The effects of age,gender, and crash types on drivers’ injury-related health care costs

There are many studies that evaluate the effects of age, gender, and crash types on crash related injury severity. However, few studies investigate the effects of those crash factors on the crash related health care costs for drivers that are transported to hospital. The purpose of this study is to examine the relationships between drivers’ age, gender, and the crash types, as well as other crash characteristics (e.g., not wearing a seatbelt, weather condition, and fatigued driving), on the crash related health care costs. The South Carolina Crash Outcome Data Evaluation System (SC CODES) from 2005 to 2007 was used to construct six separate hierarchical linear regression models based on drivers’ age and gender. The results suggest that older drivers have higher health care costs than younger drivers and male drivers tend to have higher health care costs than female drivers in the same age group. Overall, single vehicle crashes had the highest health care costs for all drivers. For males older than 64-years old sideswipe crashes are as costly as single vehicle crashes. In general, not wearing a seatbelt, airbag deployment, and speeding were found to be associated with higher health care costs. Distraction-related crashes are more likely to be associated with lower health care costs in most cases. Furthermore this study highlights the value of considering drivers in subgroups, as some factors have different effects on health care costs in different driver groups. Developing an understanding of longer term outcomes of crashes and their characteristics can lead to improvements in vehicle technology, educational materials, and interventions to reduce crash-related health care costs.     

An exploration of alternative intersection designs in the context of Safe System

Fatal and serious injury crashes persist at intersections despite current efforts to address this. Little research specifically investigates the role played by existing intersection design in perpetuating serious intersection crash outcomes despite an increasing move to incorporate Safe System design on to roads. This paper identifies design principles deemed important to align intersection design with Safe System approaches, including exploring the impact of speed and angle on overall kinetic energy of a crash. Existing as well as new intersection designs are presented that are believed to incorporate the identified principles. An assessment is made of the alignment of the new and existing designs with the identified principles.     

Validity of surrogate measures of alcohol involvement when applied to nonfatal crashes

Since the 1970s, nighttime fatal crashes have been used as a surrogate measure for alcohol-related fatalities for crashes for which more direct measures were absent. The validity of this approach was confirmed in 1985 but has not been re-evaluated since. Although this measure has also been applied to identify alcohol involvement in nonfatal crashes, its validity when applied to nonfatal cases has never been determined.The objective of this study was to evaluate the appropriateness of using nighttime crashes as surrogate measures for alcohol impairment when applied to fatal and nonfatal injury and property damage only (PDO) crashes. To do so, we used data from a crash-control design study collected at the roadside in two U.S. states between 1997 and 1999, as well as from the 1997-1999 and 2004-2006 Fatality Analysis Reporting System.The outcome of this study confirms the validity of using nighttime crashes as a surrogate measure for alcohol-related fatalities and supports the use of after-midnight crashes for measuring alcohol involvement in nonfatal and PDO crashes when the number of late-night crashes permits.     

Effect of change in velocity on the development of medical complications in patients with multisystem trauma sustained in vehicular crashes

Medical complications such as sepsis or multiple organ system failure increase the morbidity and mortality associated with injuries sustained in car crashes. This study addresses the question of the association, if any, between one crash characteristic, i.e. change in velocity (Δv), and subsequent medical complications. Data on seventy-six severely injured patients were obtained as part of an in-depth, trauma-center-based study of the biomechanics of vehicular trauma. Factors found to be predictive of the development of complications included patient age≥36 years, Δv≥30 mph, and injury severity score≥25. Vehicle occupants involved in crashes with a Δv≥30 mph had a risk of complications more than five times greater than for those in the lower Δv group. Better knowledge of the dynamics and severity of the crash could help clinical staff anticipate the development of complications and initiate timely prevention strategies.     

Validating a driving simulator using surrogate safety measures

Traffic crash statistics and previous research have shown an increased risk of traffic crashes at signalized intersections. How to diagnose safety problems and develop effective countermeasures to reduce crash rate at intersections is a key task for traffic engineers and researchers. This study aims at investigating whether the driving simulator can be used as a valid tool to assess traffic safety at signalized intersections. In support of the research objective, this simulator validity study was conducted from two perspectives, a traffic parameter (speed) and a safety parameter (crash history). A signalized intersection with as many important features (including roadway geometries, traffic control devices, intersection surroundings, and buildings) was replicated into a high-fidelity driving simulator. A driving simulator experiment with eight scenarios at the intersection were conducted to determine if the subjects' speed behavior and traffic risk patterns in the driving simulator were similar to what were found at the real intersection. The experiment results showed that speed data observed from the field and in the simulator experiment both follow normal distributions and have equal means for each intersection approach, which validated the driving simulator in absolute terms. Furthermore, this study used an innovative approach of using surrogate safety measures from the simulator to contrast with the crash analysis for the field data. The simulator experiment results indicated that compared to the right-turn lane with the low rear-end crash history record (2 crashes), subjects showed a series of more risky behaviors at the right-turn lane with the high rear-end crash history record (16 crashes), including higher deceleration rate (1.80+/-1.20 m/s(2) versus 0.80+/-0.65 m/s(2)), higher non-stop right-turn rate on red (81.67% versus 57.63%), higher right-turn speed as stop line (18.38+/-8.90 km/h versus 14.68+/-6.04 km/h), shorter following distance (30.19+/-13.43 m versus 35.58+/-13.41 m), and higher rear-end probability (9/59=0.153 versus 2/60=0.033). Therefore, the relative validity of driving simulator was well established for the traffic safety studies at signalized intersections.     

Rear seat safer: seating position, restraint use and injuries in children in traffic crashes in Victoria, Australia

Car crashes are a major cause of death and serious injury to children but most analyses of risk are based on US data. The Australian context is different in at least three ways: (1) the proportion of passenger-side airbags, a potential risk to children in front seats, is much lower; (2) unlike in the US, Australian airbags are designed to work with restrained passengers; (3) restraint use for children 0-12 years is high (>90%). Official data drawn from Victorian crash records (n=30,631) were used to calculate relative risks of death or serious injury for children (0-3 years, 4-7 years; 8-12 years) traveling in passenger cars during 1993-1998 and 1999-2004. Over 90% were reportedly wearing a restraint, and 20% were traveling in the front seat. For children under 4 years traveling in the front seat, the relative risk of death was twice as great as when traveling in the rear, and that of serious injury was 60% greater. The relative risk of death whilst traveling in the front seat was almost four times greater for children aged under 1 year. We suggest that serious consideration should be given to mandating rear seating for children, particularly those aged 4 and under.     

Modeling left-turn crash occurrence at signalized intersections by conflicting patterns

In order to better understand the underlying crash mechanisms, left-turn crashes occurring at 197 four-legged signalized intersections over 6 years were classified into nine patterns based on vehicle maneuvers and then were assigned to intersection approaches. Crash frequency of each pattern was modeled at the approach level by mainly using Generalized Estimating Equations (GEE) with the Negative Binomial as the link function to account for the correlation among the crash data. GEE with a binomial logit link function was also applied for patterns with fewer crashes. The Cumulative Residuals test shows that, for correlated left-turn crashes, GEE models usually outperformed basic Negative Binomial models. The estimation results show that there are obvious differences in the factors that cause the occurrence of different left-turn collision patterns. For example, for each pattern, the traffic flows to which the colliding vehicles belong are identified to be significant. The width of the crossing distance (represented by the number of through lanes on the opposing approach of the left-turning traffic) is associated with more left-turn traffic colliding with opposing through traffic (Pattern 5), but with less left-turning traffic colliding with near-side crossing through traffic (Pattern 8). The safety effectiveness of the left-turning signal is not consistent for different crash patterns; "protected" phasing is correlated with fewer Pattern 5 crashes, but with more Pattern 8 crashes. The study indicates that in order to develop efficient countermeasures for left-turn crashes and improve safety at signalized intersections, left-turn crashes should be considered in different patterns.     

Modeling crash outcome probabilities at rural intersections: application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.     

An analysis of pedestrian crashes using a spatial count data model

We develop a methodology to analyze pedestrian crash frequency in relation to neighborhood characteristics and exposure to traffic using a spatial count data model with a Poisson framework that is expanded to consider spatial factors, a methodological innovation. We also develop and apply a methodology for defining urban boundaries in mid-sized metropolitan areas, following the functionality of the urban area. These methodologies are applied to a case study. The case study identifies specific neighborhood sociodemographic characteristics associated with higher pedestrian crash frequencies. It also finds vehicle exposure to be insignificant. Results suggest that neighborhood-level planning policies may best address pedestrian crashes.     

A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods

This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents’ access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference.