crash cases. For these cases, geometric design/roadside and traffic characteristics were derived based on the corresponding milepost locations. Four groups of crashes, severe rear-end, lane-change related, pedestrian, and single-vehicle/off-road crashes, on multilane arterials segments were compared separately to the non-crash cases. Severe lane-change related crashes may primarily be attributed to exposure while single-vehicle crashes and pedestrian crashes have no significant relationship with the ADT (Average Daily Traffic). For severe rear-end crashes speed limit, ADT, K-factor, time of day/day of week, median type, pavement condition, and presence of horizontal curvature were significant factors. The proposed approach uses general roadway characteristics as independent variables rather than event-specific information (i.e., crash characteristics such as driver/vehicle details); it has the potential to fit within a safety evaluation framework for arterial segments. [Copyright &y& Elsevier]

Abstract: Introduction: The study aims at identifying traffic/highway design/driver-vehicle information significantly related with fatal/severe crashes on urban arterials for different crash types. Since the data used in this study are observational (i.e., collected outside the purview of a designed experiment), an information discovery approach is adopted for this study. Method: Random Forests, which are ensembles of individual trees grown by CART (Classification and Regression Tree) algorithm, are applied in numerous applications for this purpose. Specifically, conditional inference forests have been implemented. In each tree of the conditional inference forest, splits are based on how good the association is. Chi-square test statistics are used to measure the association. Apart from identifying the variables that improve classification accuracy, the methodology also clearly identifies the variables that are neutral to accuracy, and also those that decrease it. Results: The methodology is quite insightful in identifying the variables of interest in the database (e.g., alcohol/ drug use and higher posted speed limits contribute to severe crashes). Failure to use safety equipment by all passengers and presence of driver/passenger in the vulnerable age group (more than 55 years or less than 3 years) increased the severity of injuries given a crash had occurred. A new variable, ‘element’ has been used in this study, which assigns crashes to segments, intersections, or access points based on the information from site location, traffic control, and presence of signals. Impact: The authors were able to identify roadway locations where severe crashes tend to occur. For example, segments and access points were found to be riskier for single vehicle crashes. Higher skid resistance and k-factor also contributed toward increased severity of injuries in crashes. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Crashes that occur on freeway ramps and in the vicinity of ramps were analyzed using a variety of statistical methods. The data used in this study are the 6 year records of crashes that occurred on 98 ramps of a 25 mile section of Interstate-4 freeway in the Orlando, Florida, metro area. Log linear models were used to identify the relationship among the frequency of ramp crashes, ramp geometry, and ramp traffic volume. Binary logit models and bivariate probit models were developed to observe the propensity of specific type of ramp crashes in terms of curvature of ramps, number of vehicles involved in crashes, and collision type. The binary logit model was also used to identify the factors correlated with injury severity of drivers involved in ramp crashes. The results of model estimates showed that crash characteristics differ by the location of crashes with respect to ramps: ramp related and ramp intersection related crashes. It was found that the presence of advisory speed signs potentially reduces the likelihood of the occurrence of crashes on off ramps and rear-end crashes on ramps. Finally, it was found that female drivers, single vehicle crashes, and shorter ramp length are correlated with higher probability of fatal and severe crashes on ramps. The findings suggest some important factors closely related to ramp crash risk and hidden relationships that have not been identified in the past. [ABSTRACT FROM AUTHOR]