Single-vehicle Crashes Research Articles

Injury severity of single-vehicle large-truck crashes: accounting for heterogeneity

This research examines the injury severity of single-vehicle large-truck crashes in Florida while exploring the role of heterogeneity. A random parameter ordered logit (RPOL) model was applied to 27,505 single-vehicle large-truck crashes from 2007 to 2016 in Florida, and the contributing factors were identified. Random parameters and interaction effects were introduced to the model to determine the heterogeneity and its potential sources. The results suggested that driving speed of 76–120 mph and defective tires were the most influential factors in crash injury severity, increasing the probability of severe crashes. Regarding truckers’ attributes, asleep or fatigued conditions and driving under the influence were correlated with a higher possibility of severe crashes. Interestingly, the results showed that truckers from outside the state of Florida were less likely to cause severe single-vehicle large-truck crashes compared to their Floridian counterparts. Y-intersections were also found as a high-risk location for single-vehicle large-truck crashes, leading to more severe outcomes. Regarding heterogeneity, the results indicated that the impacts of driving speed (26–50 mph) and light condition (dark – not lighted) significantly varied among the observations, and these variations could be attributed to driver action, vision obstruction, driver distraction, roadway type and roadway alignment.

The influence of middle-aged male driver profile on driving performance and the effects of three perceptual countermeasures: A simulator study

The human-environment-vehicle triad and how it relates to crashes has long been a topic of discussion, in which the human factor is consistently seen as the leading cause. Recently, more sophisticated approaches to Road Safety have advocated for a road-driver interaction view, in which human characteristics influence road perception and road environment affects driver behavior. This study focuses on road-driver interaction by using a driving simulator. The objective is to investigate how the driver profile influences driving performance and the effects of three countermeasures (peripheral transverse lines before and after the beginning of the curves and roadside poles in the curves). Fifty-six middle-aged male participants drove a non-challenging rural highway simulated scenario based on a real road where many single-vehicle crashes occurred. The drivers' profiles were assessed through their behavioral history measured by a validated version of the Driver Behavior Questionnaire (DBQ) comprising three dimensions: Errors (E), Ordinary Violations (OV), and Aggressive Violations (AV). The relationship between speed and trajectory measures and drivers' profiles was investigated using random-parameter models with heterogeneity in the means. The models' results showed that the DBQ subscale scores in OV explained a considerable part of the heterogeneity found in drivers' performance. Furthermore, the heterogeneity in the means caused by the DBQ subscale scores in OV and E in the presence of peripheral transverse lines indicates a difference in how drivers react to the countermeasures. The peripheral lines were more efficient than roadside poles to moderate speed but did not positively influence all drivers' trajectories. Although the peripheral lines could be seen as an alternative to change driver behavior in a non-challenging or monotonous road environment, the design used in this study should be reviewed.

Crash severity analysis of single-vehicle rollover crashes in Namibia: A mixed logit approach

Road traffic crashes are a leading cause of serious injuries and fatalities globally and place unnecessary developmental and economic burdens on low- and middle-income countries (LMIC) as they account for the vast majority of the world's road related deaths. This is typically due to both the increased frequency of dangerous crash types and the increased severity of said crash types. Rollover crashes while quite rare are a particularly dangerous crash type among other various crash types. In the case of Namibia, rollover crashes reportedly accounted for 34% of both road related injuries and fatalities in Namibia for 2020. When compared to high-income countries the issue of rollover crash severity in Namibia and like sub-Saharan African (SSA) countries becomes apparent. Therefore, it crucial to understand the contributing factors and their associated effects on rollover crash severities in these countries. This study aims to investigate and identify the significant factors influencing crash severities and their associated impact magnitudes on single-vehicle rollover crashes in Namibia by adopting a mixed logit with heterogeneity in means and variances approach to account for unobserved heterogeneity in the data. Although it is not without its limitations the dataset used in this study includes single-vehicles rollover crash instances from 2014 to 2016 within Namibia and is able to provide unique details for the crash observations including various driver, environmental, roadway, and vehicle characteristics. Results from this study indicate several factors including weekends, open roadways, and minibuses to be significantly increasing the crash severity of single-vehicle rollover crashes. Additionally, results provide a basis for which researchers and policy makers can understand rollover crashes in Namibia and adopt an appropriate approach to address this issue, such as, Safe Systems. Such an approach would include but not be limited to the implementation of roadside features, educational campaigns, speed enforcement, and vehicle standards policy.

ANALYSIS OF SINGLE-VEHICLE AND MULTIPLE-VEHICLE CRASHES ALONG THE HAWAN KIBO CRASH CORRIDOR, PLATEAU STATE, NIGERIA

In-depth analysis of the characteristics of road traffic crashes at blackspots or hotspot locations is, generally, insufficient in Nigeria. This is despite the fact that blackspots represent recognized locations with road safety deficiencies and mitigation of crashes at such locations produce multiple benefits. This paper examines characteristics of road traffic crashes along Hawan Kibo route, one of the most recognized crash corridors in Nigeria, with particular emphasis on single-vehicle (SV) and multiple-vehicle (MV) crashes. The data shows that between 2015 and 2019, 355 crashes were recorded (SV: 219; MV: 136), with 1288 persons sustaining injuries (SV: 652; MV: 636) and 121 fatalities (SV: 46; MV: 75). The most important causes of crashes and casualties for SV crashes were brake failure, speed violation, and fatigue; while wrongful overtaking, brake failure and speed violations were the most prominent for MV crashes. Time of day for crashes was not significantly different between SV and MV crashes but number of persons injured per crash was significantly different between them. Though MV crashes were less in number, they appeared to be more severe. There was no statistically significant difference between the fatality rates per crash for SV and MV crashes even though SV fatality rates were significantly less than those for MV crashes. In the light of the fact that the most prominent causes of crashes and casualties are associated with poor human judgment and attitude, the study suggests that more creative and concerted efforts should be made to educate drivers and passengers on road...

Statistical Analysis of Traffic Crashes on Mountainous Freeway Tunnel Sections

Tunnels on mountainous freeways are affected by abrupt changes in brightness, complex geometric alignments, heavy traffic flow, bad weather, and other factors, some of which contribute toward tunnels having more traffic crashes than other sections of the freeway. Previous research, however, has given limited attention to tunnel length and heterogeneity in the parts of the tunnels, such as at the tunnels’ entrance and exit zones. Focusing on 36 tunnels on the Guidu Freeway in China’s Guizhou Province, this study collects data on crashes and their influencing factors over 2 years (2020–2021), constructs a negative binomial panel data random effects model, and analyzes single-vehicle crashes, multi-vehicle crashes, and total crashes. The results show that: 1) multi-vehicle crashes occur throughout the tunnel sections, 2) crashes are more likely to occur in long tunnel sections, 3) the crash frequency from the tunnel entrance zone to the mid zone is higher than in other areas of the tunnel, 4) the crash frequency is higher for circular curve/easy curve tunnel sections than for straight tunnel sections, 5) the crash frequency is higher for downhill and concave curve sections than for flat sections, 6) the crash frequency increases with heavy traffic flow and adverse weather conditions, and 7) the crash frequency increases as road surface skidding resistance and ride quality decrease. These findings can provide theoretical support for engineering improvement and the formulation and revision of specifications for designing freeway tunnel sections, especially in mountainous areas.

Exploring the effect of COVID-19 on driver injury severities in freeway single-vehicle crashes accounting for unobserved heterogeneity

ABSTRACT The COVID-19 pandemic, characterized by travel restrictions and reduced traffic volumes, heightened the risk of severe single-vehicle crashes on Florida's freeways. This study utilized random parameter multinomial logit models, accounting for heterogeneity in means and variances, to analyze driver injury severities in 2020 and compare variations in the magnitude of factors contributed to these injuries across different freeway systems. The estimated models identified 31 statistically significant variables across Florida's major freeways (I-4, I-10, I-75, and I-95). Among these variables, only two—normal driving and restraint usage—were statistically significant across all four freeway systems. Moreover, the model results indicated that factors contributing to severe driver injuries were most prominent on I-95 compared to the other freeways in 2020. These findings improve our understanding of freeway safety measures during a pandemic and provide valuable insights to enhance traffic management strategies for state highway agencies, prioritizing operational safety in potential future pandemics.

Temporal instability and differences in injury severity between restrained and unrestrained drivers in speeding-related crashes

Upon detecting a crash impact, the vehicle restraint system locks the driver in place. However, external factors such as speeding, crash mechanisms, roadway attributes, vehicle type, and the surrounding environment typically contribute to the driver being jostled within the vehicle. As a result, it is crucial to model unrestrained and restrained drivers separately to reveal the true impact of the restraint system and other factors on driver injury severities. This paper aims to explore the differences in factors affecting injury severity for seatbelt-restrained and unrestrained drivers involved in speeding-related crashes while accounting for temporal instability in the investigation. Utilizing crash data from Thailand between 2012 and 2017, mixed logit models with heterogeneity in means and variances were employed to account for multi-layered unobserved heterogeneity. For restrained drivers, the risk of fatal or severe crashes was positively associated with factors such as male drivers, alcohol influence, flush/barrier median roadways, sloped roadways, vans, running off the roadway without roadside guardrails, and nighttime on unlit or lit roads. For unrestrained drivers, the likelihood of fatal or severe injuries increased in crashes involving older drivers, alcohol influence, raised or depressed median roadways, four-lane roadways, passenger cars, running off the roadway without roadside guardrails, and crashes occurring in rainy conditions. The out-of-sample prediction simulation results are particularly significant, as they show the maximum safety benefits achievable solely by using a vehicle's seatbelt system. Likelihood ratio test and predictive comparison findings highlight the considerable combined impact of temporal instability and the non-transferability of restrained and unrestrained driver injury severities across the periods studied. This finding also demonstrates a potential reduction in severe and fatal injury rates by simply replicating restrained driver conditions. The findings should be of value to policymakers, decision-makers, and highway engineers when developing potential countermeasures to improve driver safety and reduce the frequency of severe and fatal speeding-related single-vehicle crashes.

Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis

Although hazardous material (HAZMAT) truck-involved crashes are uncommon compared to other types of traffic crashes, these crashes pose considerable threats to the public, property, and environment due to the unique feature of low probability with high consequences. Using ten-year (2010–2019) crash data from the Fatality Analysis Reporting System (FARS) database, this study applies cluster correspondence analysis to identify the underlying patterns and the associations between the risk factors for HAZMAT-truck-involved fatal crashes. A low-dimensional space projects the categorical variables (including the crash, road, driver, vehicle, and environmental characteristics) into different clusters based on the optimal clustering validation criterion. This study reveals that fatal HAZMAT-truck-involved crashes are highly distinguishable concerning collision types (angle and front-to-front crashes, single-vehicle crashes, and front-to-end crashes) and roadway geometric variables, such as two-way undivided roadways, curve alignments, and high-speed (65 mph or more) urban interstate highways. Driver behavior (distraction, asleep or fatigue, and physical impairment), lighting conditions (dark–lighted and dark–not lighted), and adverse weather are also interrelated. The findings from this study will help HAZMAT carriers, transportation management authorities, and policymakers develop potential targeted countermeasures for HAZMAT-truck-involved crash reduction and safety improvement.

Bicyclist crashes with cars and SUVs: Injury severity and risk factors

Objective The popularity of bicycle travel has increased in recent years alongside a comparable increase in the risk of injury or death for those cyclists. The current study was conducted to investigate the differences in injury outcomes between bicyclists struck by SUVs and those struck by cars and to uncover the mechanisms behind injury patterns that have been observed in past research. Methods We analyzed 71 single-vehicle crashes from the Vulnerable Road User Injury Prevention Alliance pedestrian crash database, focusing on crashes involving an SUV or car. Each crash from this database included an in-depth analysis of police reports, bicyclist medical records, crash reconstructions, and injury attribution by a panel of experts. Results Bicyclist injuries from crashes with SUVs were more severe than those from crashes with cars, particularly with respect to head injuries. The greater injury severity associated with SUVs was related to these vehicles’ tendency to produce injuries from ground contact or from vehicle components near the ground. In contrast, cars were much less likely to produce ground injuries and instead tended to distribute less severe injuries across multiple vehicle components. Conclusions The pattern of results suggest that the size and shape of SUV front ends are responsible for the differences in bicyclist injury outcomes. In particular, we found that SUV crashes inflicted more severe head injuries compared with car crashes and that SUVs were disproportionately likely to throw bicyclists to the ground and run them over.

Empirical examination of interdependent relationship between usage of seatbelt restraint system and driver-injury severity of single-vehicle crashes in Thailand using a joint econometric analysis

Objectives The paper aims to examine the interdependent relationship between the usage of the seatbelt restraint system and severities of the driver-injury in single-vehicle crashes. Methods This paper developed a comprehensive joint econometric structure − a joint random parameters binary probit-binary probit model − that allows for the simultaneous examination of injury severity of the driver in a crash, and taking into account the fact that seat belt use can be endogenous to the outcomes of driver injury. The developed model is tested using data on drivers-injury severities involved in single-vehicle crashes in Thailand from 2012-2017. Results In terms of the interdependent relationship between seatbelt use status and driver-injury severities, the findings suggest that drivers who do not use seat belts may demonstrate more dangerous or aggressive driving behaviors (such as speeding), subsequently increasing their likelihood of involvement in severe or fatal crashes. Additionally, the result also shows that drivers who are involved in speeding-related crashes are less likely to wear a seatbelt and have a higher risk of sustaining severe and fatal injuries. The findings also reveal that in crashes, drivers who are young, or operating trucks are less likely to be wearing their seat belts. The study also indicates that severe and fatal crashes are associated with factors such as elderly drivers, alcohol involvement, unbelted drivers, fatigue, depressed medians, and barrier medians. Conversely, a crash in a U-turn area, driving a passenger car, pickup truck, or large truck, or colliding with a guardrail reduces the likelihood of severe and fatal injuries. Conclusions Neglecting the hidden endogenous effect in statistical analyses could result in an overestimation of the impact of seat belt usage on crash-injury outcomes. The findings of this study can provide valuable insights for relevant authorities aiming to improve driver safety.

Association between short-term air pollution exposure and traumatic intracranial hemorrhage: pilot evidence from Taiwan.

The detrimental effects of air pollution on the brain are well established. However, few studies have examined the effect of air pollution on traumatic brain injury (TBI). This pilot study evaluated the association between short-term air pollution exposure and traumatic intracranial hemorrhage (TIH). Hospital data of patients with TBI following road traffic accidents were retrospectively collected from the electronic medical records at five trauma centers in Taiwan between 1 January and 31 December 2017. TIH was employed as an outcome measure. All road accident locations were geocoded, and air quality data were collected from the nearest monitoring stations. Air pollutants were entered into five multivariable models. A sensitivity analysis was performed on patients who are vulnerable to suffering TBI after road accidents, including motorcyclists, bicyclists, and pedestrians. Among 730 patients with TBI, 327 had TIH. The ages of ≥65 [odds ratio (OR), 3.24; 95% confidence interval (CI), 1.85-5.70], 45-64 (OR, 2.61; 95% CI, 1.64-4.15), and 25-44 (OR, 1.79; 95% CI, 1.13-2.84) years were identified as significant risk factors in the multivariable analysis. In the best-fit multivariable model, exposure to higher concentrations of particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5) was associated with an elevated TIH risk (OR, 1.50; 95% CI, 1.17-1.94). The concentration of nitrogen oxides (NOX) did not increase the risk of TIH (OR, 0.45; 95% CI, 0.32-0.61). After categorizing the air pollution concentration according to quartile, the trend tests in the multivariate model showed that the concentrations of PM2.5 and NOX were significant (p = 0.017 and p < 0.001, respectively). There was a negative borderline significant association between temperature and TIH risk (OR, 0.75; 95% CI, 0.56-1.00, p = 0.05). Notably, the single-vehicle crash was a significant risk factor (OR, 2.11; 95% CI, 1.30-3.42) for TIH. High PM2.5 concentrations and low temperatures are risk factors for TIH in patients with TBI. High NOX concentrations are associated with a lower TIH risk.

Differences in single-vehicle motorcycle crashes caused by distraction and overspeed behaviors: considering temporal shifts and unobserved heterogeneity in prediction

Distraction and overspeed behaviors are acknowledged as two significant contributors to single-vehicle motorcycle crashes, injuries and fatalities resulting from which are severe and critical issues in Pakistan. To explore the temporal instability and differences in the factors determining the injury severities between single-vehicle motorcycle crashes caused by distraction and overspeed behaviors, this study estimated two groups of random parameter logit models with heterogeneity in means and variances. Single-vehicle motorcycle crash data in Rawalpindi city between 2017 and 2019 was used for model estimation, and a wide variety of explanatory variables relating to the rider, roadways, environments, and temporal attributes was simulated in the models. The current study considered three possible crash injury severity outcomes: minor injury, severe injury and fatal injury. Likelihood ratio tests were conducted to explore the temporal instability and non-transferability. Marginal effects were also calculated to further reveal temporal instability of the variables. Except for several variables, the most significant factors reported temporal instability and non-transferability, manifested as the effects varied from year to year and across different crashes. Moreover, out-of-sample prediction was also implemented to capture temporal instability and non-transferability between distraction and overspeed crash observations. The non-transferability between motorcycle crashes caused by distraction and overspeed behaviors provides insights into developing differentiated countermeasures and policies targeted at preventing and mitigating single-vehicle motorcycle crashes caused by the two risk-taking behaviors.

Factors Associated with Work Zone Crashes

Nationally, the annual number of fatal crashes in work zones over the past decade (2010–2019) increased from 521 to 762, which led to rising fatalities from 546 in 2010 to 842 in 2019. Traffic safety in work zones with the aim of reducing fatal and serious injury crashes has been under investigation by researchers and related agencies for decades. The main objective of this study was to investigate the factors associated with work zone crashes to recommend policies targeted at addressing them. Data collected by the Kansas Department of Transportation on crashes that occurred between 2016 and 2020 were used for this research. For the statistical model, binary logistic regression was used in which the dichotomous response variable was defined as either crashes in work zones or crashes outside work zones. According to the model’s results, several significant factors were associated with work zone crashes. Those that had a reasonable expectation of improvement through state policies included the following: alcohol involvement; driver distraction; large commercial vehicle involvement; pedestrian involvement; single-vehicle crashes; speed limits, and speeding. Although these may already be areas targeted in a state’s strategic highway safety plan, special consideration of the overrepresentation of these factors in work zones can provide additional policy options for consideration. Existing countermeasures can be explored, or new countermeasures can be developed based on the needs of the existing conditions.

Impact of event encoding and dissimilarity measures on traffic crash characterization based on sequence of events

Crash sequence analysis has been shown in prior studies to be useful for characterizing crashes and identifying safety countermeasures. Sequence analysis is highly domain-specific, but its various techniques have not been evaluated for adaptation to crash sequences. This paper evaluates the impact of encoding and dissimilarity measures on crash sequence analysis and clustering. Sequence data of interstate highway, single-vehicle crashes in the United States, from 2016 to 2018, were studied. Two encoding schemes and five optimal matching based dissimilarity measures were compared by evaluating the sequence clustering results. The five dissimilarity measures were categorized into two groups based on correlations between dissimilarity matrices. The optimal dissimilarity measure and encoding scheme were identified based on the agreements with a benchmark crash categorization. The transition-rate-based, localized optimal matching dissimilarity and consolidated encoding scheme had the highest agreement with the benchmark. Evaluation results indicate that the selection of dissimilarity measure and encoding scheme determines the results of sequence clustering and crash characterization. A dissimilarity measure that considers the relationships between events and domain context tends to perform well in crash sequence clustering. An encoding scheme that consolidates similar events naturally takes domain context into consideration.

Temporal instability and age differences of determinants affecting injury severities in nighttime crashes

Driving at nighttime may make drivers more likely to be involved in fatal crashes. To investigate the temporal instability and age differences of contributors determining different injury severity levels in nighttime crashes, this paper estimates three groups of random parameters logit models with heterogeneity in the means and variances (young/middle-age/old groups). Nighttime single-vehicle crashes in this study are gathered over four years in California, from January 1, 2014, to December 31, 2017, provided by Highway Safety Information System, including single-vehicle crashes occurring under dark, dawn, and dusk lighting conditions. Simultaneously, to investigate the temporal instability and transferability of nighttime crash severity relating to drivers of different ages, three disaggregate groups are defined: young drivers (15–29 years old), middle-age drivers (30–49 years old), old drivers (over 49 years old). Three injury-severity categories are determined as outcome variables: severe injury, minor injury, and no injury, while multiple factors are investigated as explanatory variables, including driver characteristics, vehicle characteristics, roadway characteristics, environmental characteristics, crash characteristics, and temporal characteristics. Two series of likelihood ratio tests are undertaken to unveil the contributors determining nighttime crash injury severities varying among drivers of different ages over time. Besides, the current study also compares the differences between out-of-sample and within-sample predictions. The results indicate the unstable direction of predictions across different age groups over time and underscore the necessity to adequately accommodate the temporal instability and age differences in accident prediction. More studies can be conducted to accommodate the self-selectivity issue and the out-of-sample prediction differences between using the parametric models and non-parametric models.

Modeling injury severities of single and multi-vehicle freeway crashes considering spatiotemporal instability and unobserved heterogeneity

ABSTRACT Single and multi-vehicle (SMV) crashes remain a significant issue, causing serious safety and economic concerns, and therefore deserve more attention. Using crash data in the Beijing-Shanghai and Changchun-Shenzhen freeways over the five years (2015–2019), this paper explored the transferability and heterogeneity for crash type (single-vehicle versus multi-vehicle crashes) and spatiotemporal stability of determinants affecting the injury severity. The random parameters logit approach with heterogeneity in means and variances was used to model three possible crash injury severity outcomes (measured by the most severely injured individual in the crash) of no injury, minor injury, and severe injury and identify the determinants in terms of driver, vehicle, roadway, environment, temporal, spatial, traffic, and crash characteristics. Remarkable differences were observed in the SMV crashes, and the contributing factors also reported considerable temporal and (or) spatial instabilities. The insights of this study should be valuable to help freeway designers and decision-makers understand the contributing mechanism of the factors and develop the proper management strategies and enforcement countermeasures.

Self-harm in adolescence and risk of crash: a 13-year cohort study of novice drivers in New South Wales, Australia

IntroductionSelf-harm and suicide are leading causes of morbidity and death for young people, worldwide. Previous research has identified self-harm is a risk factor for vehicle crashes, however, there is a...

Modeling spatiotemporal interactions in single-vehicle crash severity by road types

Introduction: Spatiotemporal correlations have been widely recognized in single-vehicle (SV) crash severity analysis. However, the interactions between them are rarely explored. The current research proposed a spatiotemporal interaction logit (STI-logit) model to regression SV crash severity using observations in Shandong, China. Method: Two representative regression patterns-mixture component and Gaussian conditional autoregression (CAR)-were employed separately to characterize the spatiotemporal interactions. Two existing statistical techniques-spatiotemporal logit and random parameters logit-were also calibrated and compared with the proposed approach with the aim of highlighting the best one. In addition, three road types-arterial road, secondary road, and branch road-were modeled separately to clarify the variable influence of contributors on crash severity. Results: The calibration results indicate that the STI-logit model outperforms other crash models, highlighting that comprehensively accommodating spatiotemporal correlations and their interactions is a recommended crash modeling approach. Additionally, the STI-logit using mixture component fits crash observations better than that using Gaussian CAR and this finding remains stable across road types, suggesting that simultaneously accommodating stable and unstable spatiotemporal risk patterns can further strengthen model fit. According to the significance of risk factors, there is a significant positive correlation between distracted diving, drunk driving, motorcycle, dark (without street lighting), and collision with fixed object and serious SV crashes. Truck and collision with pedestrian significantly mitigate the likelihood of serious SV crashes. Interestingly, the coefficient of roadside hard barrier is significant and positive in branch road model, but it is not significant in arterial road model and secondary road model. Practical Applications: These findings provide a superior modeling framework and various significant contributors, which are beneficial for mitigating the risk of serious crashes.

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.

Modelling injury severity in single-vehicle crashes using full Bayesian random parameters multinomial approach

Single-vehicle (SV) crash severity model considering spatiotemporal correlations has been extensively investigated, but spatiotemporal interactions have not received sufficient attention. This research is dedicated to propose a superior spatiotemporal interaction correlated random parameters logit approach with heterogeneity in means and variances (STICRP-logit-HMV) for systematically characterizing unobserved heterogeneity, spatiotemporal correlations, and spatiotemporal interactions. Four flexible interaction formulations are developed to uncover the spatiotemporal interactions, including linear structure, Kronecker product, mixture-2 model, and mixture-5 model. Four candidate approaches-random parameters logit (RP-logit), RP-logit with heterogeneity in means and variances (RP-logit-HMV), correlated RP-logit-HMV (CRP-logit-HMV), and spatiotemporal CRP-logit-HMV (STCRP-logit-HMV)-are also established and compared with the proposed model. SV crash observations in Shandong Province, China, are employed to calibrate regression parameters. The model comparison results show that (1) the performance of the RP-logit-HMV model outperforms the RP-logit model, implying that capturing heterogeneity in the means and variances can strengthen model fit; (2) the CRP-logit-HMV model and the RP-logit-HMV model are comparable; (3) the STCRP-logit-HMV model outperforms the CRP-logit-HMV model, implying that addressing the spatiotemporal crash mechanisms is beneficial to the overall fitting of the crash model; (4) the STICRP-logit-HMV model performs better than the STCRP-logit-HMV model and this finding remains stable across different interaction formulations, indicating that comprehensively reflecting the spatiotemporal correlations and their interactions is a promising approach to model SV crashes. Among the four interaction models, the STICRP-logit-HMV model with mixture-5 component maintains the best fit, which is a recommended approach to model crash severity. The regression coefficients for young driver, male driver, and non-dry road surface are random across observations, suggesting that the influence of these factors on SV crash severity maintains significant heterogeneity effects. The research results provide transportation professionals with a superior statistical framework for diagnosing crash severity, which is beneficial for improving traffic safety.