Computational methodology to predict injury risk for motor vehicle crash victims: A framework for improving Advanced Automatic Crash Notification systems

Advanced Automatic Crash Notification (AACN) systems, capable of predicting post-crash injury severity and subsequent automatic transfer of injury assessment data to emergency medical services, may significantly improve the timeliness, appropriateness, and efficacy of care provided. The estimation of injury severity based on statistical field data, as incorporated in current AACN systems, lack specificity and accuracy to identify the risk of life-threatening conditions. To enhance the existing AACN framework, the goal of the current study was to develop a computational methodology to predict risk of injury in specific body regions based on specific characteristics of the crash, occupant and vehicle. The computational technique involved multibody models of the vehicle and the occupant to simulate the case-specific occupant dynamics and subsequently predict the injury risk using established physical metrics. To demonstrate the computational-based injury prediction methodology, three frontal crash cases involving adult drivers in passenger cars were extracted from the US National Automotive Sampling System Crashworthiness Data System. The representative vehicle model, anthropometrically scaled model of the occupant and kinematic information related to the crash cases, selected at different severities, were used for the blinded verification of injury risk estimations in five different body regions. When compared to existing statistical algorithms, the current computational methodology is a significant improvement toward post-crash injury prediction specifically tailored to individual attributes of the crash. Variations in the initial posture of the driver, analyzed as a pre-crash variable, were shown to have a significant effect on the injury risk.     

The joint analysis of injury severity of drivers in two-vehicle crashes accommodating seat belt use endogeneity

The current study contributes to the existing injury severity modeling literature by developing a multivariate probit model of injury severity and seat belt use decisions of both drivers involved in two-vehicle crashes. The modeling approach enables the joint modeling of the injury severity of multiple individuals involved in a crash, while also recognizing the endogeneity of seat belt use in predicting injury severity levels as well as accommodating unobserved heterogeneity in the effects of variables. The proposed model is applied to analyze the injury severity of drivers involved in two-vehicle road crashes in Denmark.The empirical analysis provides strong support for the notion that people offset the restraint benefits of seat belt use by driving more aggressively. Also, men and those individuals driving heavy vehicles have a lower injury risk than women and those driving lighter vehicles, respectively. At the same time, men and individuals driving heavy vehicles pose more of a danger to other drivers on the roadway when involved in a crash. Other important determinants of injury severity include speed limit on roadways where crash occurs, the presence (or absence) of center dividers (median barriers), and whether the crash involves a head-on collision. These and other results are discussed, along with implications for countermeasures to reduce injury severities in crashes. The analysis also underscores the importance of considering injury severity at a crash level, while accommodating seat belt endogeneity effects and unobserved heterogeneity effects.     

Accident severity analysis using ordered probit model

To reduce injuries in road crashes, better understanding is needed between the relationship of injury severity and risk factors. This study seeks to identify the contributing factors affecting crash severity with broad considerations of driver characteristics, roadway features, vehicle types, pedestrian characteristics and crash characteristics using an ordered probit model. It also explores how the interaction of these factors will affect accident severity risk. Three types of accidents were investigated: two-vehicle crashes, single vehicle crashes and pedestrian accidents. The reported crash data in Singapore from 1992 to 2001 were used to illustrate the process of parameter estimation. Several factors such as vehicle type, road type, collision type, location type, pedestrian age, time of day of accident occurrence were found to be significantly associated with injury severity. It was also found that injury severity decreases over time for the three types of accident investigated.     

Effects of crash warning systems on rear-end crash avoidance behavior under fog conditions

Reduced visibility conditions increase both the probability of rear-end crash occurrences and their severity. Crash warning systems that employ data from connected vehicles have potential to improve vehicle safety by assisting drivers to be aware of the imminent situations ahead in advance and then taking timely crash avoidance action(s). This study provides a driving simulator study to evaluate the effectiveness of the Head-up Display warning system and the audio warning system on drivers’ crash avoidance performance when the leading vehicle makes an emergency stop under fog conditions. Drivers’ throttle release time, brake transition time, perception response time, brake reaction time, minimum modified time-to-collision, and maximum brake pedal pressure are assessed for the analysis. According to the results, the crash warning system can help decrease drivers’ reaction time and reduce the probability of rear-end crashes. In addition, the effects of fog level and drivers’ characteristics including gender and age are also investigated in this study. The findings of this study are helpful to car manufacturers in designing rear-end crash warning systems that enhance the effectiveness of the system’s application under fog conditions.     

Severity of injury resulting from pedestrian–vehicle crashes: What can we learn from examining the built environment?

This paper examines the impact of personal and environmental characteristics on severity of injuries sustained in pedestrian–vehicle crashes using a generalized ordered probit model. The data covers 2000–2004 of pedestrian–vehicle crashes taken from police incident reports for Baltimore City and supplemented with local land use, urban form and transportation information specific to the individual crash locations. The results on personal and behavioral variables confirm previous findings. Women pedestrians involved in crashes tend to be injured less frequently than their male counterparts; children have an increased likelihood of sustaining injuries and older persons are more likely to be fatally injured. Pedestrians who cross against the traffic signal, are not in a crosswalk and are involved in a crash after dark are associated with greater injury risk. Of the built environment policy variables of interest, transit access and greater pedestrian connectivity, such as central city areas, are significant and negatively associated with injury severity. These results suggest that the environmental conditions should be given more scrutiny and be an important consideration when evaluating and planning for pedestrian safety.     

Lights,camera, legal action! The effectiveness of red light cameras on collisions in Los Angeles

This study estimates the effect of red light cameras (henceforth cameras) on collisions under the Los Angeles Automated Photo Enforcement Program that ran from 2006 to 2011. To control for selection bias and unobservables, a data set is constructed such that intersections with cameras are compared to control groups of nearby intersections without cameras, matched on observable characteristics. To capture potential spillover effects of cameras, control groups at various distances from the intersections with cameras are considered. A Poisson panel data model with random coefficients is applied to these data and estimated using Bayesian methods. The program suffered from weaknesses in enforcement. The city’s courts did not uphold citations and this dampened the effect cameras had on drivers. These problems are accounted for in modeling. Controlling for these concerns, results indicate that the cameras decreased red light running related collisions, but increased right-angle and injury collisions, as well as collisions overall.     

A Bayesian Network model for contextual versus non-contextual driving behavior assessment

Driving behavior is generally considered to be one of the most important factors in crash occurrence. This paper aims to evaluate the benefits of utilizing context-relevant information in the driving behavior assessment process (i.e. contextual driving behavior assessment approach). We use a Bayesian Network (BN) model that investigates the relationships between GPS driving observations, individual driving behavior, individual driving risks, and individual crash frequency. In contrast to prior studies without context information (i.e. non-contextual approach), the data used in the BN approach is a combination of contextual features in the surrounding environment that may contribute to crash risk, such as road conditions surrounding the vehicle of interest and dynamic traffic flow information, as well as the non-contextual data such as instantaneous driving speed and the acceleration/deceleration of a vehicle. An information-aggregation mechanism is developed to aggregates massive amounts of vehicle GPS data points, kinematic events and context information into drivel-level data. With the proposed model, driving behavior risks for drivers is assessed and the relationship between contextual driving behavior and crash occurrence is established. The analysis results in the case study section show that the contextual model has significantly better performance than the non-contextual model, and that drivers who drive at a speed faster than others or much slower than the speed limit at the ramp, and with more rapid acceleration or deceleration on freeways are more likely to be involved in crash events. In addition, younger drivers, and female drivers with higher VMT are found to have higher crash risk.     

Estimating the value of risk reductions for car drivers when pedestrians are involved: a case study in Spain

We estimated the benefits associated with reducing fatal and severe injuries from traffic accidents using a stated choice experiment where choice situations were generated through a statistically efficient design. Specifically, the risk variables were defined as the expected annual number of vehicle car-users that suffered their death or were severely injured in a traffic accident. In addition, and differing from previous research, the number of pedestrians that died or were severely injured in traffic accidents per year was also included as a risk attribute in the choice experiment, to attempt at measuring drivers’ willingness to pay to reduce the risk of hitting pedestrians in a crash. The empirical setting was a choice of route for a particular trip that a sample of car drivers periodically undertakes in Tenerife, Spain. Models were estimated accounting for random taste heterogeneity and pseudo-panel data correlation. The median of the distribution of simulated parameters was used to obtain a representative measure for the monetary valuation of risk reductions. We found that the ratio between the values of reducing the risk of suffering a serious injury and that of reducing a fatality was approximately 18 %. Further, and quite novel, we also found that the value of reducing a pedestrian fatality was 39 % of the value of reducing a car occupant fatality.     

Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model

The present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation in econometric crash‐severity models. The data from 1889 pedestrian‐related crashes at 318 signalized intersections between 2008 and 2012 were elaborately collected from the Traffic Accident Database System maintained by the Hong Kong Transport Department. To account for the cross‐intersection heterogeneity, a Bayesian hierarchical logit model with uncorrelated and spatially correlated random effects was developed. An intrinsic conditional autoregressive prior was specified for the spatial correlation term. Results revealed that (1) signalized intersections with greater pedestrian volumes generally exhibited a lower injury risk; (2) ignoring the spatial correlation potentially results in reduced model goodness‐of‐fit, an underestimation of variability and standard error of parameter estimates, as well as inconsistent, biased, and erroneous inference; (3) special attention should be paid to the following factors, which led to a significantly higher probability of pedestrians being killed or sustaining severe injury: pedestrian age greater than 65 years, casualties with head injuries, crashes that occurred on footpaths that were not obstructed/overcrowded, heedless or inattentive crossing, crashes on the two‐way carriageway, and those that occurred near tram or light‐rail transit stops. Copyright © 2017 John Wiley & Sons, Ltd.     

A model for exploring the relationship between payment structures,fatigue, crash risk,and regulatory response in a heavy-vehicle transport system

Investigations of heavy vehicle crashes have predominantly taken a reductionist view of accident causation. However, there is growing recognition that broader economic factors play a significant role in producing conditions that exacerbate crash risk, especially in the area of fatigue. The aim of this study was to determine whether agent-based modelling (ABM) may be usefully applied to explore the effect of driver payment methods on driver fatigue, crash-risk, and the response of enforcement agencies to major heavy-vehicle crashes. Simulation results showed that manipulation of payment methods within agent-based models can produce similar patterns of behaviour among simulated drivers as that observed in real world studies. Simulated drivers operating under ‘per-km’ and ‘per-trip’ piece rate incentive systems were significantly more likely to drive while fatigued and subsequently incur all associated issues (loss of license, increased crash risk, increased fines) than those paid under ‘flat-rate’ wage conditions. Further, the pattern of enforcement response required under ‘per-km’ and ‘per-trip’ systems was significantly higher in response to greater numbers of major crashes than in flat-rate regimes. With further refinement and collaborative design, ABMs may prove useful in studying the potential effects of economic policy settings within freight or other transport systems ahead of time.     

On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level

This paper proposes a new spatial multivariate count model to jointly analyze the traffic crash-related counts of pedestrians and bicyclists by injury severity. The modeling framework is applied to predict injury counts at a Census tract level, based on crash data from Manhattan, New York. The results highlight the need to use a multivariate modeling system for the analysis of injury counts by road-user type and injury severity level, while also accommodating spatial dependence effects in injury counts.     

Crash prediction with behavioral and physiological features for advanced vehicle collision avoidance system

Real-time crash prediction is the key component of the Vehicle Collision Avoidance System (VCAS) and other driver assistance systems. The further improvements of predictability requires the systemic estimation of crash risks in the driver-vehicle-environment loop. Therefore, this study designed and validated a prediction method based on the supervised learning model with added behavioral and physiological features. The data samples were extracted from 130 drivers’ simulator driving, and included various features generated from synchronized recording of vehicle dynamics, distance metrics, driving behaviors, fixations and physiological measures. In order to identify the optimal configuration of proposed method, the Discriminant Analysis (DA) with different features and models (i.e. linear or quadratic) was tested to classify the crash samples and non-crash samples. The results demonstrated the significant improvements of accuracy and specificity with added visual and physiological features. The different models also showed significant effects on the characteristics of sensitivity and specificity. These results supported the effectiveness of crash prediction by quantifying drivers’ risky states as inputs. More importantly, such an approach also provides opportunities to integrate the driver state monitoring into other vehicle-mounted systems at the software level.     

A lane-change trajectory model from drivers’ vision view

Car-following and Lane-changing are two fundamental tasks during driving. While many car-following models can be applied, relatively, only a few lane-changing models have been developed. Classical lane-changing models mainly focus on drivers’ lane selection and gap acceptance behaviors, but very limited research has paid attention to formulating detailed lane-changing trajectories. This research aims to fill the gap by proposing a lane-changing trajectory model, which is built directly from drivers’ vision view, to model detailed lane-changing trajectories. A large amount of data of reference angles, defined as the angle changes between the drivers’ vision angle and left or right lane line, were first extracted from the videos recorded by the vehicle traveling data recorders (VTDRs) installed in 11 taxies. A comprehensive data analysis indicates that same drivers show similarity of their daily lane-changing habit but with variety, and different drivers’ lane-change trajectory data show different lane-change “personality” including aggressive or non-aggressive behaviors. Based on these findings, this paper then proposed a hyperbolic tangent lane-change trajectory model to describe drivers’ detailed lane-change trajectories. The model is verified using both real data and simulation. The results show the proposed lane-change trajectory model can successfully describe drivers’ lane-changing trajectories. More importantly, some parameters in the model are directly associated to drivers’ driving characteristics during lane-change. With this unique feature, the proposed model can generate driver-specific lane-change trajectories. Such improvement could contribute to the future development of Advanced Driver Assistance Systems (ADAS).     

Over-reporting vs. overreacting: Commuters’ perceptions of travel times

In a large-scale, real-life peak avoidance experiment, we asked participants to provide estimates of their average in-vehicle travel time during their morning commute. After comparing the reported travel times with the actual corresponding travel times, we found that the average travel times were overstated by a factor of 1.5. We showed that driver- and link-specific characteristics partially explained these exaggerations. Using the stated and revealed preference data, we investigated whether the driver-specific reporting errors were consistent with the drivers’ scheduling behaviors in reality and in hypothetical choice experiments. In both cases, we found no robust evidence that drivers behave as if they misperceive travel times to a similar extent as those they misreported, thereby implying that the reported travel times did not represent the actual or perceived travel times in a truthful manner. The results of this study suggest that caution should be recommended when reported travel time data are used in an uncritical manner during transport research and when determining policy.     

Accidents involving pedestrians with their backs to traffic or facing traffic: an evaluation of crash characteristics and injuries

This paper examines pedestrian anatomical injuries and crash characteristics in back‐to‐traffic and facing‐traffic crashes. Pedestrian crashes involving pedestrians walking along streets (i.e. with their backs to traffic or facing traffic) have been overlooked in literature. Although this is not the most frequent type of crash, the crash consequence to pedestrians is a safety concern. Combining Taiwan A1A2 police‐reported accident data and data from the National Health Insurance Database from years 2003–2013, this paper examines anatomical injuries and crash characteristics in back‐to‐traffic and facing‐traffic crashes. There were a total of 830 and 2267 pedestrian casualties in back‐to‐traffic and facing‐traffic crashes respectively. The injuries sustained by pedestrians and crash characteristics of these two crash types were compared with those of other crossing types of crashes (nearside crash, nearside dart‐out crash, offside crash, and offside dart‐out crash). Odds of various injuries to body regions were estimated using logistic regressions. Key findings include that the percentage of fatalities in back‐to‐traffic crashes is the highest; logistic models reveal that pedestrians in back‐to‐traffic crashes sustained more head, neck, and spinal injuries than did pedestrians in other crash types, and unlit darkness and non‐built‐up roadways were associated with an increased risk of pedestrian head injuries. Several crash features (e.g. unlit darkness, overtaking manoeuvres, phone use by pedestrians and drivers, and intoxicated drivers) are more frequently evident in back‐to‐traffic crashes than in other types of crashes. The current research suggests that in terms of crash consequence, facing traffic is safer than back to traffic. Copyright © 2016 John Wiley & Sons, Ltd.     

Using the Bayesian updating approach to improve the spatial and temporal transferability of real-time crash risk prediction models

This study aimed to improve the spatial and temporal transferability of the real-time crash risk prediction models by using the Bayesian updating approach. Data from California’s I-880N freeway in 2002 and 2009 and the I-5N freeway in 2009 were used. The crash risk models for these three datasets are quite different from each other. The model parameters do not remain stable over time or space. The transferability evaluation results show that the crash risk models cannot be directly transferred across time and space. The updating results indicate that the Bayesian updating approach is effective in improving both spatial and temporal transferability even when new data are limited. The predictive performance of the updated model increases with an increase in the sample size of the new data. In addition, when limited new data are available, updating an existing model is better than developing a model using the limited new data.     

Strava Metro data for bicycle monitoring: a literature review

ABSTRACT

Monitoring bicycle trips is no longer limited to traditional sources, such as travel surveys and counts. Strava, a popular fitness tracker, continuously collects human movement trajectories, and its commercial data service, Strava Metro, has enriched bicycle research opportunities over the last five years. Accrued knowledge from colleagues who have already utilised Strava Metro data can be valuable for those seeking expanded monitoring options. To convey such knowledge, this paper synthesises a data overview, extensive literature review on how the data have been applied to deal with drivers’ bicycle-related issues, and implications for future work. The review results indicate that Strava Metro data have the potential—although finite—to be used to identify various travel patterns, estimate travel demand, analyse route choice, control for exposure in crash models, and assess air pollution exposure. However, several challenges, such as the under-representativeness of the general population, bias towards and away from certain groups, and lack of demographic and trip details at the individual level, prevent researchers from depending entirely on the new data source. Cross-use with other sources and validation of reliability with official data could enhance the potentiality.     

Age related annual crash incidence rate ratios in professional drivers of heavy goods vehicles

ObjectivesEvidence concerning crash risk for older heavy vehicle drivers is sparse, making it difficult to assess if it is prudent to encourage older drivers to remain in the workforce in a climate of labour shortages. The objective of this study was to estimate annual crash rate ratios of older male heavy vehicle drivers relative to their middle aged peers.MethodsData utilized in this study includes all crashes meeting inclusion criteria involving heavy goods vehicles, categorised as rigid trucks and articulated trucks; this data was recorded by the New South Wales Roads and Traffic Authority. The exposure to the risk of a crash was represented by distance travelled for each vehicle type and year, by age of driver, as estimated by the Australian Survey of Motor Vehicle Use. Negative binomial regression modelling was applied to estimate annual crash incidence rate ratios for male drivers in various age groups.ResultsA total of 26,146 crashes occurred in New South Wales during 1999–2006, involving a total of 54,191 vehicles; removing observations that did not meet the inclusion criteria, 19,736 observations remained representing 12,501 crashes. For rigid trucks, the incidence rate ratio for drivers aged 65+ years, compared to 45–54 year olds, was 0.74 (95% CI 0.51, 0.98). For articulated trucks, the annual crash incidence rate ratio for drivers aged 65+ years compared to 45–54 year olds was 1.4 (95% CI 0.96, 1.9), and that for drivers aged 55–64 years compared to 45–54 year olds was 1.1 (95% CI 0.83, 1.3).ConclusionsOlder male professional drivers of heavy goods vehicles have lower risk of crashes in rigid vehicles, possibly due to accrued driving experience and self-selection of healthy individuals remaining in the workforce. Thus, encouraging these drivers to remain in the workforce is appropriate in the climate of labour shortages, as this study provides evidence that to do so would not endanger road safety.     

Effects of intersection field of view on emergent collision avoidance performance at unsignalized intersections: analysis based on driving simulator experiments

Proper intersection sight distance can effectively lower the possibility of intersection accidents. American Association of State Highway and Transportation Officials (2011) provide a series of recommended dimensions of intersection sight triangles for uncontrolled and stop/yield‐controlled intersections. However, in reality, although the actual intersection design for unsignalized intersections satisfies the requirements of sight distance and clear sight triangle in American Association of State Highway and Transportation Officials' guideline, there are still a large number of crashes occurring at unsignalized intersections for drivers running stop/yield signs or failing to slow down. This paper presents a driving simulator study on pre‐crash at intersections under three intersection field of view (IFOV) conditions. The aim was to explore whether better IFOVs at unsignalized intersections improve their emergent collision avoidance performance under an assumption of valid intersection sight distance design. The experimental results show drivers' ability to identify potential hazards to be significantly affected by their IFOVs. As drivers' IFOV improved, drivers were more likely to choose braking actions to avoid collisions. Better IFOVs were also associated with significant increases in brake time to intersection and significant reductions in deceleration rate and crash rate, thus leading to a lower risk of traffic crash involvement. The results indicate that providing a better IFOV for drivers at intersections should be encouraged in practical applications in order to improve drivers' crash avoidance capabilities. Copyright © 2016 John Wiley & Sons, Ltd.     

Simultaneous determination of the equilibrium market penetration and compliance rate of advanced traveler information systems

We consider a specific advanced traveler information systems (ATIS) whose objective is to reduce drivers’ travel time uncertainty with recurrent network congestion through provision of traffic information. Since the provided information is still partial or imperfect, drivers equipped with an ATIS cannot always find the shortest travel time route and thus may not always comply with the advice provided by ATIS. Thus, there are three classes of drivers on a specific day: drivers without ATIS, drivers with ATIS but without compliance with ATIS advice, drivers with ATIS and in compliance with ATIS advice. All three classes of drivers make route choice in a stochastic manner, but with different degree of uncertainty of travel time on the network. In this paper we investigate the interactions among the three classes of drivers in an ATIS environment using a multiple behavior stochastic user equilibrium model. By assuming that the market penetration of ATIS is an increasing function of the actual private gain (time saving minus the cost associated with system use) derived from ATIS service, and the ATIS compliance rate of equipped drivers is given as the probability of the actual travel time of complied drivers being less than that of non-complied drivers, we determine the equilibrium market penetration and compliance rate of ATIS and the resulting equilibrium network flow pattern using an iterative solution procedure.