Estimating under-reporting of road crash injuries to police using multiple linked data collections

Real-time conflict risk at signalized intersection using drone video: A random parameters logit model with heterogeneity in means and variances

Signalized intersections are crash prone. This can be attributed to driver errors, red light running behaviour, and poor coordination of conflicting traffic. It is anticipated that overall crash risk at signalized intersection would increase when mixed traffic like motorcycles is involved. In this study, a real-time prediction model for motorcycle and non-motorcycle involved conflict risk at the signalized intersection is proposed. For example, high-resolution vehicle and motorcycle trajectory data are extracted from drone videos using advanced computer vision techniques. Additionally, conflict types including rear-end, angle, and head-on conflicts are also considered. Then, the multinomial logit approach is adopted to model the propensity of severe and slight vehicle-vehicle and vehicle-motorcycle conflicts. Furthermore, the problem of unobserved heterogeneity is addressed using the random parameters model with heterogeneity in means and variances. Results indicate that risk of vehicle-vehicle conflict is significantly associated with vehicle speed and acceleration, and conflict type, and that of vehicle-motorcycle conflict is associated with vehicle speed and acceleration, motorcycle lateral speed, conflict type, and time to green signal. Findings should shed light to the development and implementation of optimal traffic signal time plan and traffic management strategy that can mitigate the potential crash risk, especially involving motorcycles, at the signalized intersection.

Replication of a local record keeping method for collecting road crash data in low resource settings: lessons from Bangladesh and Nepal

BACKGROUND

Police road crash and injury data in low-income and middle-income countries are known to under-report crashes, fatalities and injuries, especially for vulnerable road users. Local record keepers, who are members of the public, can be engaged to provide an additional source of crash and injury data.

METHODS

This paper compares the application of a local record keeper method to capture road crash and injury data in Bangladesh and Nepal, assesses the quality of the data collected and evaluates the replicability and value of the methodology using a framework developed to evaluate the impact of being a local record keeper.

OUTCOME

Application in research studies in both Bangladesh and Nepal found the local record keeper methodology provided high-quality and complete data compared with local police records. The methodology was flexible enough to adapt to project and context differences. The evaluation framework enabled the identification of the challenges and unexpected benefits realised in each study. This led to the development of an 11-step process for conducting road crash data collection using local record keepers, which is presented to facilitate replication in other settings.

CONCLUSION

Data collected by local record keepers are a flexible and replicable method to understand the strengths and limitations of existing police data, adding to the evidence base and informing local and national decision-making. The method may create additional benefits for data collectors and communities, help design and assess road safety interventions and support advocacy for improved routine police data.

Sepsis-associated acute kidney injury in patients with chronic kidney disease: Patient characteristics, prevalence, timing, trajectory, treatment and associated outcomes

AIM

The features and outcomes of sepsis-associated acute kidney injury (SA-AKI) may be affected by chronic kidney disease (CKD). Accordingly, we aimed to compare SA-AKI in patients with or without CKD.

METHODS

Retrospective cohort study in 12 intensive care units (ICU). We studied the prevalence, patient characteristics, timing, trajectory, treatment and outcomes of SA-AKI with and without CKD.

RESULTS

Of 84 240 admissions, 7255 (8.6%) involved patients with CKD. SA-AKI was more common in patients with CKD (21% vs 14%; p < .001). CKD patients were older (70 vs. 60 years; p < .001), had a higher median Charlson co-morbidity index (5 vs. 3; p < .001) and acute physiology and chronic health evaluation (APACHE) III score (78 vs. 60; p < .001) and were more likely to receive renal replacement therapy (RRT) (25% vs. 17%; p < .001). They had less complete return to baseline function at ICU discharge (48% vs. 60%; p < .001), higher major adverse kidney events at day 30 (MAKE-30) (38% vs. 27%; p < .001), and higher hospital and 90-day mortality (21% vs. 13%; p < .001, and 27% vs. 16%; p < .001, respectively). After adjustment for patient characteristics and severity of illness, however, CKD was not an independent risk factor for increased 90-day mortality (OR 0.88; 95% CI 0.76-1.02; p = .08) or MAKE-30 (OR 0.98; 95% CI 0.80-1.09; p = .4).

CONCLUSION

SA-AKI is more common in patients with CKD. Such patients are older, more co-morbid, have higher disease severity, receive different ICU therapies and have different trajectories of renal recovery and greater unadjusted mortality. However, after adjustment day-90 mortality and MAKE-30 risk were not increased by CKD.

Daily motor vehicle traffic volume and other risk factors associated with road deaths in U.S. counties

INTRODUCTION

Road death risk is often characterized as deaths per volume of traffic in geographic regions, the denominator in miles or kilometers supposedly indicative of the magnitude of risk exposure. This paper reports an examination of the differences in the predictive value of factors hypothesized to influence traffic volume and road death risk.

METHOD

The association of 11 risk factors in U.S. counties during the first 7 months of 2020 was examined for consistency of predictions of road death and traffic volume measured by cell phone and vehicle location data. The study employed least squares regression for traffic volume and Poisson regression for deaths with the population as the offset variable.

RESULTS

The directions of the regression coefficients for traffic volume and odds of road deaths per population were opposite from one another for 9 of the 11 variables in the analysis of vehicle occupant deaths. Only the coefficients for maximum daily temperature and Saturday travel were in the same direction. The confidence intervals of three risk ratios for pedestrian deaths indicated low reliability but most of the predictor variables were opposite in association with traffic volume and odds of death. Although traffic volume plunged in the first weeks of the pandemic, the results for the months before and during the COVID-19 pandemic were similar.

PRACTICAL APPLICATIONS

Traffic volume is an inverse risk factor for road deaths at the local level, likely the result of lower speeds on congested roads. Without the application of countermeasures aimed at reducing speed and other risk factors, the reduction of road congestion is likely to increase deaths.

Point-of-care creatinine vs. central laboratory creatinine in the critically ill

Objective

Frequent measurement of creatinine by point-of-care testing (POCT) may facilitate the earlier detection of acute kidney injury (AKI) in critically ill patients. However, no robust data exist to confirm its equivalence to central laboratory testing. We aimed to conduct a multicenter study to compare POCT with central laboratory creatinine (CrC) measurement.

Design

Retrospective observational study, using hospital electronic medical records. Obtained paired point-of-care creatinine (CrP) from arterial blood gas machines and CrC.

Setting

Four intensive care units in Queensland, Australia.

Participants

Critically ill patients, where greater than 50% of POCT contained creatinine.

Main outcome measures

Mean difference, bias, and limits of agreement between two methods, and biochemical confounders.

Results

We studied 79,767 paired measurements in 19,118 patients, with a median Acute Physiology and Chronic Health Evaluation 3 score of 51. The mean CrC was 115.5 μmol/L (standard deviation: 100.2) compared to a CrP mean of 115 μmol/L (standard deviation: 100.7) (Pearson coefficient of 0.99). The mean difference between CrP and CrC was 0.49 μmol/L with 95% limits of agreement of -27 μmol/L and +28 μmol/L. Several biochemical variables were independently associated with the difference between tests (e.g., pH, potassium, lactate, glucose, and bilirubin), but their impact was small.

Conclusion

In critically ill patients, measurement of creatinine by POCT yields clinically equivalent values to those obtained by central laboratory measurement and can be easily used for more frequent monitoring of kidney function in such patients. These findings open the door to the use of POCT for the earlier detection of acute kidney injury in critically ill patients.

Ascertainment and description of pedestrian and bicycling injuries and fatalities in Ontario from administrative health records 2003-2017: contributions of non-collision falls and crashes

INTRODUCTION

Pedestrian and bicycling injuries may be less likely to be captured by traffic injury surveillance relying on police reports. Non-collision injuries, including pedestrian falls and single bicycle crashes, may be more likely than motor vehicle collisions to be missed. This study uses healthcare records to expand the ascertainment of active transportation injuries and evaluate their demographic and clinical features.

METHODS

We identified pedestrian and bicyclist injuries in records of deaths, hospitalisations and emergency department visits in Ontario, Canada, between 2002 and 2017. We described the most common types of clinical injury codes among these records and assessed overall counts and proportions of injury types captured by each ascertainment definition. We also ascertained relevant fall injuries where the location was indicated as 'street or highway'.

RESULTS

Pedestrian falls represented over 50% of all pedestrian injuries and affected all age groups, particularly non-fatal falls. Emergency department records indicating in-traffic bicycle injuries not involving a collision with motor vehicles increased from 14% of all bicycling injury records in 2003 to 34% in 2017. The overall number of injuries indicated by these ascertainment methods was substantially higher than official counts derived from police reports.

CONCLUSION

The use of healthcare system records to ascertain bicyclist and pedestrian injuries, particularly to include non-collision falls, can more fully capture the burden of injury associated with these transportation modes.

Comparative analysis of injury identification using KABCO and ISS in linked North Carolina trauma registry and crash data

OBJECTIVE

The purpose of this study was to examine differences between police-reported injury severity and trauma registry data among persons with linked records in North Carolina and quantify the degree of alignment.

METHODS

We analyzed linked North Carolina trauma registry and motor vehicle crash data from 2018. Injury severity identification was assessed using police-reported 5-point scale KABCO from crash data and Injury Severity Score (ISS) from trauma records. The analysis was stratified by age, sex/gender, race, ethnicity, and road users type to examine differences across groups. We calculated sensitivity, specificity, positive predictive values, and negative predictive values between police-reported injury severity and trauma registry data using ISS as the gold standard.

RESULTS

A higher proportion of patients were classified as suspected minor injuries (39.0%) compared to moderate injuries in trauma registry (25.1%). Police-reported crash data underreported injury severity when compared to trauma registry data. Police-reported KABCO had a higher degree of specificity when classifying minor injuries (79.3%) but substantially underestimated seriously injured patients, with a sensitivity of 49.9%. These findings were also consistent when stratified by subpopulations.

CONCLUSION

Hospital-based motor vehicle crash data are a main source of injury severity identification for road safety. Police-reported data were relatively accurate for minor injuries but not seriously injured patients. Understanding the characteristics of each data source both separately and linked will be critical for problem identification and program development to move toward a safe transportation system for all road users.

Identifying emerging hot spots of road traffic injury severity using spatiotemporal methods: longitudinal analyses on major roads in Ghana from 2005 to 2020

BACKGROUND

Although road traffic injuries and deaths have decreased globally, there is substantial national and sub-national heterogeneity, particularly in low- and middle-income countries (LMICs). Ghana is one of few countries in Africa collecting comprehensive, spatially detailed data on motor vehicle collisions (MVCs). This data is a critical step towards improving roadway safety, as accurate and reliable information is essential for devising targeted countermeasures.

METHODS

Here, we analyze 16 years of police-report data using emerging hot spot analysis in ArcGIS to identify hot spots with trends of increasing injury severity (a weighted composite measure of MVCs, minor injuries, severe injuries, and deaths), and counts of injuries, severe injuries, and deaths along major roads in urban and rural areas of Ghana.

RESULTS

We find injury severity index sums and minor injury counts are significantly decreasing over time in Ghana while severe injury and death counts are not, indicating the latter should be the focus for road safety efforts. We identify new, consecutive, intensifying, and persistent hot spots on 2.65% of urban roads and 4.37% of rural roads. Hot spots are intensifying in terms of severity and frequency on major roads in rural areas.

CONCLUSIONS

A few key road sections, particularly in rural areas, show elevated levels of road traffic injury severity, warranting targeted interventions. Our method for evaluating spatiotemporal trends in MVC, road traffic injuries, and deaths in a LMIC includes sufficient detail for replication and adaptation in other countries, which is useful for targeting countermeasures and tracking progress.

Comparison of the number of pedestrian and cyclist injuries captured in police data compared with health service utilisation data in Toronto, Canada 2016-2021

INTRODUCTION

Pedestrian and cyclist injuries represent a preventable burden to Canadians. Police-reported collision data include information on where such collisions occur but under-report the number of collisions. The primary objective of this study was to compare the number of police-reported collisions with emergency department (ED) visits and hospitalisations in Toronto, Canada.

METHODS

Police-reported collisions were provided by Toronto Police Services (TPS). Data included the location of the collision, approximate victim age and whether the pedestrian or cyclist was killed or seriously injured. Health services data included ED visits in the National Ambulatory Care Reporting System and hospitalisations from the Discharge Abstract Database using ICD-10 codes for pedestrian and cycling injuries. Data were compared from 2016 to 2021.

RESULTS

Injuries reported in the health service data were higher than those reported in the TPS for cyclists and pedestrians. The discrepancy was the largest for cyclists treated in the ED, with TPS capturing 7.9% of all cycling injuries. Cyclist injuries not involving a motor vehicle have increased since the start of the pandemic (from 3629 in 2019 to 5459 in 2020 for ED visits and from 251 in 2019 to 430 for hospital admissions).

IMPLICATIONS

While police-reported data are important, it under-reports the burden. There have been increases in cyclist collisions not involving motor vehicles and decreases in pedestrian injuries since the start of the pandemic. The results suggest that using police data alone when planning for road safety is inadequate, and that linkage with other health service data is essential.

Police and hospital data linkage for traffic injury surveillance: A systematic review

This systematic review examines studies of traffic injury that involved linkage of police crash data and hospital data and were published from 1994 to 2023 worldwide in English. Inclusion and exclusion criteria were the basis for selecting papers from PubMed, Web of Science, and Scopus, and for identifying additional relevant papers using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and supplementary snowballing (n = 60). The selected papers were reviewed in terms of research objectives, data items and sample size included, temporal and spatial coverage, linkage methods and software tools, as well as linkage rates and most significant findings. Many studies found that the number of clinically significant road injury cases was much higher according to hospital data than crash data. Under-estimation of cases in crash data differs by road user type, pedestrian cases commonly being highly under-counted. A limited number of the papers were from low- and middle-income countries. The papers reviewed lack consistency in what was reported and how, which limited comparability.

Linking crash and breathalyzer data in Connecticut

OBJECTIVES

To document the process of linking breathalyzer and motor vehicle crash (MVC) data for the State of Connecticut using a unique identifier in the place of personal and private information.

METHODS

Deterministic linkage methodologies were utilized in Microsoft SQL Server to join 5,634 (of 6,650) breathalyzer records to corresponding MVC driver records for the period of January 1, 2017 to December 31, 2022. Differences between the linked and original datasets were documented by comparing the consistency of frequency and proportion distributions of key variables.

RESULTS

Proportions of annual records, alcohol breath tests, and refusals were nearly unchanged when comparing linked and original breathalyzer data. When examining variables in the original MVC driver records, there were differences in the within-group proportions for sex and age, with an overrepresentation of males and drivers aged 26-to-40 years old. For crash and injury severity, the linked dataset had lower proportions of more severe injury records when compared to the original MVC data. Additionally, 1,007 breathalyzer records were not matched with an associated MVC record.

CONCLUSIONS

Linkage methodology is sound and produced quality matches. The use of a unique identifier provided a strong match qualifier in the absence of personal and private data. Changes in proportions for age, sex, crash and injury severity align with previous research. Potential missed matches may be attributed to several factors outside of the linkage process, including data discrepancies and varied reporting practices. Future studies will further explore these differences and incorporate additional toxicology data as part of a continued effort to fuze crash, citation, toxicology, and public health data. The end result will be a holistic, comprehensive, and multifaceted database for transportation research and education.

Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review

Accurately modelling crashes, and predicting crash occurrence and associated severities are a prerequisite for devising countermeasures and developing effective road safety management strategies. To this end, crash prediction modelling using machine learning has evolved over two decades. With the advent of big data that provides unprecedented opportunities to better understand the crash mechanism and its determinants, such efforts will likely be accelerated. To gear these efforts, understanding state-of-the-art machine learning-based crash prediction models becomes paramount to summarise the lessons learned from past efforts, which can assist in developing robust and accurate models. This review paper aims to address this gap by systematically reviewing the machine learning studies on crash modelling. Models are reviewed from three aspects of the application: (a) crash occurrence (or real-time crash) prediction, (b) crash frequency prediction, and (c) injury severity prediction. Further, model intricacies that impact model performance are identified and thoroughly reviewed. This comprehensive review highlights specific gaps and future research needs in three aforementioned model applications, such as improper selection of non-crash events for crash occurrence models, the inability of future forecasting of crash frequency models, and inconsistency in injury severity classes. Critical research needs relating to model development, evaluation, and application are also discussed. This review envisages methodological advancements in machine learning models for crash prediction modelling and leveraging big data to better link crashes with its determinants.

Computer vision-based assessment of cyclist-tram track interactions for predictive modeling of crossing success

INTRODUCTION

Single Bicycle Brashes (SBCs) are common, and underreported in official statistics. In urban environments, light rail tram tracks are a frequent factor, however, they have not yet been the subject of engineering analysis.

METHOD

This study employs video-based analysis at nine Dublin city centre locations and introduces a predictive model for crossing success on tram tracks, utilising cyclist crossing angles within a Surrogate Measure of Safety (SMoS) framework. Additionally, Convolutional Neural Networks (CNNs) were explored for automatic estimation of crossing angles.

RESULTS

Modelling results indicate that cyclist crossing angle is a strong predictor of crossing success, and that cyclist velocity is not. Findings also highlight the prevalence of external factors which limit crossing angles for cyclists. In particular, kerbs are a common factor, along with passing/approaching vehicles or other cyclists. Furthermore, results indicate that further training on a relatively small sample of 100 domain-specific examples can achieve substantial accuracy improvements for cyclist detection (from 0.31AP0.5 to 0.98AP0.5) and crossing angle inference from traffic camera footage.

CONCLUSIONS

Ensuring safe crossing angles is important for cyclist safety around tram tracks. Infrastructural planners should aim for intuitive, self-explainable road layouts that allow for and encourage crossing angles of 60° or more - ideally 90°.

PRACTICAL APPLICATIONS

The SMoS framework and the open-source SafeCross1 application offer actionable insights and tools for enhancing cyclist safety around tram tracks.

Crash frequency prediction based on extreme value theory using roadside lidar-based vehicle trajectory data

Crash prediction models (CPMs) are mostly developed using statistical or data-driven methods that rely on observed crashes. However, the historical crash records can be unreliable due to availability and data quality issues. Near-crashes based CPMs offer a proactive approach to predict crash frequencies prior to the occurrence of crashes. Surrogate safety measures can be used to identify near-crashes from road user trajectories. Roadside LiDAR offers an innovative approach to collect vehicle trajectory data at a microscopic resolution with high accuracy providing detailed information of all road user movements. This study presents a methodology to identify near-crashes from Roadside LiDAR based vehicle trajectory data using the surrogate indicators: TTC (Time to Collision), PET (Post Encroachment Time), ACT (Anticipated Collision Time) and MaxD (Maximum Deceleration). Additionally, time-based, and evasive-action-based surrogate measures are combined as different pairs to obtain crash probabilities using extreme value theory (EVT). The study results show that the bivariate EVT model displays a better fit to conflict extremes, predicting crash frequencies better than the univariate model. Likewise, while the bivariate model with ACT and MaxD pair performed the best in terms of accuracy, the TTC and MaxD pair was able to reflect the relative threat levels at the study intersections. Overall, the methodology lays ground for using roadside lidar based trajectory data for proactive safety analysis of signalized intersections.

A Crash Data Analysis through a Comparative Application of Regression and Neural Network Models

One way to reduce road crashes is to determine the main influential factors among a long list that are attributable to driver behavior, environmental conditions, vehicle features, road type, and traffic signs. Hence, selecting the best modelling tool for extracting the relations between crash factors and their outcomes is a crucial task. To analyze the road crash data of Milan City, Italy, gathered between 2014–2017, this study used artificial neural networks (ANNs), generalized linear mixed-effects (GLME), multinomial regression (MNR), and general nonlinear regression (NLM), as the modelling tools. The data set contained 35,182 records of road crashes with injuries or fatalities. The findings showed that unbalanced and incomplete data sets had an impact on outcome performance, and data treatment methods could help overcome this problem. Age and gender were the most influential recurrent factors in crashes. Additionally, ANNs demonstrated a superior capability to approximate complicated relationships between an input and output better than the other regression models. However, they cannot provide an analytical formulation, but can be used as a baseline for other regression models. Due to this, GLME and MNR were utilized to gather information regarding the analytical framework of the model, that aimed to construct a particular NLM.

Learning about injury severity from no-injury crashes: A random parameters with heterogeneity in means and variances approach

The traditional approach to injury-severity analyses does not allow in-depth understanding of no-injury crashes, as crash factors found to contribute to the various injury severities may have similar effects on the severity of vehicle damage even if no injury is recorded. Viewing no-injury crashes using the vehicle damage severities as sub-categories and bases for potential injuries can improve understanding of future injury crashes. To better understand the mechanism of no-injury crashes and the crash factors that contribute to the extent of vehicle damage beyond the single categorization of these crashes in injury severity analysis, this study presents a vehicle damage severity analysis for no-injury crashes. To compare the effects of crash contributing factors on crash outcomes, two injury severity models were also estimated. Random parameters multinomial logit models with heterogeneity in means and variances were developed to account for unobserved heterogeneity. Model estimation results revealed that several common factors (e.g., unsafe speed, distracted driving, driving under influence, vehicle age, and run-off-road) are correlated with both injury severity in injury crashes and vehicle damage severity in no-injury crashes. Therefore, the sub-categorization of no-injury crashes by vehicle damage severity can potentially improve estimates of injury severity considered in resource allocation decisions for traffic safety.

Severity analysis of single-vehicle left and right run-off-road crashes using a random parameter ordered logit model

OBJECTIVES

Single vehicle (SV) run-off-road crashes are a major cause of severe injury and fatality. Such crashes can result in different levels of severity depending on the direction (i.e., left or right) in which the vehicle runs off the road. This paper investigated the factors contributing to the crash severities of right run-off-road (R-ROR) and left run-off-road (L-ROR) SV crashes.

METHODS

The study used SV crash data from the City of Charlotte, North Carolina, covering 2014 to 2017. Two separate random parameter ordered logit (RPOL) models were developed to estimate the contributing factors to R-ROR and L-ROR SV crash severities. The impact of the explanatory variables on the crash severity outcomes was quantified using the models' direct pseudo-elasticities.

RESULTS

The model results showed that male drivers, Driving Under Influence (DUI), motorcycles, and dry road surfaces were significant contributing factors to R-ROR and L-ROR SV crash severities. Specifically for the R-ROR model, speeding, reckless driving, 1-2 lanes, and older drivers increased crash severity. For the L-ROR model, phone distraction, crossed centerline/median, 3-4 lanes, rain, and dark unlighted roadway increased crash severity.

CONCLUSIONS

Based on the estimated parameters for the common significant variables in the two models, it was inferred that L-ROR SV crashes are more likely to result in severe crashes compared to R-ROR SV crashes. Hence, this study contributes to the literature on ROR SV crashes by providing additional insight into contextual factors influencing ROR crash severity for more effective countermeasures.

A data fusion approach of police-hospital linked data to examine injury severity of motor vehicle crashes

Injury severity studies typically rely on police-reported crash data to examine risk factors associated with traffic injuries. The police crash database includes essential information on roadways, crashes and driver-vehicle characteristics but may not contain accurate and sufficient information on traffic injuries. Despite sizable efforts on injury severity modelling, very few studies have employed hospital records to classify injury severities accurately. As such, the inferences drawn from the police-recorded injury severity classifications may be questionable. This study investigates factors affecting road traffic injuries of motor vehicle crashes in two approaches (1) police-reported injury severity data and (2) a data fusion approach linking police and hospital records. Data from 2015 to 2019 were collected from the Abu Dhabi Traffic Police Department and linked with hospital records by the Department of Health, Abu Dhabi. A total of 6,333 casualty crashes were categorised into non-severe, severe, and fatal crashes following police-reported data and non-hospitalised, hospitalised and fatal crashes based on the police-hospital linked data. The state-of-the-art random thresholds random parameters hierarchical ordered Probit models were then employed to examine the differences in factors affecting crash-injury severities between police-reported and police-hospital linked data. While there are similarities between these two approaches, there are numerous notable differences in injury severity factors. For instance, head-on collisions are associated with high crash-injury severities in the model with police-hospital linked data, but they tend to show low injury severities in the model with police-reported data. In addition, the police-reported approach identifies that crashes occurred in remote areas and angle collisions are associated with low injury severities, which is not intuitive. These findings highlight that modelling the misclassified injury severity in police crash data may lead to wrong estimations and misleading inferences. Instead, the data fusion approach of police-hospital linked data provides critical and accurate insights into road traffic injuries and is a valuable approach for understanding traffic injuries.

The incidence burden of unreported pedestrian crashes in Illinois

OBJECTIVE

Communities with high rates of pedestrians struck by motor vehicles may miss out on mitigation resources and suffer worse medical outcomes if crashes there go unreported to police. This study investigates the places, people, and communities in Illinois where struck pedestrians are most likely to go unreported. A better understanding of the true burden and distribution of struck pedestrians will help guide policy and direct investments and interventions where they are most needed.

METHODS

Hospital records of pedestrians treated for injuries sustained by a motor vehicle that were not able to be linked with a corresponding crash report across three consecutive years are investigated. Discordance rates of struck pedestrians are calculated and disaggregated by region. A presentation of summary statistics is accompanied by an ordinary least squares predictive model to estimate the relationship between discordant struck pedestrians and sociodemographic factors.

RESULTS

The incidence of unreported struck pedestrians was not randomly distributed. Blacks struck by a motor vehicle were disproportionately likely to go unreported to police. Zip codes with the most unreported crashes per capita on average had double the poverty rate and 2.6 times the carless household rate as the rest of Illinois. Struck pedestrians diagnosed at the hospital with an intoxicating substance went unreported to police nearly 70% of the time. Generally, more severe head and thorax injuries were more likely to be reported. Struck pedestrians outside of Cook County averaged a 60% discordance rate, those within Cook County averaged a discordance rate of about 50%. Struck pedestrian cases reported to police averaged emergency department charges of about $2,500 more than unreported cases.

CONCLUSIONS

Underlying and contributing factors influential of a struck pedestrian's decision of whether to report to police is complex and layered by social constructs mixed with difficult economic decisions, often further complicated by the fog of impairment. Recommendations are made for community outreach to stress the importance of reporting incidents to police, along with adjusting case count numbers in police records using hospital data and discordance rates.

Disadvantaged by More Than Distance: A Systematic Literature Review of Injury in Rural Australia

Rural populations experience injury-related mortality and morbidity rates 1.5 times greater than metropolitan residents. Motivated by a call for stronger epidemiological evidence around rural injuries to inform prevention, a systematic review of peer-reviewed literature published between January 2010 and March 2021 was undertaken to explore the epidemiology of rural injury and associated risk factors in Australia. A subsequent aim was to explore definitions of rurality used in injury prevention studies. There were 151 papers included in the review, utilizing 23 unique definitions to describe rurality. People living in rural areas were more likely to be injured, for injuries to be more severe, and for injuries to have greater resulting morbidity than people in metropolitan areas. The increase in severity reflects the mechanism of rural injury, with rural injury events more likely to involve a higher energy exchange. Risk-taking behavior and alcohol consumption were significant risk factors for rural injury, along with rural cluster demographics such as age, sex, high socio-economic disadvantage, and health-related comorbidities. As injury in rural populations is multifactorial and nonhomogeneous, a wide variety of evidence-based strategies are needed. This requires funding, political leadership for policy formation and development, and implementation of evidence-based prevention interventions.

Cycling Tourism: A Literature Review to Assess Implications, Multiple Impacts, Vulnerabilities, and Future Perspectives

Cycle tourists are increasingly prominent in the profile of world tourism and, in the light of the literature, it is essential, among other things, to examine more closely who they are, what their concerns and motivations are that generate the choice of a cycle tourism product, and, as a priority, the level of economic, social, and environmental impact they cause at destination. In this context, this literature review aims at identifying authors’ and publishers’ interest in cycle tourism, the positive and negative effects of this form of tourism on the economic environment (direct and indirect), as well as effects on the social environment (benefits and potential drawbacks for local communities, along with health benefits for practitioners) and, last but not least, the degree of vulnerability to economic crises generated by travel restrictions. The conclusions reported in this article, as they have been drawn from analyses and examples of best practice, based on natural and anthropogenic geographical conditions, will be prioritised as future research directions. The usefulness of this approach lies in the information with significant applied and novelty aspects, addressed to local, regional, and national authorities, cycling and cycle-tourism associations, and various private interested enterprises, with a view to promoting cycling for recreational purposes and implementing cycling/cycle-tourism infrastructure as a sustainable way of developing small towns and rural areas with tourism potential.

Improving identification of crash injuries: Statewide integration of hospital discharge and crash report data

OBJECTIVE

The availability of complete and accurate crash injury data is critical to prevention and intervention efforts. Relying solely on hospital discharge data or police crash reports may result in a biased undercount of injuries. Linking hospital data with crash reports may allow for a more robust identification of injuries and an understanding of which populations may be missed in an analysis of one source. We used the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse to examine the share of the entire crash-injured population identified in each of the two data sources, overall and by age, race/ethnicity, sex, injury severity, and road user type.

METHODS

We utilized 2016-2017 data from the NJ-SHO warehouse. We identified crash-involved individuals in hospital discharge data by applying the ICD-10-CM external cause of injury matrix. Among crash-involved individuals, we identified those with injury- or pain-related diagnosis codes as being injured. We also identified crash-involved individuals via crash report data and identified injuries using the KABCO scale. We jointly examined the two sources; injuries in the hospital discharge data were documented as being related to the same crash as injuries found in the crash report data if the date of the crash report preceded the date of hospital admission by no more than two days.

RESULTS

In total, there were 262,338 crash-involved individuals with a documented injury in the hospital discharge data or on the crash report during the study period; 168,874 had an injury according to hospital discharge data, and 164,158 had an injury in crash report data. Only 70,694 (26.9%) had an injury in both sources. We observed differences by age, race/ethnicity, injury severity, and road user type: hospital discharge data captured a larger share of those ages 65+, those who were Black or Hispanic, those with higher severity injuries, and those who were bicyclists or motorcyclists.

CONCLUSIONS

Each data source in isolation captures approximately two-thirds of the entire crash-injured population; one source alone misses approximately one-third of injured individuals. Each source undercounts people in certain groups, so relying on one source alone may not allow for tailored prevention and intervention efforts.

Data linkage for crash outcome assessment: Linking police-reported crashes, emergency response data, and trauma registry records

INTRODUCTION

Traffic crash reports lack detailed information about emergency medical service (EMS) responses, the injuries, and the associated treatments, limiting the ability of safety analysts to account for that information. Integrating data from other sources can enable a better understanding of characteristics of serious crashes and further explain variance in injury outcomes. In this research, an approach is proposed and implemented to link crash data to EMS run data, patient care reports, and trauma registry data.

METHOD

A heuristic framework is developed to match EMS run reports to crashes through time, location, and other indicators present in both datasets. Types of matches between EMS and crashes were classified. To investigate the fidelity of the match approach, a manual review of a sample of data was conducted. A comparative bias analysis was implemented on several key variables.

RESULTS

72.2% of EMS run reports matched to a crash record and 69.3% of trauma registry records matched with a crash record. Females, individuals between 11 and 20 years old, and individuals involved in single vehicle or head on crashes were more likely to be present in linked data sets. Using the linked data sets, relationships between EMS response time and reported injury in the crash report, and between police-reported injury and injury severity score were examined.

CONCLUSION

Linking data from other sources can greatly enhance the information available to address road safety issues, data quality issues, and more. Linking data has the potential to result in biases that must be investigated as they relate to the use-case for the data.

PRACTICAL IMPLICATIONS

This research resulted in a transferable heuristic approach that can be used to link data sets that are commonly collected by agencies across the world. It also provides guidance on how to check the linked data for biases and errors.

Police-reported pedestrian crash matching and injury severity misclassification by body region in New Mexico, USA

Between 2009 and 2019, pedestrian fatalities in the U.S. increased 51.0% while all other traffic fatalities increased 0.4%. To mitigate pedestrian safety issues, practitioners increasingly use police-reported data to identify and treat locations that experience either serious or fatal injuries. We investigated how many and which types of pedestrian injuries were misclassified by police-reported data in New Mexico between 2014 and 2018 by matching pedestrian-vehicle crash victims reported in New Mexico Department of Transportation (NMDOT) crash data to patients treated at University of New Mexico Health-Science Center, an American College of Surgeons-certified level 1 trauma center (n = 3097 pedestrians in NMDOT data; n = 512 matched pedestrians). Findings suggest that injuries involving older pedestrians, males, alcohol, more serious injuries, and those that occur at night are more likely to match to the hospital data. Of the non-fatally injured pedestrians who police estimated as seriously-injured (n = 207), 21.7% were no more than minorly-injured (n = 45) (KABCO A and ISS < 9). Of pedestrians who police estimated as minorly-injured (n = 239), 55.6% were seriously-injured (n = 133) (KABCO B,C,O and ISS ≥ 9). Of pedestrians with true serious injuries (n = 295) (ISS ≥ 9), 45.1% were under-estimated by police (n = 133) (KABCO B,C,O and ISS ≥ 9) whereas 29.8% of pedestrians with true minor injuries (n = 151) (ISS < 9) were over-estimated by police (n = 45) (KABCO A and ISS < 9). Minorly-injured pedestrians who were over-estimated by police (KABCO A and ISS < 9) were more likely to have lower extremity injuries (62.2% vs 42.5%, p-value = 0.013) compared to minorly-injured pedestrians whose injury severities were estimated correctly (KABCO B,C,O and ISS < 9). Seriously-injured pedestrians who were under-estimated (KABCO B,C,O and ISS ≥ 9) were less likely to have injuries to the head (39.8% vs. 55.6%, p-value = 0.003), spine (30.1% vs. 50.0%, p-value < 0.001), thorax (53.4% vs. 66.7%, p-value = 0.0139), or abdomen (18.8% vs. 32.1%, p-value = 0.005) compared to seriously-injured pedestrians whose injury severities were estimated correctly (KABCO A and ISS ≥ 9). This research illustrates the importance of linking police and health outcome databases to provide a more complete understanding of traffic safety.

Structural anatomy and temporal trends of road accident research: Full-scope analyses of the field

INTRODUCTION

Scholarly research on road accidents over the past 50 years has generated substantial literature. We propose a robust search strategy to retrieve and analyze this literature.

METHOD

Analyses was focused on estimating the size of this literature and examining its intellectual anatomy and temporal trends using bibliometric indicators of its articles.

RESULTS

The size of the literature is estimated to have exceeded N = 25,000 items as of 2020. At the highest level of aggregation, patterns of term co-occurrence in road accident articles point to the presence of six major divisions: (i) law, legislation & road trauma statistics; (ii) vehicular safety technology; (iii) statistical modelling; (iv) driving simulator experiments of driving behavior; (v) driver style and personality (social psychology); and (vi) vehicle crashworthiness and occupant protection division. Analyses identify the emergence of various research clusters and their progress over time along with their respective influential entities. For example, driver injury severity " and crash frequency show distinct characteristics of trending topics, with research activities in those areas notably intensified since 2015 Also, two developing clusters labelled autonomous vehicle and automated vehicle show distinct signs of becoming emerging streams of road accident literature.

CONCLUSIONS

By objectively documenting temporal patterns in the development of the field, these analyses could offer new levels of insight into the intellectual composition of this field, its future directions, and knowledge gaps. Practical Applications: The proposed search strategy can be modified to generate specific subsets of this literature and assist future conventional reviews. The findings of temporal analyses could also be instrumental in informing and enriching literature review sections of original research articles. Analyses of authorships can facilitate collaborations, particularly across various divisions of accident research field.

Investigating consecutive conflicts of pedestrian crossing at unsignalized crosswalks using the bivariate logistic approach

Pedestrians confront risky situations at unsignalized crosswalks when they are consecutively interacting with motorized vehicles and non-motorized vehicles while crossing. This study aims to investigate the safety of pedestrians with a new perspective that focuses on consecutive conflicts occurring during pedestrian crossing. Based on about 9 h video data collected by an unmanned aerial vehicle from six unsignalized crosswalks of a roundabout, consecutive conflicts were identified, and an integrated severity index that combines post encroachment time, jerk and yaw rate ratio was proposed to measure the severity of consecutive conflicts. Moreover, bivariate logistic models that account for and not account for the correlation between the pedestrian-motorized vehicle (P-MV) conflict and the pedestrian-non-motorized vehicle (P-NV) conflict of a consecutive conflict were developed, and speed-, count-, time to zebra-related factors and other factors of involved road users were considered in the models. A total of 899 consecutive conflicts were identified and on average one in six pedestrians encountered consecutive conflicts. The bivariate logistic modeling results show that the model accounting for the correlation significantly outperform its counterpart. A negative correlation is found between the severities of P-MV conflict and P-NV conflict, and the P-NV conflict is more likely to be the serious one. It is also found that speed of motorized vehicle and time to zebra for the first conflicting subject are the common factors that affect the severities of both P-NV conflicts and P-MV conflicts, while speed of pedestrian, speed of non-motorized vehicle, number of motorized vehicles, number of non-motorized vehicles, group and direction of pedestrians have significant effects on the severity of either P-MV conflicts or P-NV conflicts.

Identification and analysis of misclassified work-zone crashes using text mining techniques

Work zone safety management and research relies heavily on the quality of work zone crash data. However, it is possible that a police officer may misclassify a crash in structured data due to: restrictive options in the crash report; a lack of understanding about their importance; lack of time due to police officers' work load; and ignorance of work zone as one of the crash contributing factors. Consequently, work zone crashes are under representative in crash statistics. Crash narratives contain valuable information that is not included in the structured data. The objective of this study is to develop a classifier that applies text mining techniques to quickly find missed work zone (WZ) crashes through the unstructured text saved in the crash narratives. The study used three-year crash data from 2017 to 2019. The data from 2017 to 2018 was used as training data, and the 2019 data was used as testing data. A unigram + bigram noisy-OR classifier was developed and proven to be an efficient and effective means of classifying work zone crashes based on key information in the crash narrative. The ad-hoc analysis of misclassified work zone crashes sheds light on when, where and the plausible reasons as to why work zone crashes are more likely to be missed.

Injury-severity analysis of intercity bus crashes in Ghana: A random parameters multinomial logit with heterogeneity in means and variances approach

Travel by bus is an efficient, cost-effective, safe and preferred means of intercity transport in many advanced countries. On the contrary, there is huge public sentiment about the safety records of intercity buses in low- and middle-income countries given the increasing bus-involved road traffic crashes and high fatality rates. This study sought to model the injury severity of intercity bus transport in Ghana using the random parameters multinomial logit with heterogeneity in means and variances modelling technique to account for unobserved heterogeneity in the dataset. The dataset involves crash data from the 575 km long Accra-Kumasi-Sunyani-Gonokrom highway in Ghana. Four discrete crash outcome categories were considered in this study: fatal injury, hospitalized injury, minor injury, and no injury. The study observed that crashes involving pedestrians, unlicensed drivers, and drivers and passengers aged more than 60 years have a higher probability of sustaining fatal injuries. Also, speeding, wrong overtaking, careless driving and inexperienced drivers were associated with fatal injury outcomes on the highway. The incidence of intercity bus transport crashes involving larger buses and minibuses were also found to more likely result in fatalities. The probability of hospitalized injury increased for crashes that occurred in a village setting. Given these findings, the study proposed improvement of the road infrastructure, enforcing seatbelt availability and use in intercity buses, increased enforcement of the traffic rules and regulations to deter driver recklessness and speeding as well as improving the luminance of the highways. Additionally, apps that have features for customers to rate intercity bus operators, the quality of services provided, and also have the option to report reckless driving activities can be developed to promote safe and inclusive public transport in the country.

Evaluating cyclist biometrics to develop urban transportation safety metrics

The transportation safety paradigm for urban transportation - particularly safety for those walking and cycling - relies on counting crashes to parameterize safety. These objective measures of safety are spatially static and reflective of past events: they can be enriched by including the human response to risk at diverse infrastructure designs. This perceived risk has been well captured qualitatively in the transportation safety literature; in the following study, we seek to develop a quantitative methodology that captures perceived risk as a continuous measure of human biometrics. Building on diverse safety-critical fields, we hypothesize that the perception of safety can be measured proactively with traveler biometrics, including eye and head movements, such that high readings of biometric indicators correlate with less safe areas. We collect biometric data from cyclists traversing an urban corridor with a protected, yet not continuously, cycle lane. By isolating and correlating peaks in cyclist biometric measures with infrastructure design, we develop a set of continuous variables - lateral head movements, gaze velocity, and off-mean gaze distance, both independently and as a vector - that allow for the evaluation of urban infrastructure based on perceived risk. The results reflect that higher biometric readings correspond to less safe (i.e., unprotected) areas, indicating that perceived risk can be measured proactively with biometric data.

Configurations of underreported cyclist-motorised vehicle and single cyclist collisions: Analysis of a self-reported survey

Lower severity cycling collisions, and single cyclist collisions (or single bicycle crashes) are significantly underreported in police statistics, introducing biases into the types of collisions that are available for analysis. Furthermore, many lower severity collisions do not appear in other collision data sources (e.g. hospital and insurance data). This in turn affects priorities for cyclist safety and puts an underemphasis on certain collision types. Due to an absence of data, little is known of the configurations of unreported collisions. In this paper, data from a recent self-reporting survey of cycling collisions in Ireland is used to provide details of cyclist collisions with motorised vehicles and single cyclist collisions, with the inclusion of unreported collision types. Pre-crash scenarios and impact configurations for cyclist collisions with bonnet-type vehicles, and collision factors and fall types for single cyclist collisions are coded. Injury patterns and police underreporting levels are compared, and representative collision scenarios are identified. This study highlights the relative importance of collisions resulting from the cyclist and vehicle travelling in the same direction, specifically, nearside-hook, vehicle lane changing, and overtaking manoeuvres are emphasised. Furthermore, cases involving the cyclist struck from the side by vehicle fronts comprise a smaller share than previous studies. Specifically, side to side impacts, impacts between the front of the cyclist/bicycle and the side of the vehicle, and impacts with open(ing) doors emerge as important impact configurations with the inclusion of self-reported cases. For single cyclist collisions, the importance of loss of traction of the tyres due to slippery road conditions and interactions with tram tracks and kerbs are emphasised. Fall types differ between single cyclist collision scenarios and are related to differences in injury severity. These findings add to existing knowledge for fatal and higher severity collisions, demonstrating that cyclist safety priorities change with inclusion of underreported, and lower severity collisions. The findings are particularly relevant to road infrastructural planners, as well as in the fields of injury biomechanics, and automated vehicle safety (ADAS). Representative scenarios for collisions with bonnet-type vehicles and single cyclist collisions have been identified, allowing for their future inclusion in development of collision and injury prevention strategies. The dataset generated in this study is available from the authors on reasonable request.

Crash involvement among unlicensed 17 year old drivers before and after licensing at 17 was allowed

Unlicensed driving among youth is associated with increased crash risk, and partly motivated by a wish to learn to drive. In this paper we examine whether crash involvement among 17-year-old unlicensed drivers changed after post-licence accompanied driving from the age of 17 was allowed in Denmark in 2017. The study includes police-registered crashes occurring three years before and three years after the change (2014-2019). Results show an increase in crash involvement among 17-year-olds and a small increase in crash involvement among unlicensed 17-year-olds, if population size is taken into account, but no differences in the crash and person characteristics before and after the change. Being male, speeding, and impairment at the time of the crash predicted unlicensed crash involvement. A latent class clustering analysis (LCCA) identified seven clusters of crashes involving an unlicensed 17-year-old. The cluster characteristics reveal different patterns in the associated factors such as females and parked vehicles being more likely to be included in C1, alcohol impaired in C2 and drug impaired in C7. Brief crash descriptions provided by the police indicate that driving with extra motives such as showing-off or pleasure are prevalent in all clusters. Results confirm, that unlicensed crash involvement among 17-year olds is associated with risk-taking behaviours such as speeding, impaired driving, showing-off, and the car being pursued by the police. However, unfortunate manoeuvres and loss of control of the vehicle possibly related to poor driving skills are also associated with the crashes. Crash characteristics such as impairment by alcohol and drugs indicate that unlicensed crash involvement is a distinct safety challenge associated with health risk behaviours rather than a transport related need for a driver's license. Additional studies exploring the motivations and circumstances associated with unlicensed driving among 17-year olds are needed along with measures to prevent car access among unlicensed youth..

Development of the integrated New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse: catalysing advancements in injury prevention research

OBJECTIVE

Our objective was to describe the development of the New Jersey Safety and Health Outcomes (NJ-SHO) data warehouse-a unique and comprehensive data source that integrates state-wide administrative databases in NJ to enable the field of injury prevention to address critical, high-priority research questions.

METHODS

We undertook an iterative process to link data from six state-wide administrative databases from NJ for the period of 2004 through 2018: (1) driver licensing histories, (2) traffic-related citations and suspensions, (3) police-reported crashes, (4) birth certificates, (5) death certificates and (6) hospital discharges (emergency department, inpatient and outpatient). We also linked to electronic health records of all NJ patients of the Children's Hospital of Philadelphia network, census tract-level indicators (using geocoded residential addresses) and state-wide Medicaid/Medicare data. We used several metrics to evaluate the quality of the linkage process.

RESULTS

After the linkage process was complete, the NJ-SHO data warehouse included linked records for 22.3 million distinct individuals. Our evaluation of this linkage suggests that the linkage was of high quality: (1) the median match probability-or likelihood of a match being true-among all accepted pairs was 0.9999 (IQR: 0.9999-1.0000); and (2) the false match rate-or proportion of accepted pairs that were false matches-was 0.0063.

CONCLUSIONS

The resulting NJ-SHO warehouse is one of the most comprehensive and rich longitudinal sources of injury data to date. The warehouse has already been used to support numerous studies and is primed to support a host of rigorous studies in the field of injury prevention.

Characteristics of cyclist collisions in Ireland: Analysis of a self-reported survey

As both a utility mode of transport and recreational activity, cycling has well-known health, environmental, and economic benefits. For these reasons it has been encouraged in many countries, including the Republic of Ireland. However, with increasing popularity there have been concurrent increases in road traffic related cyclist injuries. This study aims to characterise cyclist collisions, which are known to be underreported in Police statistics. For data collection, a survey addressing collisions was distributed to cyclists across the country in 2018. Univariable testing was used to identify differences in collision factors and injury outcomes for cyclist collisions with motorised vehicles, and those where a motorised vehicle is not involved as a collision partner i.e. single cyclist, cyclist-pedestrian, or cyclist-cyclist collisions. Furthermore, binary logistic regression modelling was used to clarify biasing factors for Police reporting of collisions. The largest proportion of collisions was between cyclists and motorised vehicles (56%), followed by single cyclist collisions (29%), collisions with other cyclists (8%), and pedestrians (7%). The odds of Police reporting for collisions with motorised vehicles in this study was 20 times greater than single cyclist collisions, 10 times greater than cyclist-cyclist collisions, and 4 times greater than collisions with pedestrians. The odds of Police reporting of serious injury collisions was 7 times greater than minor injury collisions. There were several differences in road, environmental, and human factors, and injury patterns between cyclist-motorised vehicle collisions and non-motorised vehicle collisions. The findings of this study indicate that greater attention should be paid to the following underreported collision types: 1) those that do not involve collisions with motorised vehicles (single cyclist collisions in particular), which have been shown to have differing collision characteristics to motorised vehicle collisions, and 2) less severe injuries, which have been shown to be a substantial contributor to the cyclist safety problem. Furthermore, surveys have been shown to be a valuable mechanism for investigation of lower severity cyclist injuries, which are largely unrecorded in Police or hospital data.

Improving knowledge of cyclist crashes based on hospital data including crash descriptions from open text fields

INTRODUCTION

In this study we explore the added value of bicycle crash descriptions from open text fields in hospital records from the Aarhus municipality in Denmark. We also explore how bicycle crash data from the hospital complements crash data registered by the police in the same area and time period.

METHOD

The study includes 5,313 Danish bicycle crashes, of which 4,205 were registered at the hospital and 1,078 by the police. All crashes occurred from 2010 to 2015. We performed an in-depth analysis of the open text fields on hospital records to identify factors associated with each crash using four categories: bicyclist, road, bicycle, and the other party. We employed the chi-squared test to compare the distribution of variables between crashes registered at the hospital and by the police. A binary logit model was used to estimate the probability that a crash factor is identified, and that each crash factor is associated with a single-bicycle crash.

RESULTS

The open-ended text fields in hospital records provide detailed information about crash factors not available in police records, including riding speed, inattention, clothing, specific road conditions, and bicycle defects. The factors alcohol and curb had the highest odds of being identified in relation to a single-bicycle crash. Crash data registered at the hospital included a larger number of bicycle crashes, particularly single-bicycle crashes and crashes with slight injuries only.

CONCLUSION

Crash information registered at the hospital in Aarhus Municipality contributes to a better understanding of bicycle crashes due to detailed information about crash-associated factors as well as information about a larger number of bicycle crashes, particularly single-bicycle crashes. Practical implication: Efforts to improve access to detailed information about bicycle crashes are needed to provide a better basis for bicycle crash prevention.

Using a novel data linkage approach to investigate potential reductions in motor vehicle crash severity - An evaluation of strategic highway safety plan emphasis areas

INTRODUCTION

With the significant number of motor-vehicle fatalities occurring on the nation's roadways in recent years, there exists a need to integrate a more complete range of data sources, available at a regional or statewide level, to effectively evaluate existing safety concerns and quantify their impacts. Crash data alone does not provide ample crash-associated citation, injury, and roadway characteristics; therefore, a more cohesive dataset is required to accurately and completely analyze the true impacts of motor-vehicle crashes. Previously developed strategies linked crash data with citation and roadway inventory data to enhance the identification and optimization of highway safety strategies.

METHOD

The main objective of this research focused on developing a new deterministic linkage between crash and Emergency Medical Services (EMS) data, by utilizing the Massachusetts Crash Data System (CDS) and the Massachusetts Ambulance Trip Record Information System (MATRIS).

RESULTS

After several iterations of match criterion, the validated linkage successfully matched 58.3% of MATRIS records (containing an Injury Cause of Motor Vehicle Crash) to a CDS person record (55011 linked pairs, between 2014 and 2016). The data linkage provided significant insight into injury trends in several highway safety emphasis areas such as roadway departure, speeding-related, and distraction-affected crashes. The findings from this research are twofold: (1) an established process for linking previously separate data sets, and (2) a mechanism for analysis that provides decision-makers and safety professionals with a better measure of crash outcomes.

Fifty Years of Accident Analysis & Prevention: A Bibliometric and Scientometric Overview

Accident Analysis & Prevention (AA&P) is a leading academic journal established in 1969 that serves as an important scientific communication platform for road safety studies. To celebrate its 50^th anniversary of publishing outstanding and insightful studies, a multi-dimensional statistical and visualized analysis of the AA&P publications between 1969 and 2018 was performed using the Web of Science (WoS) Core Collection database, bibliometrics and mapping-knowledge-domain (MKD) analytical methods, and scientometric tools. It was shown that the annual number of AA&P's publications has grown exponentially and that over the course of its development, AA&P has been a leader in the field of road safety, both in terms of innovation and dissemination. By determining its key source countries and organizations, core authors, highly co-cited published documents, and high burst-strength publications, we showed that AA&P's areas of focus include the "effects of hazard and risk perception on driving behavior", "crash frequency modeling analysis", "intentional driving violations and aberrant driving behavior", "epidemiology, assessment and prevention of road traffic injuries", and "crash-injury severity modeling analysis". Furthermore, the key burst papers that have played an important role in advancing research and guiding AA&P in new directions - particularly those in the fields of crash frequency and crash-injury severity modeling analyses were identified. Finally, a modified Haddon matrix in the era of intelligent, connected and autonomous transportation systems is proposed to provide new insights into the emerging generation of road safety studies.

What does crowdsourced data tell us about bicycling injury? A case study in a mid-sized Canadian city

With only ∼20 % of bicycling crashes captured in official databases, studies on bicycling safety can be limited. New datasets on bicycling incidents are available via crowdsourcing applications, with opportunity for analyses that characterize reporting patterns. Our goal was to characterize patterns of injury in crowdsourced bicycle incident reports from BikeMaps.org. We extracted 281 incidents reported on the BikeMaps.org global mapping platform and analyzed 21 explanatory variables representing personal, trip, route, and crash characteristics. We used a balanced random forest classifier to classify three outcomes: (i) collisions resulting in injury requiring medical treatment; (ii) collisions resulting in injury but the bicyclist did not seek medical treatment; and (iii) collisions that did not result in injury. Results indicate the ranked importance and direction of relationship for explanatory variables. By knowing conditions that are most associated with injury we can target interventions to reduce future risk. The most important reporting pattern overall was the type of object the bicyclist collided with. Increased probability of injury requiring medical treatment was associated with collisions with animals, train tracks, transient hazards, and left-turning motor vehicles. Falls, right hooks, and doorings were associated with incidents where the bicyclist was injured but did not seek medical treatment, and conflicts with pedestrians and passing motor vehicles were associated with minor collisions with no injuries. In Victoria, British Columbia, Canada, bicycling safety would be improved by additional infrastructure to support safe left turns and around train tracks. Our findings support previous research using hospital admissions data that demonstrate how non-motor vehicle crashes can lead to bicyclist injury and that route characteristics and conditions are factors in bicycling collisions. Crowdsourced data have potential to fill gaps in official data such as insurance, police, and hospital reports.

Approaching Bike Hazards via Crowdsourcing of Volunteered Geographic Information

Information on individual hazard perception while cycling and the associated feeling of safety are key aspects to foster sustainable urban cycling mobility. Although cyclist’s perceptions must also be critically reviewed, such crowdsourced Volunteered Geographic Information (VGI) provides wide-ranging insights on diverse hazard categories in cycling. In this case study in the city of Freiburg, Germany, hazard perceptions, information about lane types, and the underlying routes were crowdsourced via an open source smartphone application by a small group with the aim of providing cyclists with effective solutions. By dealing with levels of reliability, we show that even a small group of laypersons can generate an extensive and valuable set of VGI consisting of comprehensive hazard categories. We demonstrate that (1) certain hazards are interlinked to specific lane types, and (2) the individual hazard perceptions and objective parameters, i.e., accident data, are often congruent spatially; consequently, (3) dangerous hot spots can be derived. By considering cyclists’ needs, this approach outlines how a people-based perspective can supplement regional planning on the local scale.

Injury surveillance information system: A review of the system requirements

PURPOSE

An injury surveillance information system (ISIS) collects, analyzes, and distributes data on injuries to promote health care delivery. The present study aimed to review the data elements and functional requirements of this system.

METHOD

This study was conducted in 2019. Studies related to injury surveillance system were searched from January 2000 to September 2019 via the databases of PubMed, Web of Knowledge, ScienceDirect, and Scopus. Articles related to the epidemiology of injury, population survey, and letters to the editor were excluded, while the review and research articles related to ISISs were included in the study. Initially 324 articles were identified, and finally 22 studies were selected for review. Having reviewed the articles, the data needed were extracted and the results were synthesized narratively.

RESULTS

The results showed that most of the systems reviewed in this study used the minimum data set suggested by the World Health Organization injury surveillance guidelines along with supplementary data. The main functions considered for the system were injury track, data analysis, report, data linkage, electronic monitoring and data dissemination.

CONCLUSION

ISISs can help to improve healthcare planning and injury prevention. Since different countries have various technical and organizational infrastructures, it is essential to identify system requirements in different settings.

Cyclist crash rates and risk factors in a prospective cohort in seven European cities

Increased cycling uptake can improve population health, but barriers include real and perceived risks. Crash risk factors are important to understand in order to improve safety and increase cycling uptake. Many studies of cycling crash risk are based on combining diverse sources of crash and exposure data, such as police databases (crashes) and travel surveys (exposure), based on shared geography and time. When conflating crash and exposure data from different sources, the risk factors that can be quantified are only those variables common to both datasets, which tend to be limited to geography (e.g. countries, provinces, municipalities) and a few general road user characteristics (e.g. gender and age strata). The Physical Activity through Sustainable Transport Approaches (PASTA) project was a prospective cohort study that collected both crash and exposure data from seven European cities (Antwerp, Barcelona, London, Örebro, Rome, Vienna and Zürich). The goal of this research was to use data from the PASTA project to quantify exposure-adjusted crash rates and model adjusted crash risk factors, including detailed sociodemographic characteristics, attitudes about transportation, neighbourhood built environment features and location by city. We used negative binomial regression to model the influence of risk factors independent of exposure. Of the 4,180 cyclists, 10.2 % reported 535 crashes. We found that overall crash rates were 6.7 times higher in London, the city with the highest crash rate, relative to Örebro, the city with the lowest rate. Differences in overall crash rates between cities are driven largely by crashes that did not require medical treatment and that involved motor-vehicles. In a parsimonious crash risk model, we found higher crash risks for less frequent cyclists, men, those who perceive cycling to not be well regarded in their neighbourhood, and those who live in areas of very high building density. Longitudinal collection of crash and exposure data can provide important insights into individual differences in crash risk. Substantial differences in crash risks between cities, neighbourhoods and population groups suggest there is great potential for improvement in cycling safety.

Cycling related major trauma in Ireland

INTRODUCTION

Cycling as a means of transport or recreational activity is increasing in popularity in Ireland. However, increasing numbers of cyclists may lead to an increased number of bicycle collisions and fatalities. The Road Safety Authority is the statutory body for road safety in Ireland but uses police data alone to collate cycling collision statistics. This may lead to an underestimation of cycling injuries in Ireland. Using hospital statistics may provide a greater understanding of cycling trauma in Ireland.

OBJECTIVE

The present study examines cycling related trauma in Ireland using the Major Trauma Audit (MTA) data collected via the Trauma and Research Network (TARN) from hospitals in Ireland for the period 2014 to 2016. The database was interrogated for demographics, mechanism of injury, injury characteristics and patient outcomes.

RESULTS

There were 410 cycling collisions recorded in the TARN database which represented 4.4% of trauma captured by TARN for the study period. Of this cohort 79% were male compared with 58% in the overall (TARN) trauma cohort (p < 0.001) and the median (IQR) age was 43.8 years (31.0, 55.7) which is younger than the median (IQR) of 58.9 (36.2, 76.0) years for the overall trauma cohort (p < 0.001). Cycling collisions had a median (IQR) injury severity score (ISS) of 10 (9, 20) which was higher than the overall trauma cohort ISS of 9 (9, 17). Of the mechanisms observed for cycling trauma, 31.7% (n = 130) had a collision with a motor vehicle. Of those who did not wear a helmet, 52.2% (n = 47) sustained a head injury compared with 27.5% (n = 44) in the group who were wearing a helmet (p < 0.001).

CONCLUSION

The TARN data presented in this paper builds a more complete overview of the burden of cycling collisions in Ireland. Particular points of focus are that serious cycling injuries occur in a predominantly male population, and that only around 30% of cases are recorded as involving a motor vehicle, with the majority having an unknown mechanism of injury. There was an association between helmets and head injuries in this study, but there are likely other contributing factors such as mechanism of injury, velocity or cycling infrastructure. Using hospital data such as the MTA provides valuable information on the injuries sustained by cyclists, but more prospective studies to capture injury mechanism and contributing factors are needed.

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

OBJECTIVE

Identifiable 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.

METHODS

Legal 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.

RESULTS

Thirty-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.

CONCLUSIONS

It 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.

Are multiple speed cameras more effective than a single one? Causal analysis of the safety impacts of multiple speed cameras

Most previous studies investigate the safety effects of a single speed camera, ignoring the potential impacts from adjacent speed cameras. The mutual influence between two or even more adjacent speed cameras is a relevant attribute worth taking into account when evaluating the safety impacts of speed cameras. This paper investigates the safety effects of two or more speed cameras observed within a specific radius which are defined as multiple speed cameras. A total of 464 speed cameras at treated sites and 3119 control sites are observed and related to road traffic accident data from 1999 to 2007. The effects of multiple speed cameras are evaluated using pairwise comparisons between treatment units with different doses based on the propensity score methods. The spatial effect of multiple speed cameras is investigated by testing various radii. There are two major findings in this study. First, sites with multiple speed cameras perform better in reducing the absolute number of road accidents than those with a single camera. Second, speed camera sites are found to be most effective with a radius of 200 m. For a radius of 200 m and 300 m, the reduction in the personal injury collisions by multiple speed cameras are 21.4 % and 13.2 % more than a single camera. Our results also suggest that multiple speed cameras are effective within a small radius (200 m and 300 m).

Identifying the causes of road traffic collisions: Using police officers' expertise to improve the reporting of contributory factors data

Worldwide, road crashes are a major course of death and serious injury. Police reports provide a rich source of data on the proximal causes (e.g., impairment by alcohol, failure to look properly) of road traffic collisions. Yet, road safety research has raised concerns about the quality and reliability of police reported data. In the UK crash report form, contributory factors are categorised (e.g., vehicle defects, driver error or reaction) to aid police officers in identifying appropriate factors. However, discord between the classification of contributory factors in crash reports and police officers' own categorical perceptions may lead to misunderstanding, and in turn, misreporting of contributory factors. The current investigation recruited 162 police officers to report their perceptions of the relations among contributory factors in the UK crash report form. Hierarchical clustering analysis was used to identify an optimal category structure based on police officers' perceptions. The clustering analysis identified a classification system with seven or eleven categories of contributory factors, maximising the internal coherence of categories and minimising discord with police officers' perceptions. The findings also yield new insights into police officers' perceptions of crash causation and demonstrate how statistical techniques can be used to inform the design of road traffic collision report forms.

A critical overview of driver recording tools

INTRODUCTION

Technological advancements during recent decades have led to the development of a wide array of tools and methods in order to record driving behavior and measure various aspects of driving performance. The aim of the present study is to present and comparatively assess the various driver recording tools that researchers have at their disposal.

METHOD

In order to achieve this aim, a multitude of published studies from the international literature have been examined based on the driver recording methodologies that have been implemented. An examination of more traditional survey methods (questionnaires, police reports, and direct observer methods) is initially conducted, followed by investigating issues pertinent to the use of driving simulators. Afterwards, an extensive section is provided for naturalistic driving data tools, including the utilization of on-board diagnostics (OBD) and in-vehicle data recorders (IVDRs). Lastly, in-depth incident analysis and the exploitation of smartphone data are discussed.

RESULTS

A critical synthesis of the results is conducted, providing the advantages and disadvantages of utilizing each tool and including additional knowledge regarding ease of experimental implementation, data handling issues, impacts on subsequent analyses, as well as the respective cost parameters.

CONCLUSIONS

New technologies provide undeniably powerful tools that allow for seamless data handling, storage, and analysis, such as smartphones and in-vehicle data recorders. However, this sometimes comes at considerable costs (which may or may not pay off at a later stage), while legacy driver recording methods still have their own niches to fill in research. Practical Applications: The present research supports researchers when designing driver behavior monitoring studies. The present work enables better scheduling and pacing of research activities, but can also provide insights for the distribution of research funds.

Road Safety in Low-Income Countries: State of Knowledge and Future Directions

Road safety in low-income countries (LICs) remains a major concern. Given the expected increase in traffic exposure due to the relatively rapid motorisation of transport in LICs, it is imperative to better understand the underlying mechanisms of road safety. This in turn will allow for planning cost-effective road safety improvement programs in a timely manner. With the general aim of improving road safety in LICs, this paper discusses the state of knowledge and proposes a number of future research directions developed from literature reviews and expert elicitation. Our study takes a holistic approach based on the Safe Systems framework and the framework for the UN Decade of Action for Road Safety. We focused mostly on examining the problem from traffic engineering and safety policy standpoints, but also touched upon other sectors, including public health and social sciences. We identified ten focus areas relating to (i) under-reporting; (ii) global best practices; (iii) vulnerable groups; (iv) disabilities; (v) road crash costing; (vi) vehicle safety; (vii) proactive approaches; (viii) data challenges; (ix) social/behavioural aspects; and (x) capacity building. Based on our findings, future research ought to focus on improvement of data systems, understanding the impact of and addressing non-fatal injuries, improving estimates on the economic burden, implementation research to scale up programs and transfer learnings, as well as capacity development. Our recommendations, which relate to both empirical and methodological frontiers, would lead to noteworthy improvements in the way road safety data collection and research is conducted in the context of LICs.

Errors in accident data, its types, causes and methods of rectification-analysis of the literature

Most of the decisions taken to improve road safety are based on accident data, which makes it the back bone of any country's road safety system. Errors in this data will lead to misidentification of black spots and hazardous road segments, projection of false estimates pertinent to accidents and fatality rates, and detection of wrong parameters responsible for accident occurrence, thereby making the entire road safety exercise ineffective. Its extent varies from country to country depending upon various factors. Knowing the type of error in the accident data and the factors causing it enables the application of the correct method for its rectification. Therefore there is a need for a systematic literature review that addresses the topic at a global level. This paper fulfils the above research gap by providing a synthesis of literature for the different types of errors found in the accident data of 46 countries across the six regions of the world. The errors are classified and discussed with respect to each type and analysed with respect to income level; assessment with regard to the magnitude for each type is provided; followed by the different causes that result in their occurrence, and the various methods used to address each type of error. Among high-income countries the extent of error in reporting slight, severe, non-fatal and fatal injury accidents varied between 39-82%, 16-52%, 12-84%, and 0-31% respectively. For middle-income countries the error for the same categories varied between 93-98%, 32.5-96%, 34-99% and 0.5-89.5% respectively. The only four studies available for low-income countries showed that the error in reporting non-fatal and fatal accidents varied between 69-80% and 0-61% respectively. The logistic relation of error in accident data reporting, dichotomised at 50%, indicated that as the income level of a country increases the probability of having less error in accident data also increases. Average error in recording information related to the variables in the categories of location, victim's information, vehicle's information, and environment was 27%, 37%, 16% and 19% respectively. Among the causes identified for errors in accident data reporting, Policing System was found to be the most important. Overall 26 causes of errors in accident data were discussed out of which 12 were related to reporting and 14 were related to recording. "Capture-Recapture" was the most widely used method among the 11 different methods: that can be used for the rectification of under-reporting. There were 12 studies pertinent to the rectification of accident location and almost all of them utilised a Geographical Information System (GIS) platform coupled with a matching algorithm to estimate the correct location. It is recommended that the policing system should be reformed and public awareness should be created to help reduce errors in accident data.

Crash data quality for road safety research: Current state and future directions

Crash databases are one of the primary data sources for road safety research. Therefore, their quality is fundamental for the accuracy of crash analyses and, consequently the design of effective countermeasures. Although crash data often suffer from correctness and completeness issues, these are rarely discussed or addressed in crash analyses. Crash reports aim to answer the five "W" questions (i.e. When?, Where?, What?, Who? and Why?) of each crash by including a range of attributes. This paper reviews current literature on the state of crash data quality for each of these questions separately. The most serious data quality issues appear to be: inaccuracies in crash location and time, difficulties in data linkage (e.g. with traffic data) due to inconsistencies in databases, severity misclassification, inaccuracies and incompleteness of involved users' demographics and inaccurate identification of crash contributory factors. It is shown that the extent and the severity of data quality issues are not equal between attributes and the level of impact in road safety analyses is not yet entirely known. This paper highlights areas that require further research and provides some suggestions for the development of intelligent crash reporting systems.

Building capacity for injury prevention: a process evaluation of a replication of the Cardiff Violence Prevention Programme in the Southeastern USA

OBJECTIVES

Violence is a major public health problem in the USA. In 2016, more than 1.6 million assault-related injuries were treated in US emergency departments (EDs). Unfortunately, information about the magnitude and patterns of violent incidents is often incomplete and underreported to law enforcement (LE). In an effort to identify more complete information on violence for the development of prevention programme, a cross-sectoral Cardiff Violence Prevention Programme (Cardiff Model) partnership was established at a large, urban ED with a level I trauma designation and local metropolitan LE agency in the Atlanta, Georgia metropolitan area. The Cardiff Model is a promising violence prevention approach that promotes combining injury data from hospitals and LE. The objective was to describe the Cardiff Model implementation and collaboration between hospital and LE partners.

METHODS

The Cardiff Model was replicated in the USA. A process evaluation was conducted by reviewing project materials, nurse surveys and interviews and ED-LE records.

RESULTS

Cardiff Model replication centred around four activities: (1) collaboration between the hospital and LE to form a community safety partnership locally called the US Injury Prevention Partnership; (2) building hospital capacity for data collection; (3) data aggregation and analysis and (4) developing and implementing violence prevention interventions based on the data.

CONCLUSIONS

The Cardiff Model can be implemented in the USA for sustainable violent injury data surveillance and sharing. Key components include building a strong ED-LE partnership, communicating with each other and hospital staff, engaging in capacity building and sustainability planning.