Crash Record Research Articles

Missed opportunities to advance knowledge on traffic safety: Accessibility of driver licensing and crash data for scientific research

ObjectiveIdentifiable individual-level driver licensing and motor vehicle crash data are essential to advancing transportation safety research. However, epidemiologic studies using such data are rare, which may reflect their inaccessibility. We conducted a legal mapping study to evaluate US state laws regulating access to driver licensing and motor vehicle crash data for use in scientific research. MethodsLegal statutes regulating the release of driver licensing and motor vehicle crash data for all 50 US states and the District of Columbia (D.C.) were retrieved. Legal text was evaluated to determine whether these jurisdictions authorize release of identifiable individual-level licensing and crash data for use in non-governmental research. ResultsThirty-six states and D.C. explicitly authorize release of identifiable individual-level licensing data to researchers. Only five states and D.C. authorize release of identifiable individual-level crash records. No states explicitly prohibit the release of individual-level data about licensing records and only three states prohibit release of individual-level crash record data, meaning that in many states it is ambiguous whether and when releasing such data to researchers is permitted. ConclusionsIt is important to understand why licensing data are not used more frequently in transportation safety research given that many state laws permit access for non-governmental researchers. Reforming state laws to clarify and increase access to identifiable individual-level crash report data is an important priority for transportation safety advocates and researchers.

A Comprehensive Analysis for the Heterogeneous Effects on Driver Injury Severity in Single-Vehicle Passenger Car and SUV Rollover Crashes

In road traffic crashes, although rollover crashes account for a relatively low proportion, those result in a high fatality rate. The present study performed random parameters ordered logit models to examine risk factors as well as their heterogeneous effects on driver injury severity in single-vehicle passenger car and SUV rollover crashes. Crash data for the empirical analysis were extracted from Texas Crash Record Information System (CRIS) database during the year 2016. Model estimation results show that six variables (male drivers, drivers’ age, airbag deployment, failure to drive in single lane, speed limit, and rural area) were found to produce normally distributed parameters in passenger car model, while nine parameters (male drivers, safety belt use, airbag deployment, drug or alcohol use, failure to drive in single lane, improper evasive action, vehicle model year, friday, and rural area) in SUV model were found to be normally distributed. Several other factors with fixed parameters were found to be associated with driver injury severity in single-vehicle passenger car or SUV rollover crashes, most notably: ejection or partial ejection, turning left, intersection, August, adverse weather conditions, and night with light. These variables were significant in both models; most variables have stronger effects on nonincapacitating injury and serious injury outcomes in SUV than in passenger car rollover crashes. These findings provide a deep insight into causality nature and factor involved in driver injury severity in single-vehicle passenger car and SUV rollover crashes and are also helpful for transport agencies to determine appropriate countermeasures aimed at mitigating injuries sustained by drivers in single-vehicle rollover crashes.

Speed distribution and safety effects of license plate recognition: Analysis combining crash and toll record data in Hunan Province, China

With the Chinese motor expressway network rapidly expanding, the number of speed-related crashes has become an alarming phenomenon. Private vehicles that cannot be identified by enforcement cameras pose a threat to highway safety, specifically on expressways where it is problematic to initiate human enforcement. The purposes of this study are to investigate the speed characteristics of vehicles with no official identification (non–ID) on expressways and to compare key variables corresponding to the effects on casualties and injury severity. Data for expressway toll records and crash statistics from the expressway network of Hunan Province, China, have been collected and analyzed. The results highlighted: (1) The means and variance of non-ID-vehicles average travel speed are both higher than their normal counterpart; (2) There are relatively higher speeding rates among non-ID vehicles than their counterparts; (3) The injury severity of non-ID vehicles involved in crashes is significantly higher than that of normal vehicles. Moreover, according to the analysis of crash severity, drivers’ improper driving conduct, namely, speeding and fatigued driving, are the fundamental causes of disastrous crashes. Summer, senior drivers, invalid license, and alcohol intake also have an adverse effect on expressway safety. Similarly, crash time (evening) negatively affects injury severity.

Evaluation of Pedestrian/Bicycle Crash Injury Case Definitions for Use with NC DETECT

ObjectiveTo evaluate four ICD-10-CM based case definitions designed to capture pedestrian and bicycle crash-related emergency department (ED) visits in North Carolina’s statewide syndromic surveillance system, NC DETECT.IntroductionOver the last few decades, the United States has made considerable progress in decreasing the incidence of motor vehicle occupants injured and killed in traffic collisions.1 However, there is still a need for continued motor vehicle crash (MVC) injury surveillance, particularly for vulnerable road users, such as pedestrians and bicyclists. In NC, the average annual number of pedestrian-motor vehicle crashes increased by 13.5 percent during the period 2011-2015, as compared to 2006-2010.2 Therefore, the Carolina Center for Health Informatics (CCHI), as part of a NC Governor’s Highway Safety Program-funded project to improve statewide MVC injury surveillance, developed and evaluated four ICD-10-CM based case definitions for use with NC DETECT, NC’s statewide syndromic surveillance system.MethodsWe created four pedestrian/bicycle crash injury case definitions based on ICD-10-CM transportation codes (“V-codes”): Traffic-Related Pedestrian Crashes, Traffic/Non-Traffic-Related Pedestrian Crashes, Traffic-Related Bicycle Crashes, and Traffic/Non-Traffic-Related Bicycle Crashes. These definitions were based on the Centers for Disease Control and Prevention (CDC) “ICD-10-CM External Cause of Injury Codes”.3 We then applied these pedestrian/bicycle crash case definitions to 2016-2017 NC DETECT ED visit data and data obtained from a single NC Level I Trauma Center. Next, we linked the two data sources using the variables date of visit, time of visit, and medical record number. Since trauma center data are collected and verified by a designated trauma registrar, we considered the data obtained from the Level I Trauma Center to be the “gold standard”.ResultsThe linkage between the two data sources was successful, with 99.5% of all Level I Trauma Center records linking to ED visits in NC DETECT. However, we found discrepancies in the assignment of codes between the ED visit and Trauma Center data. For example, 47.5% of NC DETECT ED visits that linked to a pedestrian/bicycle crash record in the Trauma Center data, were missing an ICD-10-CM injury mechanism code of any category. Historically, the proportion of injury-related ED visits that were missing corresponding injury mechanism codes was low (<15%). However, the transition from ICD-9-CM to ICD-10-CM increased the proportion of injury-related visits missing injury mechanism codes.4 Among the 92 NC DETECT ED visits missing injury mechanism codes, 35.9% contained a pedestrian/bicycle crash-related keyword in the Chief Complaint or Triage Note.Among the 100 linked records with valid ICD-10-CM injury mechanism codes, the percent agreement between the two data sources on whether the ED visit was a “pedestrian” or “bicycle” crash was 54.4% and 71.9%, respectively. Percent agreement decreased for “traffic” and “non-traffic” designations, however. The most common V-code assigned to misclassified pedestrian/bicycle crashes in the NC DETECT ED visit data was “V87.7XXA-Person injured in a collision between other specified motor vehicles (traffic)”.Although the linkage study used data obtained from only a single Level I Trauma Center and primarily a single facility in NC DETECT, we felt that the results of this limited linkage study were generalizable to statewide NC DETECT ED visit data. For example, many facilities in NC DETECT underreport injury mechanism codes. Therefore, we added pedestrian/bicycle crash injury-related keywords to the Traffic/Non-Traffic Pedestrian/Bicycle Crash Injury case definitions (Table 1). After inclusion of these keywords, the number of identified pedestrian and bicycle crash injury-related ED visits identified in NC DETECT increased by 16.9% and 57.9% from January-June 2018, respectively (Figure 1).ConclusionsPedestrian and bicycle crashes represent a major cause of MVC injury morbidity and mortality. Therefore, the development and evaluation of case definitions is key for the successful surveillance of these types of injuries. The inclusion of keywords can help account for some of the injury mechanism data missingness common to ED surveillance systems.

Hotspot identification considering daily variability of traffic flow and crash record: A case study

Hotspots identification (HSID), a reactive crash prediction based on the historical accident counts, is crucial to transport authorities for evaluating the risk level of the object road sites. The objective of the research is to identify unidentified hotspots that should have been treated. Numerous conventional HSID approaches have been developed and applied for decades, none of which takes daily variability of traffic flow and crash record into account. In this regard, we categorize the time of day into four groups: (1) morning peak hours, (2) afternoon peak hours, (3) daytime, and (4) night off-peak hours. The authors further apply this proposed methodology to Pacific Motorway Southeast Queensland section linking Brisbane to Gold Coast based on an Empirical Bayesian (EB) approach. Finally, the applications of these proposed EB-based methods and the conventional EB method are discussed through an aggregated view.

Random parameter probit models to analyze pedestrian red-light violations and injury severity in pedestrian–motor vehicle crashes at signalized crossings

Pedestrian red-light violations at signalized crossings are an important traffic safety concern, particularly in densely populated cities. In this study, the authors quantitatively investigated the factors associated with pedestrian red-light violations and injury severity resulting from pedestrian–motor vehicle crashes at signalized crossings. Random parameter probit models are used to account for individual-specific heterogeneity that arises from a set of unmeasured factors related to traffic conditions and the pedestrians’ physical and mental status. Data for the analysis are based on the historical crash record maintained by the Hong Kong Transport Department. Children younger than age 11 years are not only associated with a higher likelihood of pedestrian red-light violations but also tend to have a higher probability of severe injuries. Factors including summer, dual carriageways with a central traffic island, and pedestrian age of 12 to 25 years are solely related to a higher likelihood of pedestrian red-light violations; meanwhile, variables solely associated with a higher probability of severe injuries include crashes that occur between 22:00 and 06:59, crashes occurring in rainy weather, crashes involving pedestrians older than age 46 years, and bus crashes. Appropriate countermeasures are recommended to curb pedestrian red-light violations and to reduce the injury severity of pedestrian crashes.

Surrogate Measures for Traffic Safety Evaluation in Developing Countries: An Application Toward Sustainable Safety

Abstract Road traffic crashes have significant negative social, economic, and infrastructure consequences, particularly in developing countries. The methods for evaluating the causal factors to identify appropriate interventions and to mitigate the problem are causes of great concern in those countries. One of the major reasons is that the evaluation methods are still based on traditional approaches, which mainly rely on the reported crash records. The underreporting coupled with the incomplete and inconsistent recording of crashes is a major issue in such settings. Poor timelines, ethical issues, biasness, and human error are also critical issues. Using a case study from Bangladesh, an assessment of the data quality of reported crash databases is presented in this article. The approach offers a means to develop and apply a robust method that can yield targeted safety measures without the need for using crash data. The application of traffic conflict techniques, which has gained research interest as a proactive surrogate approach, could be a better alternative. However, the testing and application of this approach are still confined to the lane-based homogeneous traffic environment, which mainly prevails in developed countries. Considering the traffic characteristics and limited facilities in developing countries, this article outlines an adapted framework for safety evaluation using a traffic conflict technique. The adapted technique has been applied in a major highway section of Bangladesh and validated by comparing the characteristics of the estimated conflicts and real crash data. Finally, future research directions for achieving sustainable road safety objectives in developing countries are outlined.

Assessment of the safety benefits of vehicles’ advanced driver assistance, connectivity and low level automation systems

The Connected Vehicle (CV) technologies together with other Driving Assistance (DA) technologies are believed to have great effects on traffic operation and safety, and they are expected to impact the future of our cities. However, few research has estimated the exact safety benefits when all vehicles are equipped with these technologies. This paper seeks to fill the gap by using a general crash avoidance effectiveness framework for major CV&DA technologies to make a comprehensive crash reduction estimation. Twenty technologies that were tested in recent studies are summarized and sensitivity analysis is used for estimating their total crash avoidance effectiveness. The results show that crash avoidance effectiveness of CV&DA technology is significantly affected by the vehicle type and the safety estimation methodology. A 70% crash avoidance rate seems to be the highest effectiveness for the CV&DA technologies operating in the real-world environment. Based on the 2005–2008 U.S. GES Crash Records, this research found that the CV&DA technologies could lead to the reduction of light vehicles’ crashes and heavy trucks’ crashes by at least 32.99% and 40.88%, respectively. The rear-end crashes for both light vehicles and heavy trucks have the most expected crash benefits from the technologies. The paper also studies the effectiveness of Forward Collision Warning technology (FCW) under fog conditions, and the results show that FCW could reduce 35% of the near-crash events under fog conditions.

Transport company safety climate—The impact on truck driver behavior and crash involvement

ABSTRACTObjective: The present study investigated the relationships between safety climate and driving behavior and crash involvement.Methods: A total of 339 company-employed truck drivers completed a questionnaire that measured their perceptions of safety climate, crash record, speed choice, and aberrant driving behaviors (errors, lapses, and violations).Results: Although there was no direct relationship between the drivers' perceptions of safety climate and crash involvement, safety climate was a significant predictor of engagement in risky driving behaviors, which were in turn predictive of crash involvement.Conclusions: This research shows that safety climate may offer an important starting point for interventions aimed at reducing risky driving behavior and thus fewer vehicle collisions.

Transport, driving and ageing

SummaryTransport is the invisible glue that holds our lives together, an under-recognized contributor to economic, social and personal well-being. In public health terms, the medical profession had previously allowed itself to focus almost exclusively on the downsides of transport. However, the research basis for transport, driving and ageing is steadily evolving and has important academic and practical considerations for gerontologists and geriatricians. For gerontologists, teasing out the critical role of transport in the health and well-being of older people is an imperative, as well as the key challenges inherent in transitioning from driving to not driving. The safe crash record of a group with significant multi-morbidity allows us to focus on the remarkable strategic and adaptive skills of older people. From a policy perspective, strictures on older drivers are an exemplar of institutionalized ageism. For geriatricians, a key challenge is to develop strategies for including transportation in our clinical assessments, formulating effective strategies for assessment of medical fitness to drive, incorporating enabling techniques, giving due consideration to ethical and legal aspects, and developing and promoting multi-modality and alternative transportation options.

Estimating national road crash fatalities using aggregate data

Injuries and fatalities from road traffic crashes have emerged a major public health challenge in Pakistan. Reliable estimates of road crash fatalities (RCF) of a country, is a vital element needed for identification and control of key risk factors, road-safety improvement efforts and prioritizing national health. Reliability of current annual RCF estimates for Pakistan becomes highly questionable due to serious underreporting. This study aimed to predict annual RCF for Pakistan using data from World Health Organization and International Road Federation sources. An ordinary least square (OLS) regression model that relates fatality rate with different explanatory variables was developed. RCF were predicted for Pakistan for year 2012 and 2013, and results were compared with national police reported estimates. Study results indicated that there is serious underreporting of RCF in Pakistan and immediate measures are needed to improve the existing road crash recording and reporting system at the national and subnational levels.

Developing Calibration Factors for Crash Prediction Models with Consideration of Crash Recording Threshold Change

The focus of this paper is on presenting a revised method to develop calibration factors (CFs) with consideration of the change in the crash recording threshold (CRT), which is a minimum value to report crashes. The higher the CRT, the fewer the number of recorded property damage only (PDO) crashes. In this paper, a threshold adjustment factor was defined and used to estimate the new CFs. Because the threshold change affects only the total number of crashes and the PDO crashes, the percentage of fatal and injury (F/I) crashes before the threshold change needs to be adjusted to properly estimate the total number of F/I crashes. The revised method was verified with case studies using Illinois data. Five years of crash data were gathered and used to develop CFs for five roadway types, including two-lane undivided (2U), two-lane with a two-way left-turn lane (3T), four-lane undivided (4U), four-lane divided (4D), and four-lane with a two-way left-turn lane (5T). Because of an increase in the CRT in 2009, a method is needed to supplement the standard approach to adjust CFs with consideration of the effect of the new CRT. The CFs for 2U and 3T before–after considering the threshold adjustment factor were 1.44/1.32 and 1.24/1.12, respectively, while the CFs before–after the threshold change for 4U, 4D, and 5T were 0.99/0.85, 0.68/0.55, and 0.77/0.69, respectively. The results proved that the revised method can help state and local agencies predict the number of crashes without redeveloping new CFs resulting from the change in CRT.

A time of day analysis of crashes involving large trucks in urban areas

Previous studies have looked at different factors that contribute to large truck-involved crashes, however a detailed analysis considering the specific effects of time of day is lacking. Using the Crash Records Information System (CRIS) database in Texas, large truck-involved crashes occurring on urban freeways between 2006 and 2010 were separated into five time periods (i.e., early morning, morning, mid-day, afternoon and evening). A series of log likelihood ratio tests were conducted to validate that five separate random parameters logit models by time of day were warranted. The outcomes of each time of day model show major differences in both the combination of variables included in each model and the magnitude of impact of those variables. These differences show that the different time periods do in fact have different contributing factors to each injury severity further highlighting the importance of examining crashes based on time of day. Traffic flow, light conditions, surface conditions, time of year and percentage of trucks on the road were found as key differences between the time periods.

Safety Impact of Pavement Conditions

Pavement condition has been known as a key factor related to ride quality, but it is less clear how pavement condition is related to traffic crashes. The researchers used a geographic information system to link data from the Texas Department of Transportation crash record information system and the pavement management information system. Linking the data provided an opportunity to examine in depth the impact of pavement conditions on traffic crashes. This study analyzed the correlation between four key pavement condition scores and crash rates on a large number of crashes on Texas asphalt nonfreeway roadways between 2008 and 2009. The results overwhelmingly suggest that very good pavement conditions coincide with much lower crash rates compared with very poor conditions. The findings of this study provide insights into how pavement conditions might affect crash rates on different types of roadways. The study also provides critical knowledge that may lead to improvements in safety-oriented pavement design. Because of funding shortages, many state roadways suffer severe pavement deterioration, which may result in increased crash rates, as this study suggests. The findings of this study serve as scientific justification for more adequate maintenance of roadways to avoid further safety deterioration. The researchers recommend that state highway agencies maintain fair or good pavement conditions, at a minimum, on all major state highways. When possible, very good pavement conditions should be maintained to reduce crashes.

Quantification of the Reduction of Wet Weather Accidents Using Porous Friction Courses (PFC)

Porous friction courses (PFCs) are surface layers of open-graded hot-mix asphalt used to improve wet-weather safety. This paper reports results of analyses done to investigate the safety benefits of PFCs under wet conditions. A number of sections from across Texas constructed between 2003 and 2011 were evaluated. Accident rates on these sections using data from the Crash Records Information System (CRIS) were compared before and after construction of the porous surface. Inconsistencies in the CRIS wet-weather accident data prompted the use of weather station information from around Texas to more accurately identify weather conditions at the time of accidents. This information clearly indicated the benefits of PFC in reducing accidents, injuries and fatalities on roads in Texas, encouraging the continued use of these mixes. A slight but consistent increase in accidents immediately following construction of porous asphalt surfaces was also observed.

Impact of pavement conditions on crash severity

Pavement condition has been known as a key factor related to ride quality, but it is less clear how exactly pavement conditions are related to traffic crashes. The researchers used Geographic Information System (GIS) to link Texas Department of Transportation (TxDOT) Crash Record Information System (CRIS) data and Pavement Management Information System (PMIS) data, which provided an opportunity to examine the impact of pavement conditions on traffic crashes in depth. The study analyzed the correlation between several key pavement condition ratings or scores and crash severity based on a large number of crashes in Texas between 2008 and 2009. The results in general suggested that poor pavement condition scores and ratings were associated with proportionally more severe crashes, but very poor pavement conditions were actually associated with less severe crashes. Very good pavement conditions might induce speeding behaviors and therefore could have caused more severe crashes, especially on non-freeway arterials and during favorable driving conditions. In addition, the results showed that the effects of pavement conditions on crash severity were more evident for passenger vehicles than for commercial vehicles. These results provide insights on how pavement conditions may have contributed to crashes, which may be valuable for safety improvement during pavement design and maintenance. Readers should notice that, although the study found statistically significant effects of pavement variables on crash severity, the effects were rather minor in reality as suggested by frequency analyses.

Modeling Injury Outcomes of Crashes Involving Heavy Vehicles on Texas Highways

Concern related to crashes that involve large trucks has increased in Texas recently because of the potential economic impacts and level of injury severity that can be sustained. However, detailed studies on large truck crashes that highlight the contributing factors leading to injury severity have not been conducted in Texas, especially for its Interstate system. The contributing factors related to injury severity were analyzed with Texas crash data based on a discrete outcome-based model that accounts for possible unobserved heterogeneity related to human, vehicle, and road–environment factors. A random parameter logit (i.e., mixed logit) model was estimated to predict the likelihood of five standard injury severity scales commonly used in the Crash Records Information System in Texas: fatal, incapacitating, nonincapacitating, possible, and none (i.e., property damage only). Estimation results indicated that the level of injury severity outcomes was highly influenced by several complex interactions between factors and that the effects of some factors could vary across observations. The contributing factors include driver demographics, traffic flow, roadway geometric features, land use, time characteristics, weather, and lighting conditions.

Black spot overlap matching for motorcycles and cars

This research determines the size of the location-specific component of motorcycle crashes by comparing the degree of overlap between car and motorcycle crash rates at the same or similar locations...

Investigating common trends in New Zealand cycling fatalities

BackgroundFollowing the death of five cyclists in New Zealand during November 2010, The Chief Coroner announced a national Inquest to try and identify any common trends or information that could...

Estimating the Number of Serious Road Injuries in The Netherlands

This paper describes a new estimation method of the number of road injuries in The Netherlands. The bases for this method are the hospital inpatient registry and the police crash record database. Both databases contain errors and omissions. The police database in particular suffers from serious underreporting, and is also inaccurate in indicating injury severity. The hospital database is inaccurate in indicating that a patient was involved in a road crash. Nonetheless, in principle it contains all serious road injuries. After linking both databases an estimating method, inspired by capture-recapture, was used to estimate the number of road injuries. The differences in registration for transport mode, injury severity, and region of crash have been taken into account. This leads to an estimation of the number of serious road injuries in the Netherlands in 1993-2008. We found that 85% of the road injuries are recognizable as such in the hospital registry. The registration rate of the police registry is different for road injuries in crashes involving motorized vehicles (58% in 2008) and for road injuries in crashes not involving motorized vehicles (4% in 2008).