Comparative analysis of zonal systems for macro-level crash modeling

IntroductionMacro-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).MethodPoisson 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.ResultsThese 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.ConclusionsBased 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 ApplicationsThe 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.     

A conceptual framework to understand the role of built environment on traffic safety

IntroductionMany U.S. cities have adopted the Vision Zero strategy with the specific goal of eliminating traffic-related deaths and injuries. To achieve this ambitious goal, safety professionals have increasingly called for the development of a safe systems approach to traffic safety. This approach calls for examining the macrolevel risk factors that may lead road users to engage in errors that result in crashes. This study explores the relationship between built environment variables and crash frequency, paying specific attention to the environmental mediating factors, such as traffic exposure, traffic conflicts, and network-level speed characteristics. Methods: Three years (2011–2013) of crash data from Mecklenburg County, North Carolina, were used to model crash frequency on surface streets as a function of built environment variables at the census block group level. Separate models were developed for total and KAB crashes (i.e., crashes resulting in fatalities (K), incapacitating injuries (A), or non-incapacitating injuries (B)) using the conditional autoregressive modeling approach to account for unobserved heterogeneity and spatial autocorrelation present in data. Results: Built environment variables that are found to have positive associations with both total and KAB crash frequencies include population, vehicle miles traveled, big box stores, intersections, and bus stops. On the other hand, the number of total and KAB crashes tend to be lower in census block groups with a higher proportion of two-lane roads and a higher proportion of roads with posted speed limits of 35 mph or less. Conclusions: This study demonstrates the plausible mechanism of how the built environment influences traffic safety. The variables found to be significant are all policy-relevant variables that can be manipulated to improve traffic safety. Practical Applications: The study findings will shape transportation planning and policy level decisions in designing the built environment for safer travels.     

Can post encroachment time substitute intersection characteristics in crash prediction models?

IntroductionTransportation safety analyses have traditionally relied on crash data. The limitations of these crash data in terms of timeliness and efficiency are well understood and many studies have explored the feasibility of using alternative surrogate measures for evaluation of road safety. Surrogate safety measures have the potential to estimate crash frequency, while requiring reduced data collection efforts relative to crash data based measures. Traditional crash prediction models use factors such as traffic volume, sight distance, and grade to make risk and exposure estimates that are combined with observed crashes, generally using an Empirical Bayes method, to obtain a final crash estimate. Many surrogate measures have the notable advantage of not directly requiring historical crash data from a site to estimate safety. Post Encroachment Time (PET) is one such measure and represents the time difference between a vehicle leaving the area of encroachment and a conflicting vehicle entering the same area. The exact relationship between surrogate measures, such as PET, and crashes in an ongoing research area.MethodThis paper studies the use of PET to estimate crashes between left-turning vehicles and opposing through vehicles for its ability to predict opposing left-turn crashes. By definition, a PET value of 0 implies the occurrence of a crash and the closer the value of PET is to 0, the higher the conflict risk.ResultsThis study shows that a model combining PET and traffic volume characteristic (AADT or conflicting volume) has better predictive power than PET alone. Further, it was found that PET may be capturing the impact of certain other intersection characteristics on safety as inclusion of other intersection characteristics such as sight distance, grade, and other parameters result in only marginal impacts on predictive capacity that do not justify the increased model complexity.     

A classification tree based modeling approach for segment related crashes on multilane highways

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.     

Identifying factors affecting the safety of mid-block bicycle lanes considering mixed 2-wheeled traffic flow

Objective: Electric bikes (e-bikes) have been one of the fastest growing trip modes in Southeast Asia over the past 2 decades. The increasing popularity of e-bikes raised some safety concerns regarding urban transport systems. The primary objective of this study was to identify whether and how the generalized linear regression model (GLM) could be used to relate cyclists' safety with various contributing factors when riding in a mid-block bike lane. The types of 2-wheeled vehicles in the study included bicycle-style electric bicycles (BSEBs), scooter-style electric bicycles (SSEBs), and regular bicycles (RBs).

Methods: Traffic conflict technology was applied as a surrogate measure to evaluate the safety of 2-wheeled vehicles. The safety performance model was developed by adopting a generalized linear regression model for relating the frequency of rear-end conflicts between e-bikes and regular bikes to the operating speeds of BSEBs, SSEBs, and RBs in mid-block bike lanes.

Results: The frequency of rear-end conflicts between e-bikes and bikes increased with an increase in the operating speeds of e-bikes and the volume of e-bikes and bikes and decreased with an increase in the width of bike lanes. The large speed difference between e-bikes and bikes increased the frequency of rear-end conflicts between e-bikes and bikes in mid-block bike lanes. A 1% increase in the average operating speed of e-bikes would increase the expected number of rear-end conflicts between e-bikes and bikes by 1.48%. A 1% increase in the speed difference between e-bikes and bikes would increase the expected number of rear-end conflicts between e-bikes/bikes by 0.16%.

Conclusions: The conflict frequency in mid-block bike lanes can be modeled using generalized linear regression models. The factors that significantly affected the frequency of rear-end conflicts included the operating speeds of e-bikes, the speed difference between e-bikes and regular bikes, the volume of e-bikes, the volume of bikes, and the width of bike lanes. The safety performance model can help better understand the causes of crash occurrences in mid-block bike lanes.     

Investigating the different characteristics of weekday and weekend crashes

IntroductionThis study provides a systematic approach to investigate the different characteristics of weekday and weekend crashes.MethodWeekend crashes were defined as crashes occurring between Friday 9 p.m. and Sunday 9 p.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.ResultsIt 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 IndustryUtilizing safety performance to identify potential geometric improvements to reduce crash occurrence and monitoring real-time crash risks to pro-actively improve traffic safety.     

Built environment effects on bike crash frequency and risk in Beijing

IntroductionBuilding a safe biking environment is crucial to encouraging bicycle use. In developed areas with higher density and more mixed land use, the built environment factors that pose a crash risk may vary. This study investigates the connection between biking risk factors and the compact built environment, using data for Beijing.MethodIn the context of China, this paper seeks to answer two research questions. First, what types of built environment factors are correlated with bike-automobile crash frequency and risk? Second, how do risk factors vary across different types of bikes? Poisson lognormal random effects models are employed to examine how land use and roadway design factors are associated with the bike-automobile crashes.ResultsThe main findings are: (1) bike-automobile crashes are more likely to occur in densely developed areas, which is characterized by higher population density, more mixed land use, denser roads and junctions, and more parking lots; (2) areas with greater ground transit are correlated with more bike-automobile crashes and higher risks of involving in collisions; (3) the percentages of wider streets show negative associations with bike crash frequency; (4) built environment factors cannot help explain factors contributing to motorcycle-automobile crashes.Practical ApplicationsIn China's dense urban context, important policy implications for bicycle safety improvement drawn from this study include: prioritizing safety programs in urban centers, applying safety improvements to areas with more ground transit, placing bike-automobile crash countermeasures at road junctions, and improving bicycle safety on narrower streets.     

Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data

IntroductionDriving environment, including road surface conditions and traffic states, often changes over time and influences crash probability considerably. It becomes stretched for traditional crash frequency models developed in large temporal scales to capture the time-varying characteristics of these factors, which may cause substantial loss of critical driving environmental information on crash prediction.MethodCrash prediction models with refined temporal data (hourly records) are developed to characterize the time-varying nature of these contributing factors. Unbalanced panel data mixed logit models are developed to analyze hourly crash likelihood of highway segments. The refined temporal driving environmental data, including road surface and traffic condition, obtained from the Road Weather Information System (RWIS), are incorporated into the models.ResultsModel estimation results indicate that the traffic speed, traffic volume, curvature and chemically wet road surface indicator are better modeled as random parameters. The estimation results of the mixed logit models based on unbalanced panel data show that there are a number of factors related to crash likelihood on I-25. Specifically, weekend indicator, November indicator, low speed limit and long remaining service life of rutting indicator are found to increase crash likelihood, while 5-am indicator and number of merging ramps per lane per mile are found to decrease crash likelihood.ConclusionsThe study underscores and confirms the unique and significant impacts on crash imposed by the real-time weather, road surface, and traffic conditions. With the unbalanced panel data structure, the rich information from real-time driving environmental big data can be well incorporated.     

National evaluation of the effect of graduated driver licensing laws on teenager fatality and injury crashes

ProblemAutomobile crashes remain a prominent cause of death and injury for teenagers in the United States. While it is generally agreed that graduated drivers licensing (GDL) influences crash rates, it is unclear which components have the strongest effect on any specific types of crashes.MethodWe analyze the relative effect of different stages of GDL on teenage fatal and injury crash risk via a negative binomial generalized linear model with random state effects. Overall, nighttime, and crashes with multiple teenage passengers are considered.ResultsThe strongest effects are seen by 16-year-olds, for which a strict permit stage is associated with a 58% reduction in fatal crash risk over a lenient permit stage. Similar reductions are seen for injury crashes. The intermediate stage, involving nighttime and passenger restrictions, is associated with a 44% reduction in fatalities but has relatively little effect on injury crashes. The strongest effects are generally seen for passenger crashes, followed by nighttime, and then overall crashes.Impact on IndustryThis study identifies stronger relationships between GDL and crash risk than has previously been discovered and captures the relative effects of permit and intermediate licensing restrictions, two high-level components of GDL which differ in intent and implementation.     

Development of a method to rate the primary safety of vehicles using linked New Zealand crash and vehicle licensing data

Objective: Vehicle safety rating systems aim firstly to inform consumers about safe vehicle choices and, secondly, to encourage vehicle manufacturers to aspire to safer levels of vehicle performance. Primary rating systems (that measure the ability of a vehicle to assist the driver in avoiding crashes) have not been developed for a variety of reasons, mainly associated with the difficult task of disassociating driver behavior and vehicle exposure characteristics from the estimation of crash involvement risk specific to a given vehicle. The aim of the current study was to explore different approaches to primary safety estimation, identifying which approaches (if any) may be most valid and most practical, given typical data that may be available for producing ratings.

Methods: Data analyzed consisted of crash data and motor vehicle registration data for the period 2003 to 2012: 21,643,864 observations (representing vehicle-years) and 135,578 crashed vehicles. Various logistic models were tested as a means to estimate primary safety: Conditional models (conditioning on the vehicle owner over all vehicles owned); full models not conditioned on the owner, with all available owner and vehicle data; reduced models with few variables; induced exposure models; and models that synthesised elements from the latter two models.

Results: It was found that excluding young drivers (aged 25 and under) from all primary safety estimates attenuated some high risks estimated for make/model combinations favored by young people. The conditional model had clear biases that made it unsuitable. Estimates from a reduced model based just on crash rates per year (but including an owner location variable) produced estimates that were generally similar to the full model, although there was more spread in the estimates. The best replication of the full model estimates was generated by a synthesis of the reduced model and an induced exposure model.

Conclusions: This study compared approaches to estimating primary safety that could mimic an analysis based on a very rich data set, using variables that are commonly available when registered fleet data are linked to crash data. This exploratory study has highlighted promising avenues for developing primary safety rating systems for vehicle makes and models.     

Correlation between crash avoidance maneuvers and injury severity sustained by motorcyclists in single-vehicle crashes

Objectives: In order to improve motorcycle safety, this article examines the correlation between crash avoidance maneuvers and injury severity sustained by motorcyclists, under multiple precrash conditions. Method: Ten-year crash data for single-vehicle motorcycle crashes from the General Estimates Systems (GES) were analyzed, using partial proportional odds models (i.e., generalized ordered logit models). Results: The modeling results show that “braking (no lock-up)” is associated with a higher probability of increased severity, whereas “braking (lock-up)” is associated with a higher probability of decreased severity, under all precrash conditions. “Steering” is associated with a higher probability of reduced injury severity when other vehicles are encroaching, whereas it is correlated with high injury severity under other conditions. “Braking and steering” is significantly associated with a higher probability of low severity under “animal encounter and object presence,” whereas it is surprisingly correlated with high injury severity when motorcycles are traveling off the edge of the road. The results also show that a large number of motorcyclists did not perform any crash avoidance maneuvers or conducted crash avoidance maneuvers that are significantly associated with high injury severity. Conclusions: In general, this study suggests that precrash maneuvers are an important factor associated with motorcyclists' injury severity. To improve motorcycle safety, training/educational programs should be considered to improve safety awareness and adjust driving habits of motorcyclists. Antilock brakes and such systems are also promising, because they could effectively prevent brake lock-up and assist motorcyclists in maneuvering during critical conditions. This study also provides valuable information for the design of motorcycle training curriculum.     

The influence of rear turn-signal characteristics on crash risk

IntroductionThe relationship between the relative risk of a rear-end collision during a turn, merge, or lane change maneuver and the characteristics of the rear turn-signal configuration was examined using crash data from seven states in the United States.MethodRear turn-signal characteristics—including color, optics, separation, and light source—were identified for 55 vehicle models and used in a logistic regression analysis to model the odds of a rear-end collision. Additional variables including driver demographics (gender, age), vehicle age, and light condition were also modeled. Risk was assessed using a contrast group of striking vehicles in similar collisions.ResultsThe results suggest that the odds of being the struck vehicle were 3% to 28% lower among vehicles equipped with amber versus red turn signals. Although the analysis suggests that there may be a safety benefit associated with amber rear turn signals, it is unclear whether turn-signal color alone is responsible.Impact on IndustryThe results suggest that aspects of a vehicle's rear signal characteristics may influence crash risk.     

Analyses of rear-end crashes based on classification tree models

OBJECTIVE: Signalized intersections are accident-prone areas especially for rear-end crashes due to the fact that the diversity of the braking behaviors of drivers increases during the signal change. The objective of this article is to improve knowledge of the relationship between rear-end crashes occurring at signalized intersections and a series of potential traffic risk factors classified by driver characteristics, environments, and vehicle types. METHODS: Based on the 2001 Florida crash database, the classification tree method and Quasi-induced exposure concept were used to perform the statistical analysis. Two binary classification tree models were developed in this study. One was used for the crash comparison between rear-end and non-rear-end to identify those specific trends of the rear-end crashes. The other was constructed for the comparison between striking vehicles/drivers (at-fault) and struck vehicles/drivers (not-at-fault) to find more complex crash pattern associated with the traffic attributes of driver, vehicle, and environment. RESULTS: The modeling results showed that the rear-end crashes are over-presented in the higher speed limits (45-55 mph); the rear-end crash propensity for daytime is apparently larger than nighttime; and the reduction of braking capacity due to wet and slippery road surface conditions would definitely contribute to rear-end crashes, especially at intersections with higher speed limits. The tree model segmented drivers into four homogeneous age groups: < 21 years, 21-31 years, 32-75 years, and > 75 years. The youngest driver group shows the largest crash propensity; in the 21-31 age group, the male drivers are over-involved in rear-end crashes under adverse weather conditions and the 32-75 years drivers driving large size vehicles have a larger crash propensity compared to those driving passenger vehicles. CONCLUSIONS: Combined with the quasi-induced exposure concept, the classification tree method is a proper statistical tool for traffic-safety analysis to investigate crash propensity. Compared to the logistic regression models, tree models have advantages for handling continuous independent variables and easily explaining the complex interaction effect with more than two independent variables. This research recommended that at signalized intersections with higher speed limits, reducing the speed limit to 40 mph efficiently contribute to a lower accident rate. Drivers involved in alcohol use may increase not only rear-end crash risk but also the driver injury severity. Education and enforcement countermeasures should focus on the driver group younger than 21 years. Further studies are suggested to compare crash risk distributions of the driver age for other main crash types to seek corresponding traffic countermeasures.     

Modeling level-of-safety for bus stops in China

Objective: Safety performance at bus stops is generally evaluated by using historical traffic crash data or traffic conflict data. However, in China, it is quite difficult to obtain such data mainly due to the lack of traffic data management and organizational issues. In light of this, the primary objective of this study is to develop a quantitative approach to evaluate bus stop safety performance.

Methods: The concept of level-of-safety for bus stops is introduced and corresponding models are proposed to quantify safety levels, which consider conflict points, traffic factors, geometric characteristics, traffic signs and markings, pavement conditions, and lighting conditions. Principal component analysis and k-means clustering methods were used to model and quantify safety levels for bus stops.

Results: A case study was conducted to show the applicability of the proposed model with data collected from 46 samples for the 7 most common types of bus stops in China, using 32 of the samples for modeling and 14 samples for illustration. Based on the case study, 6 levels of safety for bus stops were defined. Finally, a linear regression analysis between safety levels and the number of traffic conflicts showed that they had a strong relationship (R² value of 0.908).

Conclusions: The results indicated that the method was well validated and could be practically used for the analysis and evaluation of bus stop safety in China. The proposed model was relatively easy to implement without the requirement of traffic crash data and/or traffic conflict data. In addition, with the proposed method, it was feasible to evaluate countermeasures to improve bus stop safety (e.g., exclusive bus lanes).     

Safety effects of street lighting on roadway segments: Development of a crash modification function

Objective: Nighttime crashes are overrepresented on the U.S. highway system. Roadway lighting, which provides additional visibility by supplementing vehicle headlights, has been identified as an effective countermeasure to improve nighttime safety. However, the existing literature does not provide a thorough understanding of the effects of street lighting photometric characteristics on nighttime crash occurrence on roadway segments. This study aimed to investigate the relationship between lighting photometric measures and nighttime crash risk on roadway segments and develop a crash modification function/factor (CMF).

Methods: The research team collected horizontal illuminance data on 440 roadway segments between 2 successive signalized intersections in Florida for 2012–2014 and matched 4 years of nighttime and daylight crash data (2011–2014). Random parameter negative binomial models were estimated for both nighttime and daylight crash frequencies. The expected night-to-day crash odds ratio, as an equivalent of CMF, was derived from the fitted models with the correction of estimation variances. The confidence intervals (CIs) of the developed CMF were estimated using the Cox method.

Results: The coefficient of the mean of horizontal illuminance is significantly negative in the nighttime model. The coefficients of the standard deviation of horizontal illuminance are significantly positive and normally distributed in both the nighttime and daylight models. The significance of the standard deviation in the daylight model captures the confounding effects—a high standard deviation correlates with high traffic exposures, poor safety design standards, and low maintenance quality. The CMF based on the expected daylight-to-day odds ratio was developed as an exponential function of the increments and the increment squares of the mean and the standard deviation of horizontal illuminance. Its 95% CIs indicate that the CMF is almost significant over the whole range. Other significant variables contributing to nighttime crash risk include annual average daily traffic, truck percentage, segment length, access density, undivided roads, and urban/city limits.

Conclusions: Horizontal illuminance characteristics have a significant impact on nighttime crash risk on roadway segments. An increase in the mean of horizontal illuminance, indicating an improvement in average lighting level, tends to decrease nighttime crash risk; an increase in the standard deviation, representing a poor uniformity of lighting pattern on a roadway segment, is more likely to raise nighttime crash risk. Because the 2 measures are strongly correlated in a low mean range (<0.44 fc), the 2 photometric measures need to be considered together to interpret the safety effects of lighting patterns. The standard deviation shows better performance in measuring lighting uniformity on a roadway segment than the traditional ratios (max-to-min and mean-to-min). However, a new photometric measure is needed to capture the true lighting pattern influencing driver vision at night.     

Crash risk of ESC-fitted passenger cars

Objective: We examined both fatal and injury at-fault crashes of a population of passenger cars fitted with electronic stability control (ESC). Crash rates were calculated in relation to both registration years and mileage. Crash rates were also calculated for a non-ESC car population and crash rate ratios were calculated to compare the crash risk between ESC-fitted and non-ESC-fitted passenger cars.

Methods: Passenger car models with and without ESC were identified (ESC-equipped cars: 3,352,813 registration years; non-ESC-equipped: 5,839,946 registration years) and their vehicle information for the period 2009–2013, including mileage (ESC-equipped vehicles: 89.3 billion kilometers; non-ESC-equipped: 72.4 billion kilometers), was drawn from the national Vehicular and Driver Data Register.

The registry of Finnish road accident investigation teams was accessed and all fatal at-fault crashes among the cars in the study populations (ESC 97; non-ESC 377) for the period 2009–2013 were analyzed. The motor insurance database includes at-fault crashes leading to injuries and was utilized for analyses (ESC: N?=?8,827, non-ESC: N?=?21,437).

Crash rates and crash rate ratios were calculated to evaluate crash risk of both ESC-equipped and non-ESC-equipped passenger cars. Poisson regression was used to model crash involvement rate ratios both per registration year and per mileage for vehicles with ESC and without ESC, controlling for age and gender of the vehicle owner and vehicle mass.

Results: Passenger cars fitted with ESC showed lower crash rates than non-ESC-equipped cars in all crash types studied. In general, the difference in crash rates between ESC-equipped and non-ESC-equipped vehicles was greater when the crashes were compared to the mileage rather than registration years. The mileage-proportional crash rate of ESC-equipped cars was 64% (95% confidence interval, 61%; 67%) lower in run-off-road crashes resulting in injury and as much as 82% (65%; 91%) lower in fatal run-off-road crashes when suicides and disease attacks were not taken into account.

Conclusions: Our results show that modern passenger cars provide a significant crash risk reduction, which depends on both ESC and passive safety features introduced. Results also show that exposure evaluation in terms of registration years (or vehicle population) instead of true mileage can provide an overly pessimistic view of the crash risk.     

Severity models of cross-median and rollover crashes on rural divided highways in Pennsylvania

Introduction

Crossover and rollover crashes in earth-divided, traversable medians on rural divided highways can lead to severe injury outcomes. This study estimated severity models of these two crash types. Vehicle, driver, roadway, and median cross-section design data were factors considered in the models. A unique aspect of the data used to estimate the models were the availability of median cross-slope data, which are not commonly included in roadway inventory data files.

Methods

A binary logit model of cross-median crash severity and a multinomial logit model of rollover crash severity were estimated using five years of data from rural divided highways in Pennsylvania.

Results

The highest probability of a fatal or major injury in cross-median and rollover crashes was found to occur in cases when a driver was not wearing a seatbelt. While flatter cross-slopes and narrower medians were associated with more severe cross-median crash outcomes, steeper cross-slopes and narrower medians significantly increased rollover crash severity outcomes. The presence of horizontal curves was associated with increased probabilities of high-severity outcomes in a median rollover crash.

Impact on Industry

Modeling results in this study confirmed that cross-median and median rollover crash severity outcomes are associated with median cross-section design characteristics. Based on the estimated models, it appears that flatter and narrower medians lead to more severe injury outcomes in cross-median crashes. Steeper median cross-slopes and narrower medians were associated with higher probabilities of more severe outcomes in median rollover crashes. The results presented in this study suggest that there is a trade-off between median cross-section design and cross-median and rollover crashes in earth-divided, traversable medians on rural divided highways. While the severity models can be included in a framework to develop design guidance in relation to this trade-off, models of crash frequency should also be considered.     

Quantifying the impact of adaptive traffic control systems on crash frequency and severity: Evidence from Oakland County,Michigan

IntroductionDespite seeing widespread usage worldwide, adaptive traffic control systems have experienced relatively little use in the United States. Of the systems used, the Sydney Coordinated Adaptive Traffic System (SCATS) is the most popular in America. Safety benefits of these systems are not as well understood nor as commonly documented.MethodThis study investigates the safety benefits of adaptive traffic control systems by using the large SCATS-based system in Oakland County, MI known as FAST-TRAC. This study uses data from FAST-TRAC-controlled intersections in Oakland County and compares a wide variety of geometric, traffic, and crash characteristics to similar intersections in metropolitan areas elsewhere in Michigan. Data from 498 signalized intersections are used to conduct a cross-sectional analysis. Negative binomial models are used to estimate models for three dependent crash variables. Multinomial logit models are used to estimate an injury severity model. A variable tracking the presence of FAST-TRAC controllers at intersections is used in all models to determine if a SCATS-based system has an impact on crash occurrences or crash severity.ResultsEstimates show that the presence of SCATS-based controllers at intersections is likely to reduce angle crashes by up to 19.3%. Severity results show a statistically significant increase in non-serious injuries, but not a significant reduction in incapacitating injuries or fatal accidents.     

A simultaneous equations model of crash frequency by collision type for rural intersections

Safety at roadway intersections is of significant interest to transportation professionals due to the large number of intersections in transportation networks, the complexity of traffic movements at these locations that leads to large numbers of conflicts, and the wide variety of geometric and operational features that define them. A variety of collision types including head-on, sideswipe, rear-end, and angle crashes occur at intersections. While intersection crash totals may not reveal a site deficiency, over exposure of a specific crash type may reveal otherwise undetected deficiencies. Thus, there is a need to be able to model the expected frequency of crashes by collision type at intersections to enable the detection of problems and the implementation of effective design strategies and countermeasures. Statistically, it is important to consider modeling collision type frequencies simultaneously to account for the possibility of common unobserved factors affecting crash frequencies across crash types. In this paper, a simultaneous equations model of crash frequencies by collision type is developed and presented using crash data for rural intersections in Georgia. The model estimation results support the notion of the presence of significant common unobserved factors across crash types, although the impact of these factors on parameter estimates is found to be rather modest.     

Weather impacts on single-vehicle truck crash injury severity

IntroductionThe focus of this paper is on illustrating the feasibility of aggregating data from disparate sources to investigate the relationship between single-vehicle truck crash injury severity and detailed weather conditions. Specifically, this paper presents: (a) a methodology that combines detailed 15-min weather station data with crash and roadway data, and (b) an empirical investigation of the effects of weather on crash-related injury severities of single-vehicle truck crashes.MethodRandom parameters ordinal and multinomial regression models were used to investigate crash injury severity under different weather conditions, taking into account the individual unobserved heterogeneity. The adopted methodology allowed consideration of environmental, roadway, and climate-related variables in single-vehicle truck crash injury severity.Results and conclusionsResults showed that wind speed, rain, humidity, and air temperature were linked with single-vehicle truck crash injury severity. Greater recorded wind speed added to the severity of injuries in single-vehicle truck crashes in general. Rain and warmer air temperatures were linked to more severe crash injuries in single-vehicle truck crashes while higher levels of humidity were linked to less severe injuries. Random parameters ordered logit and multinomial logit, respectively, revealed some individual heterogeneity in the data and showed that integrating comprehensive weather data with crash data provided useful insights into factors associated with single-vehicle truck crash injury severity.Practical applicationsThe research provided a practical method that combined comprehensive 15-min weather station data with crash and roadway data, thereby providing useful insights into crash injury severity of single-vehicle trucks. Those insights are useful for future truck driver educational programs and for truck safety in different weather conditions.