Vehicle Crash Data Research Articles

Rideshare Trips and Alcohol-Involved Motor Vehicle Crashes in Chicago

Rideshare companies such as Uber and Lyft have substantially changed transportation markets in the United States and globally. The aim of this study was to examine whether ridesharing is associated with reductions in alcohol-involved crashes. This case-series study used highly spatially and temporally resolved trip-level rideshare data and motor vehicle crash data from the Chicago Data Portal from November 2018 to December 2019. The units of analysis were motor vehicle crashes in Chicago. Events of interest were 962 crashes that police indicated were alcohol involved. The comparison group was 962 non-alcohol-involved crashes that occurred in the same census tract, matched 1:1. The exposure of interest was the density per square mile of rideshare trips that were in progress at the time of the crash, calculated using a kernel density function around the estimated route paths of active trips. A conditional logistic regression compared alcohol involvement to rideshare trip density while adjusting for matching and relevant time-varying covariates (taxi trips, precipitation, temperature, holidays). Mean rideshare trip density was 69.0 per square mile (SD = 129.7) at the time and location of alcohol-involved crashes and 105.7 per square mile (SD = 192.6) at the time and location of non-alcohol-involved crashes. After controlling for covariates, the conditional logistic regression model identified that a standard deviation increase in rideshare trips per square mile at the crash location was associated with 23% decreased odds that the crash location was alcohol involved (odds ratio = 0.771; 95% confidence interval [0.594, 0.878]). Ridesharing may replace motor vehicle trips by alcohol-impaired drivers.

A semantic embedding methodology for motor vehicle crash records: A case study of traffic safety in Manhattan Borough of New York City

This study introduces a hybrid Latent Dirichlet Allocation (LDA) model to excavate hidden crash patterns from the large-scale crash dataset. External semantic descriptions have been attached to raw GPS coordinates of crash events. The K-means clustering algorithm is first applied to determine land use characteristics of crash points by grouping surrounding Points of Interests (POIs). Then, each crash record is transformed into a formalized label consisting of land use, Annual Average Daily Traffic (AADT), and time stamps, allowing the analysis of massive traffic crash data as document corpora. Finally, a data-driven modeling approach based on the LDA is conducted to discover hidden crash patterns from traffic crash records combining the external semantic information. The approach is verified using motor vehicle crash data in Manhattan County of New York City. The novel semantic analysis of crash records provides an effective method to investigate the hidden information in traffic crashes. Identifying spatial-temporal patterns on motor vehicle crashes would provide insights into underlying traffic behaviors for intelligent policy-making and resource allocation.

Malignant mixes: The overlap of motor vehicle crashes and crime in Stockholm, Sweden

Places that concentrate both motor vehicle crashes and crime in Stockholm, Sweden were examined for common socio-economic, land use, and neighborhood characteristics. Using vehicle crash (N = 3,700) and non-traffic crime (N = 605,052) data from 2016 to 2018, hot spots of these two sets of events and their overlap were identified. Crash hot spots captured 14% of the crashes in only 0.5% of Stockholm’s area while crime hot spots captured 27% of the recorded offences in less than 1% of the area. There was overlap in these hot spots for 7% of the crashes and 10% of the crimes. To model predictors, the events were allocated to roadway segments (N = 5511) and tested using a Poisson-Gamma-CAR spatial regression model. Both crashes and crimes exhibit a clear center-periphery pattern that varies over time and by type of crashes and crimes.Crashes tended to occur on roadways with higher average daily traffic (ADT) while crimes tend to occur on roadways with lower ADT with around half occurring on residential streets. Both types of incidents tended to be higher in lower income neighborhoods. Land uses common to both types of harm were the location of underground stations, ATM machines, and alcohol-serving businesses. These are places where people and cars converge at particular times. The effect of these events on police, emergency, and medical services is discussed.

Examining the Effect of Pedestrian Crashes on Vulnerable Populations in North Carolina.

Over the last several years, pedestrian fatalities have increased in North Carolina; however, fatalities represent a small proportion of the total number of nonfatally injured pedestrians. Therefore, we linked statewide motor vehicle crash (MVC) and emergency department (ED) visit data to better understand the circumstances and characteristics of pedestrians treated in North Carolina emergency departments (EDs) for injuries related to crashes. We linked information for pedestrians and bicyclists from 2017 North Carolina police-reported MVCs to population-based ED visit data using hierarchical deterministic methods. We linked 45% of pedestrian crash records to ED visit records (N = 1383 incident ED visits). The rate of pedestrians treated in North Carolina EDs for their injuries was 13.6 ED visits per 100,000 person years. For pedestrian injuries treated in North Carolina EDs, rates (per 100,000 person years in parentheses) were higher among men (15.5) and Black pedestrians (22.7) than women (10.6) and White pedestrians (8.2). Sociodemographic characteristics associated with serious injuries included age, sex, race/ethnicity, and expected source of payment for the ED visit. Crash characteristics associated with serious injuries included posted speed limit, ambient light, number of lanes, and striking vehicle type. The study involved the use of secondary data, not collected specifically for pedestrian injury surveillance. Pedestrian injuries and fatalities place a considerable burden on the population of North Carolina, especially among persons of color and older adults. Injury prevention programs are actively addressing this problem, but more needs to be done.

Developing Crash Severity Model Handling Class Imbalance and Implementing Ordered Nature: Focusing on Elderly Drivers.

Along with the rapid demographic change, there has been increased attention to the risk of vehicle crashes relative to older drivers. Due to senior involvement and their physical vulnerability, it is crucial to develop models that accurately predict the severity of senior-involved crashes. However, the challenge is how to cope with an imbalanced severity class distribution and the ordered nature of crash severities, as these can complicate the classification of the severity of crashes. In that regard, this study investigates the influence of implementing ordinal nature and handling imbalanced class distribution on the prediction performance. Using vehicle crash data in Ohio, U.S., as an example, the eight machine learning classifiers (logistic and ordered logistic regressions and random forest and ordered random forest with or without handling imbalanced classes) are suggested and then compared with their respective performances. The analysis outcomes show that balancing strategy enhances performance in predicting severe crashes. In contrast, the effects of implementing ordinal nature vary across models. Specifically, the ordered random forest classifier without balancing appears to be superior in terms of overall prediction accuracy, and the ordered random forest with balancing outperforms others in predicting severer crashes.

Survivability of a Tesla collision into a non-operational crash attenuator in Mountain View, CA

Objective On March 23, 2018 a Tesla Model X driver was killed in Mountain View, CA after colliding with a previously collapsed crash attenuator at a speed of 31.7 m/s (70.8 mph). The attenuator, which must be repaired following a collision, had been struck 11 days prior by a 2010 Toyota Prius at a minimum speed of 33.9 m/s (75.8 mph). The Toyota driver survived. The maintenance of traffic safety hardware and benefit of the crash attenuator are evaluated. Methods Public information from an NTSB investigation is used to evaluate crash impact severity. Vehicle crash data from Event Data Recorders (EDRs) are compared with test data for the SCI Smart Cushion 100GM crash attenuator. An idealized triangular crash pulse is used to estimate longitudinal peak impact acceleration, and longitudinal impact energy is calculated. The medical outcomes for drivers are considered. Results The California Department of Transportation (Caltrans) maintenance and repair program for safety critical traffic safety hardware was found to be ineffective. The Tesla collision into the nonfunctional attenuator was estimated have roughly three times the impact force to the passenger compartment as it would have been if striking a functional attenuator. Conclusions The Tesla driver could have survived the collision had the crash attenuator been functional. Roadway management and timely maintenance of safety critical traffic hardware are necessary to ensure safety.

Approximation Framework of Embodied Energy of Safety: Insights and Analysis

Transportation safety, as a critical component of an efficient and reliable transportation system, has been extensively studied with respect to societal economic impacts by transportation agencies and policy officials. However, the embodied energy impact of safety, other than induced congestion, is lacking in studies. This research proposes an energy equivalence of safety (EES) framework to provide a holistic view of the long-term energy and fuel consequences of motor vehicle crashes, incorporating both induced congestion and impacts from lost human productivity resulting from injury and fatal accidents and the energy content resulting from all consequences and activities from a crash. The method utilizes a ratio of gross domestic product (GDP) to national energy consumed in a framework that bridges the gap between safety and energy, leveraging extensive studies of the economic impact of motor vehicle crashes. The energy costs per fatal, injury, and property-damage-only (PDO) crashes in gasoline gallon equivalent (GGE) in 2017 were found to be 200,259, 4442, and 439, respectively, which are significantly greater than impacts from induced congestion alone. The results from the motor vehicle crash data show a decreasing trend of EES per crash type from 2010 and 2017, due primarily in part to a decreasing ratio of total energy consumed to GDP over those years. In addition to the temporal analysis, we conducted a spatial analysis addressing national-, state-, and local-level EES comparisons by using the proposed framework, illustrating its applicability.

Integrated Highway Safety Information System (HSIS) datasets by means of a Roadway Safety Data Integrator (RSDI) tool.

The datasets and tool presented in this article are related to the research article entitled “Improving crash predictability of the Highway Safety Manual through optimizing local calibration process” (Dadvar et al., 2020) [1], in which these datasets were used to investigate alternative local calibration methods for the Highway Safety Manual (HSM) [2] local application. The datasets are integrated Highway Safety Information System (HSIS) [3] state-wide rural two-lane, two-way roads (R2U) data from states of Illinois and Washington.The HSIS is a database that maintains motor vehicle crash data, roadway inventory, and traffic volume data for several US states. It is an excellent source of data to highway safety research and can be used to investigate many research questions. However, to prepare an analysis-ready roadway safety dataset based on the HSIS or any databases that store the relevant data in multiple different datasets, the researchers should integrate multiple datasets, merge or unmerge and remove certain inconsistent records, and finally clean the integrated dataset. The HSIS staff is usually accommodating and eager to help, but sometimes the nature of data needs is complicated and laborious.A tool named Roadway Safety Data Integrator (RSDI) was developed for combining, segmenting, and selecting homogeneous (unchanged during the study period for certain variables of interest) HSIS roadway segments and also crash assignment by desired crash attributes (e.g., crash severity or type). The RSDI tool can be helpful for integrating different safety-related datasets such as roadway inventory (including grade, curve, and other subsets), traffic volume, and motor vehicle crash data; also, it can do required segmentation and identify the homogeneous roadway segments over the desired years of study that are the basis for development and calibration of the HSM predictive models.The shared datasets contain homogeneous roadway segments, geometric details, and crash data for six years from Illinois (2005–10) and Washington (2010–15). The datasets and RSDI tool would be important sources generally for investigating highway safety research questions and in particular, for HSM-related analyses. The RSDI tool can be used for similar purposes and it is not limited to the HSIS data. It can be used for segmentation and finding homogeneous segments of any datasets that follow linear referencing.The datasets and RSDI tool are hosted in the Mendeley Data repository [4].

Red light camera interventions for reducing traffic violations and traffic crashes: A systematic review.

Red light camera interventions for reducing traffic violations and traffic crashes: A systematic review.

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

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

Data visualisation of vehicle crash using interactive map and data dashboard

Vehicle crash contributes to a significant number of deaths and injuries in human races around the world. The government and the transportation expert work together around the clock to lessen the number of injuries and fatalities cause from vehicle crash issues. To come out with an effective solution whether from improving the trafficking system including road conditions or increasing awareness among drivers, the experts must analyse the data of vehicle crash from the past decades to find the major causes and come out with a decision. The lack of suitable techniques and tools to analyse a large amount of data is a deterrent in analysing vehicle crash datasets, and the process costs a lot of time. Data visualisation is a technique that allows people to display data in a more infographic form via data visualisation models. Therefore, this study aims to promote and elaborate more on data visualisation techniques using the interactive map and data dashboard to display vehicle crash data. Interactive map and data dashboard can help the government and transportation experts to describe vehicle crash data for them to make right decision to lessen car accident issues. The interactive map focuses on displaying vehicle crash data in map presentation while the data dashboard shows some charts and graph that describe the vehicle crash data in the statistical form. This study can be a reference model of creating interactive map and data dashboard or enhancing the capabilities in managing vehicle crash.

Characteristics of Agriculture Related Motor Vehicle Crashes in Rural New York State

ABSTRACTObjective: The Centers for Disease Control and Prevention (CDC) data have shown that agriculture, forestry, and fishing as an occupational group have the third highest rate of work-related roadway crashes. Agriculture-related crashes have been explored in the Midwest and South; however, we know little about agriculture-related crashes in the Northeast, especially in New York.Methods: To better understand this, researchers obtained motor vehicle crash data from 2010 to 2012 from the New York State Department of Motor Vehicles (NYSDMV). These data were then filtered to agriculture-related cases by both vehicle registration type and vehicle body type.Results: We identified 203 agriculture-related vehicle crashes, involving 381 vehicles and 482 people. Of the agriculture incidents, 91.6% caused property damage, while 36.0% caused injury. The case fatality rate for roadway vehicle crashes was nearly five times as great (2.0/0.4 = 5.00) for agriculture versus non-agriculture crashes (p = 0.0003).Conclusion: Using these data as a supplement to the New York Center for Agricultural Medicine and Health’s existing surveillance system provided information useful in setting priorities involving roadway safety.

Pediatric Restraint Use and Injury in New York City Taxis Compared with Other Passenger Vehicles

In New York City (NYC), as in several other U.S. cities, pediatric occupant restraint laws exempt rear-seated passengers in vehicles-for-hire from those that apply to private vehicles. This study compares rear-seated infant, child, adolescent, and teen passenger restraint use and injury in taxis compared with other passenger vehicles. New York State Department of Health Crash Outcome Data Evaluation System (CODES) was analyzed for rear-seated pediatric passengers aged 0–19 years traveling in taxis ( n = 1,631) or other passenger vehicles ( n = 21,984) involved in a crash in NYC 2011–2013. CODES is a probabilistically linked data set comprising emergency department, hospitalization, and Department of Motor Vehicle crash data. Bivariate and multivariable logistic regression odds ratios (OR) are reported with 95% CI. Taxi passenger restraint use was lower than in other passenger vehicles (51.2% vs. 86.7%, p < 0.0001). Use of a child restraint for ages < 8 years was low (5.9%) and one-tenth that of other passenger vehicles. Multivariable adjusted odds of restraint use was 9.80 (8.2–11.7) for other passenger vehicles compared with taxis. Compared with passengers in other vehicles, passengers in taxi crashes were twice as likely to be moderately or severely injured ( p < 0.0001) and twice as likely to have traumatic brain injury ( p = 0.0070). This study documents lower restraint use and higher injury, including traumatic brain injury, for pediatric taxi passengers compared with other passenger vehicles. Improved data systems, surveillance, and enforcement are needed to improve restraint use and reduce injury in children and teens, particularly those in vehicles-for-hire.

Dashboards for Monitoring Congestion and Crashes in Interstate Work Zones

Work zones present challenges to safety and mobility that require agencies to balance limited resources with vital traffic management activities. Extensive literature exists regarding the impact of congestion and recommendations for work zone design to provide safe and efficient traffic operations. However, it is often infeasible or unsafe to inspect every work zone within an agency’s jurisdiction, so it is important to obtain operational feedback regarding congestion and crashes in work zones to prioritize inspection activities. This paper outlines the use of connected vehicle speed data and crash report data to identify operational performance problems in work zones. This is a way to provide feedback to queuing models used to design maintenance-of-traffic (MOT) plans. A weekly work zone report and dashboards were developed for use by the Indiana Department of Transportation (INDOT) for the purpose of assessing and improving both mobility and safety in work zones. The study has developed a mile-hours of congestion graph, frequency of speed delta heat map, congestion profile graph, and the Route Builder interactive application to comprehensively visualize work zone performance. This weekly report provides a mechanism for agency staff to maintain situational awareness of which work zones were most challenging for queues and during what periods those were likely to occur. In one case study, the reports were used to identify and mitigate operational performance problems in a work zone within 4 weeks, reducing congestion and crash rates. The integration of these data provided project managers with quantitative information about traffic mobility and performance of work zones for informed decision-making during the construction season.

Bayesian Poisson hierarchical models for crash data analysis: Investigating the impact of model choice on site-specific predictions

The Poisson-gamma (PG) and Poisson-lognormal (PLN) regression models are among the most popular means for motor vehicle crash data analysis. Both models belong to the Poisson-hierarchical family of models. While numerous studies have compared the overall performance of alternative Bayesian Poisson-hierarchical models, little research has addressed the impact of model choice on the expected crash frequency prediction at individual sites. This paper sought to examine whether there are any trends among candidate models predictions e.g., that an alternative model’s prediction for sites with certain conditions tends to be higher (or lower) than that from another model. In addition to the PG and PLN models, this research formulated a new member of the Poisson-hierarchical family of models: the Poisson-inverse gamma (PIGam). Three field datasets (from Texas, Michigan and Indiana) covering a wide range of over-dispersion characteristics were selected for analysis.This study demonstrated that the model choice can be critical when the calibrated models are used for prediction at new sites, especially when the data are highly over-dispersed. For all three datasets, the PIGam model would predict higher expected crash frequencies than would the PLN and PG models, in order, indicating a clear link between the models predictions and the shape of their mixing distributions (i.e., gamma, lognormal, and inverse gamma, respectively). The thicker tail of the PIGam and PLN models (in order) may provide an advantage when the data are highly over-dispersed. The analysis results also illustrated a major deficiency of the Deviance Information Criterion (DIC) in comparing the goodness-of-fit of hierarchical models; models with drastically different set of coefficients (and thus predictions for new sites) may yield similar DIC values, because the DIC only accounts for the parameters in the lowest (observation) level of the hierarchy and ignores the higher levels (regression coefficients).

Application of Extreme Value Theory to Crash Data Analysis.

A parametric model obtained by fitting a set of data to a function generally uses a procedure such as maximum likelihood or least squares. In general this will generate the best estimate for the distribution of the data overall but will not necessarily generate a reasonable estimation for the tail of the distribution unless the function fitted resembles the underlying distribution function. A distribution function can represent an estimate that is significantly different from the actual tail data, while the bulk of the data is reasonably represented by the central part of the fitted distribution. Extreme value theory can be used to improve the predictive capabilities of the fitted function in the tail region. In this study the peak-over-threshold approach from the extreme value theory was utilized to show that it is possible to obtain a better fit of the tail of a distribution than the procedures that use the entire distribution only. Additional constraints, on the current use of the extreme value approach with respect to the selection of the threshold (an estimate of the beginning of the tail region) that minimize the sensitivity to individual data samples associated with the tail section as well as contamination from the central distribution are used. Once the threshold is determined, the maximum likelihood method was used to fit the exceedances with the Generalized Pareto Distribution to obtain the tail distribution. The approach was then used in the analysis of airbag inflator pressure data from tank tests, crash velocity distribution and mass distribution from the field crash data (NASS). From the examples, the extreme (tail) distributions were better estimated with the Generalized Pareto Distribution, than a single overall distribution, along with the probability of the occurrence for a given extreme value, or a rare observation such as a high speed crash. It was concluded that the peak-over-threshold approach from extreme value theory can be a useful tool in the vehicle crash, biomechanics and injury tolerance data analysis and in estimation of the occurrence probability of an extreme phenomenon given a set of accurate observations.

Expert Perspectives on Time Sensitivity and a Related Metric for Children Involved in Motor Vehicle Crashes

ObjectiveAdvanced Automatic Crash Notification (AACN) uses vehicle telemetry data to predict risk of serious injury among motor vehicle crash occupants and can thus improve the accuracy with which injured children are triaged by first responders. To better define serious injury for AACN systems (which typically use Abbreviated Injury Scale [AIS] metrics), an age-specific approach evaluating severity, time sensitivity (TS), and predictability of injury has been developed. This study outlines the development of the TS score. MethodsThe 95% most frequent AIS 2+ injuries in a national motor vehicle crash data set spanning 2000 to 2011 were determined for the following age groups: 0 to 4, 5 to 9, 10 to 14, and 15 to 18 years. For each age-specific injury, clinicians with pediatric trauma expertise were asked if treatment at a trauma center was required and were asked about the urgency of treatment. A TS score (range 0–1) was calculated by combining the mean trauma center decision and urgency scores. ResultsA total of 30 to 32 responses were obtained for each age-specific injury. The most frequent motor vehicle crash–induced injuries in the younger groups received significantly higher scores than those in the older groups (median TS score 0 to 4 years: 0.89, 5–9 years: 0.87, 10–14 years: 0.82, 15–18 years: 0.72, P < .001). Large variations in TS existed within each AIS severity level; for example, scores among AIS 2 injuries in 0- to 4-year-olds ranged from 0.12 to 0.98. ConclusionsThe TS of common pediatric injuries varies on the basis of age and may not be accurately reflected by AIS metrics. AIS may not capture all aspects of injury that should be considered by AACN systems.

Identifying Vehicular Crash High Risk Locations along Highways via Spatial Autocorrelation Indices and Kernel Density Estimation

Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS approach to examine the spatial patterns of vehicle crashes and determines if they are spatially clustered, dispersed, or random. Moran’s I and Getis-Ord Gi* statistic are employed to examine spatial patterns, clusters mapping of vehicle crash data, and to generate high risk locations along highways. Kernel Density Estimation (KDE) is used to generate crash concentration maps that show the road density of crashes. The proposed approach is evaluated using the 2013 vehicle crash data in the state of Indiana. Results show that the approach is efficient and reliable in identifying vehicle crash hot spots and unsafe road locations.

Effect of Regional Characteristics on Injury Severity in Local Bus Crashes

As the importance of public transportation increases, the management of bus-involved crashes has become a crucial issue for traffic safety. However, there are relatively few studies on crash severity for buses in South Korea. This study investigated factors that influence the severity of injuries that occur in local bus crashes. The study used commercial vehicle crash data from a 5-year period from 2010 through 2014 in South Korea. To determine unobserved regional effects on crash severity, a hierarchical ordered model was applied to the analysis. Individual crash characteristics were set to lower-level variables, and regional characteristics were adopted as upper-level variables. At the lower level, the factors affecting severity of injuries included vehicle speed, vehicle age, road alignment, surface status, road class, and traffic light installation, as found in previous studies. At the upper level, the factors included pavement, emergent medical environment, traffic rate of compliance, and ratio of elderly in the community. There was a 5.1% unobserved variation between regions from the intraclass correlation analysis. The validity of a hierarchical model for local bus crashes was verified by applying the model to other long-distance buses, and it appeared there were no regional effects. This study found a regional effect for local bus crash severity, and thus this factor is important when developing prevention plans to reduce local bus crashes. These results contribute to the study of traffic safety.

How to Detect and Remove Temporal Autocorrelation in Vehicular Crash Data

Temporal autocorrelation (also called serial correlation) refers to the relationship between successive values (i.e. lags) of the same variable. Although it has long been a major concern in time series models, however, in-depth treatments of temporal autocorrelation in modeling vehicle crash data are lacking. This paper presents several test statistics to detect the amount of temporal autocorrelation and its level of significance in crash data. The tests employed are: 1) the Durbin-Watson (DW); 2) the Breusch-Godfrey (LM); and 3) the Ljung-Box Q (LBQ). When temporal autocorrelation is statistically significant in crash data, it could adversely bias the parameter estimates. As such, if present, temporal autocorrelation should be removed prior to use the data in crash modeling. Two procedures are presented in this paper to remove the temporal autocorrelation: 1) Differencing; and 2) the Cochrane-Orcutt method.