Identifying crash type propensity using real-time traffic data on freeways

Introduction: We examine the effects of various traffic parameters on type of road crash. Method: Multivariate probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de-France region, France. Results: Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Rear-end crashes involving two vehicles were found to be more probable for relatively low values of both speed and density, rear-end crashes involving more than two vehicles appear to be more probable under congested conditions, while single-vehicle crashes appear to be largely geometry-dependent. Impact on Industry: Results could be integrated in a real-time traffic management application.     

Understanding traffic crash under-reporting: Linking police and medical records to individual and crash characteristics

Objective: This study aligns to the body of research dedicated to estimating the underreporting of road crash injuries and adds the perspective of understanding individual and crash factors contributing to the decision to report a crash to the police, the hospital, or both.

Method: This study focuses on road crash injuries that occurred in the province of Funen, Denmark, between 2003 and 2007 and were registered in the police, the hospital, or both authorities. Underreporting rates are computed with the capture–recapture method, and the probability for road crash injuries in police records to appear in hospital records (and vice versa) is estimated with joint binary logit models.

Results: The capture–recapture analysis shows high underreporting rates of road crash injuries in Denmark and the growth of underreporting not only with the decrease in injury severity but also with the involvement of cyclists (reporting rates of about 14% for serious injuries and 7% for slight injuries) and motorcyclists (reporting rates of about 35% for serious injuries and 10% for slight injuries). Model estimates show that the likelihood of appearing in both data sets is positively related to helmet and seat belt use, number of motor vehicles involved, alcohol involvement, higher speed limit, and females being injured.

Conclusions: This study adds significantly to the literature about underreporting by recognizing that understanding the heterogeneity in the reporting rate of road crashes may lead to devising policy measures aimed at increasing the reporting rate by targeting specific road user groups (e.g., males, young road users) or specific situational factors (e.g., slight injuries, arm injuries, leg injuries, weekend).     

Crash data analysis: collective vs. individual crash level approach

INTRODUCTION: Traffic safety literature has traditionally focused on identification of location profiles where "more crashes are likely to occur" over a period of time. The analysis involves estimation of crash frequency and/or rate (i.e., frequency normalized based on some measure of exposure) with geometric design features (e.g., number of lanes) and traffic characteristics (e.g., Average Annual Daily Traffic [AADT]) of the roadway location. In the recent past, a new category of traffic safety studies has emerged, which attempts to identify locations where a "crash is more likely to occur." The distinction between the two groups of studies is that the latter group of locations would change based on the varying traffic patterns over the course of the day or even within the hour. METHOD: Hence, instead of estimation of crash frequency over a period of time, the objective becomes real-time estimation of crash likelihood. The estimation of real-time crash likelihood has a traffic management component as well. It is a proactive extension to the traditional approach of incident detection, which involves analysis of traffic data recorded immediately after the incident. The units of analysis used in these studies are individual crashes rather than counts of crashes. RESULTS: In this paper, crash data analysis based on the two approaches, collective and at individual crash level, is discussed along with the advantages and shortcomings of the two approaches.     

Commercial truck crash injury severity analysis using gradient boosting data mining model

IntroductionTruck crashes contribute to a large number of injuries and fatalities. This study seeks to identify the contributing factors affecting truck crash severity using 2010 to 2016 North Dakota and Colorado crash data provided by the Federal Motor Carrier Safety Administration.MethodTo fulfill a gap of previous studies, broad considerations of company and driver characteristics, such as company size and driver’s license class, along with vehicle types and crash characteristics are researched. Gradient boosting, a data mining technique, is applied to comprehensively analyze the relationship between crash severities and a set of heterogeneous risk factors.ResultsTwenty five variables were tested and 22 of them are identified as significant variables contributing to injury severities, however, top 11 variables account for more than 80% of injury forecasting. The relative variable importance analysis is conducted and furthermore marginal effects of all contributing factors are also illustrated in this research. Several factors such as trucking company attributes (e.g., company size), safety inspection values, trucking company commerce status (e.g., interstate or intrastate), time of day, driver’s age, first harmful events, and registration condition are found to be significantly associated with crash injury severity. Even though most of the identified contributing factors are significant for all four levels of crash severity, their relative importance and marginal effect are all different.ConclusionsFor the first time, trucking company and driver characteristics are proved to have significant impact on truck crash injury severity. Some of the results in this study reinforce previous studies’ conclusions.Practical applicationsFindings in this study can be helpful for transportation agencies to reduce injury severity, and develop efficient strategies to improve safety.     

Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates

Introduction: Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes. Method: The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes. The GWOLR modeling approach allows the relationships between injury severity and its contributing factors to vary across the spatial domain, to account for the spatial heterogeneity. This approach makes use of geo-referenced data. This study explored more than 7,000 geo-referenced bicycle--motor-vehicle crashes in North Carolina. Results: This study performed a series of non-stationarity tests to identify local relationships that vary substantially across the spatial domain. These local relationships are related to the bicyclist (bicyclist age, bicyclist behavior, bicyclist intoxication, bicycle direction, bicycle position), motorist (driver age, driver intoxication, driver behavior, vehicle speed, vehicle type) and traffic (traffic volume). Conclusions: Results from the regular OLR are in general consistent with previous findings. For example, an increased bicyclist injury severity is associated with older bicyclists, bicyclist being intoxicated, and higher motor-vehicle speeds. Results from the GWOLR show local (rather than global) relationships between contributing factors and bicyclist injury severity. Practical Applications: Researchers and practitioners may use GWOLR to prioritize cycling safety countermeasures for specific regions. For example, GWOLR modeling estimates in the study highlighted the west part (from Charlotte to Asheville) of North Carolina for increased bicyclist injury severity due to the intoxication of road users including both bicyclists and drivers. Therefore, if a countermeasure is concerned with the road user intoxication, there may be a priority for the region from Charlotte to Asheville (relative to other areas in North Carolina).     

Determining older driver crash responsibility from police and insurance data

This study aimed to determine the extent to which older drivers can be considered responsible for their crashes, to identify key factors in those crashes for which older drivers have been judged responsible, and to assess the extent to which older drivers' extra crash responsibility contributes to the road toll. Insurance claims from the State of Tasmania, Australia, for 1998-2002 were linked with police records for crashes involving drivers aged either 41-55 years or 65 years or older. Insurance and police data sets contained independent judgments of crash responsibility. There was a high level of agreement between the two sets of judgments, with older drivers judged around 1.5 times more likely to be responsible for their crashes than middle-aged drivers and, conversely, older drivers were around 0.6 as likely to be absolved from crash responsibility. It was concluded that older drivers' additional crash responsibility while valuable in explaining "what went wrong," currently makes only a small contribution to the overall road toll.     

Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool

Data mining applications are becoming increasingly popular for many applications across a set of very divergent fields. Analysis of crash data is no exception. There are many data mining methodologies that have been applied to crash data in the recent past. However, one particular application conspicuously missing from the traffic safety literature until recently is association analysis or market basket analysis. The methodology is used by retailers all over the world to determine which items are purchased together. In this study, crashes are analyzed as supermarket transactions to detect interdependence among crash characteristics. The results from the analysis include simple rules that indicate which crash characteristics are associated with each other. The application is demonstrated using non-intersection crash data from the state of Florida for the year 2004. In the proposed methodology no variable needs to be assigned as dependent variable. Hence, it is useful in identifying previously unknown patterns in the data obtained from large jurisdictions (such as the State of Florida) as opposed to the data from a single roadway or intersection. Based on the association rules discovered from the analysis, it was concluded that there is a significant correlation between lack of illumination and high severity of crashes. Furthermore, it was found that under rainy conditions straight sections with vertical curves are particularly crash prone. Results are consistent with the understanding of crash characteristics and point to the potential of this methodology for the analysis of crash data collected by the state and federal agencies. The potential of this technique may be realized in the form of a decision support tool for the traffic safety administrators.     

The impact of traffic barrier geometric features on crash frequency and injury severity of non-interstate highways

Introduction: The main objective of this research is to investigate the effect of traffic barrier geometric characteristics on crashes that occurred on non-interstate roads. Method: For this purpose, height, side-slope rate, post-spacing, and lateral offset of about 137 miles of traffic barriers were collected on non-interstate (state, federal aid primary, federal aid secondary, and federal aid urban) highways in Wyoming. In addition, crash reports recorded between 2008 and 2017 were added to the traffic barrier dataset. The safety performance of traffic barriers with regards to their geometric features was analyzed in terms of crash frequency and crash severity using random-parameters negative binomial, and random-parameters ordered logit models, respectively. Results: From the results, box beam barriers with a height of 27–29 inches were less likely to be associated with injury and fatal injury crashes compared to other barrier types. On the other hand, the likelihood of a severe injury crash was found to be higher for box beam barriers with a height taller than 31 inches. Both W-beam and box beam barriers with a post-spacing between 6.1 and 6.3 inches reduced the probability of severe injury crashes. In terms of the crash frequency, flare traffic barriers had a lower crash frequency compared to parallel traffic barriers. Non-interstate roads without longitudinal rumble strips were associated with a higher rate of traffic barrier crashes.     

Regional economic conditions and crash fatality rates--a cross-county analysis

INTRODUCTION: Most studies that evaluate the relationship between economic conditions and traffic fatalities focus on the time-series relationship between the two factors. This analysis considers the cross-sectional perspective by estimating the cross-county correlation between per capita income and fatalities per vehicle mile traveled (VMT) in Ohio. METHOD: The empirical model employed in this analysis allows for interaction effects between per capita income and highway usage, in the determination of fatality rates. RESULTS: The resultant least squares estimates indicate that a significant interaction effect exists between per capita income and the percentage of highway VMT, indicating a nonlinear correlation between per capita income and fatality rates. This correlation rises as the proportion of VMT on highways rises, such that there is an inverse relationship with fatality rates when the highway share of county VMT is low and a direct relationship with fatality rates when the highway share of county VMT is high. Additionally, population density, the presence of interstate highways in rural counties, the prior prevalence of severe alcohol abuse, and the proportion of teen drivers all proved to be significant correlates with county fatality rates. CONCLUSIONS: These observations suggest factors that state and federal policy makers should consider when allocating resources that impact (whether directly or indirectly) traffic fatalities.     

道路交通的风险分析与控制

本文对道路交通系统(人、车、路)进行了风险分析,并在此基础上系统地提出了降低道路交通风险的技术管理措施.     

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.     

Investigating the effect of geometric dimensions of median traffic barriers on crashes: Crash analysis of interstate roads in Wyoming using actual crash datasets

IntroductionDespite the numerous safety studies done on traffic barriers’ performance assessment, the effect of variables such as traffic barrier’s height has not been identified considering a comprehensive actual crash data analysis. This study seeks to identify the impact of geometric variables (i.e., height, post-spacing, sideslope ratio, and lateral offset) on median traffic barriers’ performance in crashes on interstate roads.MethodGeometric dimensions of over 110 miles median traffic barriers on interstate Wyoming roads were inventoried in a field survey between 2016 and 2018. Then, the traffic barrier data collected was combined with historical crash records, traffic volume data, road geometric characteristics, and weather condition data to provide a comprehensive dataset for the analysis. Finally, an ordered logit model with random-parameters was developed for the severity of traffic barrier crashes. Based on the results, traffic barrier’s height was found to impact crash severity.ResultsCrashes involving cable barriers with a height between 30″ and 42″ were less severe than other traffic barrier types, while concrete barriers with a height shorter than 32″ were more likely involved with severe injury crashes. As another important finding, the post-spacing of 6.1–6.3 ft. was identified as the least severe range in W-beam barriers.Practical applicationsThe results show that using flare barriers should reduce the number of crashes compared to parallel barriers.     

Using medico-legal data to investigate fatal older road user crash circumstances and risk factors

Objective: This study used medico-legal data to investigate fatal older road user (ORU, aged 65 years and older) crash circumstances and risk factors relating to 4 key components of the Safe System approach (e.g., roads and roadsides, vehicles, road users, and speeds) to identify areas of priority for targeted prevention activity.

Method: The Coroners' Court of Victoria's (CCOV) Surveillance Database was searched to identify and describe the frequency and rate per 100,000 population of fatal ORU crashes in the Australian state of Victoria for 2013–2014. Information relating to the deceased ORU, crash characteristics and circumstances, and risk factors was extracted and analyzed.

Results: One hundred and thirty-eight unintentional fatal ORU crashes were identified in the CCOV Surveillance Database. Of these fatal ORU crashes, most involved older drivers (44%), followed by older pedestrians (32%), older passengers (17%), older pedal cyclists (4%), older motorcyclists (1%), and older mobility scooter users (1%). The average annual rate of fatal ORU crashes per 100,000 population was 8.1 (95% confidence interval [CI], 6.0–10.2). In terms of the crash characteristics and circumstances, most fatal ORU crashes involved a counterpart (98%), of which the majority were passenger cars (50%) or fixed/stationary objects (25%), including trees (46%) or embankments (23%). In addition, most fatal ORU crashes occurred close to home (73%), on-road (87%), on roads that were paved (94%), on roads with light traffic volume (37%), and during low-risk conditions: between 12 p.m. and 6 p.m. (44%), on weekdays (80%), during daylight (75%), and under dry/clear conditions (81%). Road user (RU) error was identified by the police and/or the coroner for the majority of fatal crashes (55%), with a significant proportion of deceased ORUs deemed to have failed to yield (54%) or misjudged (41%).

Conclusions: RU error was the most significant factor identified in fatal ORU crashes, which suggests that there is a limited capacity of the road system to fully accommodate RU errors. Initiatives related to safer roads and roadsides, vehicles, speed zones, as well as behavioral approaches are key areas of priority for targeted activity to prevent fatal ORU crashes in the future.     

MaryPODS revisited: updated crash analysis and implications for screening program implementation

PROBLEM: Due to the relative scarcity of crashes, there has consistently been a problem with analyses that use crashes as a criterion measure in their analyses. METHOD: Previous analyses of the relationships between functional capacity measures and at-fault crash involvement for older drivers as reported in the NHTSA Model Driver Screening and Evaluation Program Final Technical Report have been updated to include one additional year of driving experience. Eighteen new at-fault crashes involving drivers who previously had no crash involvement were recorded for the Maryland Motor Vehicle Administration (MVA) test sample during this interval. The method of odds ratio (OR) calculation was used to examine the relationships between functional status predictors and the most salient among the safety outcome measures identified in the Maryland research. Peak valid OR values for the prior and current analyses were contrasted, and the stability of candidate pass-fail cut-points for each predictor relative to values identified in the Final Technical Report was examined. RESULTS: Results indicate that the predictive value of functional tests appears to decrease over time, particularly for the perceptual-cognitive measures. IMPACT ON INDUSTRY: The impact of these findings on programs and policies is to underscore a need for periodic reevaluation, spaced at the shortest practical intervals but not more than 2 years apart, in order for functional capacity screening to be applied effectively by licensing authorities, health care professionals, and others to reduce personal risk and enhance public safety.     

Computational modeling and analysis of thoracolumbar spine fractures in frontal crash reconstruction

Abstract

Objective: This study aimed to reconstruct 11 motor vehicle crashes (6 with thoracolumbar fractures and 5 without thoracolumbar fractures) and analyze the fracture mechanism, fracture predictors, and associated parameters affecting thoracolumbar spine response.

Methods: Eleven frontal crashes were reconstructed with a finite element simplified vehicle model (SVM). The SVM was tuned to each case vehicle and the Total HUman Model for Safety (THUMS) Ver. 4.01 was scaled and positioned in a baseline configuration to mimic the documented precrash driver posture. The event data recorder crash pulse was applied as a boundary condition. For the 6 thoracolumbar fracture cases, 120 simulations to quantify uncertainty and response variation were performed using a Latin hypercube design of experiments (DOE) to vary seat track position, seatback angle, steering column angle, steering column position, and D-ring height. Vertebral loads and bending moments were analyzed, and lumbar spine indices (unadjusted and age-adjusted) were developed to quantify the combined loading effect. Maximum principal strain and stress data were collected in the vertebral cortical and trabecular bone. DOE data were fit to regression models to examine occupant positioning and thoracolumbar response correlations.

Results: Of the 11 cases, both the vertebral compression force and bending moment progressively increased from superior to inferior vertebrae. Two thoracic spine fracture cases had higher average compression force and bending moment across all thoracic vertebral levels, compared to 9 cases without thoracic spine fractures (force: 1,200.6 vs. 640.8 N; moment: 13.7 vs. 9.2?Nm). Though there was no apparent difference in bending moment at the L1–L2 vertebrae, lumbar fracture cases exhibited higher vertebral bending moments in L3–L4 (fracture/nonfracture: 45.7 vs. 33.8?Nm). The unadjusted lumbar spine index correctly predicted thoracolumbar fracture occurrence for 9 of the 11 cases (sensitivity?=?1.0; specificity?=?0.6). The age-adjusted lumbar spine index correctly predicted thoracolumbar fracture occurrence for 10 of the 11 cases (sensitivity?=?1.0; specificity?=?0.8). The age-adjusted principal stress in the trabecular bone was an excellent indicator of fracture occurrence (sensitivity?=?1.0; specificity?=?1.0). A rearward seat track position and reclined seatback increased the thoracic spine bending moment by 111–329%. A more reclined seatback increased the lumbar force and bending moment by 16–165% and 67–172%, respectively.

Conclusions: This study provided a computational framework for assessing thoracolumbar fractures and also quantified the effect of precrash driver posture on thoracolumbar response. Results aid in the evaluation of motor vehicle crash–induced vertebral fractures and the understanding of factors contributing to fracture risk.     

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.     

道路交通事故发展趋势分析与预测

根据福建省2000 -2010年交通事故相关指标,采用统计图表分析法进行交通事故发展趋势分析与安全水平比较研究,结果表明交通事故各项绝对指标总体呈下降趋势,但从万车死亡率、受伤人数与死亡人数比及交通事故死亡人数占各类事故死亡人数比重等相对指标看,交通安全总体水平偏低,交通事故后果比较严重.对交通事故死亡人数与GDP、机动车保有量、公路通车里程、人口数四项影响因素进行了多元线性回归分析,分析得出四个影响因素总体对交通事故死亡人数的线性影响是显著的,采取向后筛选策略线性回归分析得出,死亡人数与GDP的线性关系是显著的,根据回归结果建立了交通事故的预测模型.