Single-vehicle Crashes Research Articles

Unveiling the risks of speeding behavior by investigating the dynamics of driver injury severity through advanced analytics

Single-vehicle crashes, particularly those caused by speeding, result in a disproportionately high number of fatalities and serious injuries compared to other types of crashes involving passenger vehicles. This study aims to identify factors that contribute to driver injury severity in single-vehicle crashes using machine learning models and advanced econometric models, namely mixed logit with heterogeneity in means and variances. National Crash data from the Crash Report Sampling System (CRSS) managed by the National Highway Traffic Safety Administration (NHTSA) between 2016 and 2018 were utilized for this study. XGBoost and Random Forest models were employed to identify the most influential variables using SHAP (Shapley Additive Explanations), while a mixed logit model was utilized to model driver injury severity accounting for unobserved heterogeneity in the data collection process. The results revealed a complex interplay of various factors that contribute to driver injury severity in single-vehicle crashes. These factors included driver characteristics such as demographics (male and female drivers, age below 26 years and between 35 and 45 years), driver actions (reckless driving, driving under the influence), restraint usage (lap-shoulder belt usage and unbelted), roadway and traffic characteristics (non-interstate highways, undivided and divided roadways with positive barriers, curved roadways), environmental conditions (clear and daylight conditions), vehicle characteristics (motorcycles, displacement volumes up to 2500 cc and 5,000–10,000 cc, newer vehicles, Chevy and Ford vehicles), crash characteristics (rollover, run-off-road incidents, collisions with trees), temporal characteristics (midnight to 6 AM, 10 AM to 4 PM, 4th quarter of the analysis period: October to December, and the analysis year of 2017). The findings emphasize the significance of driving behavior and roadway design to speeding behavior. These aspects should be given high priority for driver training as well as the design and maintenance of roadways by relevant agencies.

Developing Risk-Informed Speed Limits Against Single-Vehicle Crashes by Exploiting an Augmented Reliability Problem With Multi-Fidelity Enhancement

Developing Risk-Informed Speed Limits Against Single-Vehicle Crashes by Exploiting an Augmented Reliability Problem With Multi-Fidelity Enhancement

Rethinking cycling safety: The role of gender in cyclist crash injury severity outcomes

Given the ongoing climate crisis and the need for environmentally friendly communities, there has been an increasing interest in sustainable mobility solutions such as cycling. This study seeks to incorporate an equitable component to studying cycling safety and uses one full year’s data of 4,457 single bicycle-single motor vehicle crashes that took place in 2022 in the state of Florida to estimate a series of random parameters multinomial logit models with heterogeneity in the means and variances to capture gender differences in outcome severities. A comparison of advanced statistical models such as unconstrained and partially constrained approaches, that were previously employed in the literature to test for temporal stability, is undertaken in a new application. A partially constrained model is estimated to best identify gender specific factors and argue the need to evaluate and promote safety of female and male cyclists separately. The study finds substantial differences between how the contributing factors and crash circumstances impact the crash injury severity of women and men cyclists. It evaluates factors such as age, location, cyclist behavior, weather, and road design as well as performs out-of-sample simulation to gain additional insights. The findings of this research emphasize the need for targeted approaches in designing our cities and policy making that account for the collective differences in behavior and experience of women and men cyclists.

Developing Safety Performance Functions for Severe Distraction-Related Crashes along Kentucky’s Rural and Urban Two-Lane Roadways

This study develops safety performance functions (SPFs) for severe (“KA” or “fatal and suspected serious injury”) distraction-related crashes along Kentucky’s rural and urban two-lane undivided roadway segments using recent four-year (2018–2021) crash records. Additional efforts were made to meticulously scrutinize crash narratives categorized as non-distracted and correct those cases. To account for crash under-dispersion, the Conway–Maxwell–Poisson, heterogeneous Conway–Maxwell–Poisson, zero-inflated Conway–Maxwell–Poisson, and zero-inflated heterogeneous Conway–Maxwell–Poisson (ZI-HTCMP) models were fitted and compared. The ZI-HTCMP model outperformed the other models with respect to several goodness-of-fit measures (e.g., mean absolute deviance and mean square prediction error). From the developed SPFs for rural and urban two-lane roads, wider lanes and higher speed limits (55 mph) were associated with increased severe distraction-related crash frequencies. Furthermore, some variables were found to be significant in rural areas, but insignificant in urban areas, and vice versa. For example, major collector roads, minor collector/local roads, the presence of roadside guardrails, wider right-hand shoulders, the presence of horizontal curves, and the presence of vertical grades were associated with increased crash frequencies along rural two-lane roads. In addition, the proportion of heavy vehicles (>5%) and the existence of paved shoulders were associated with increased crashes along urban two-lane roads. The empirical Bayes method was then used to rank the top 10 distraction-related high crash locations (HCLs) for both rural and urban two-lane segments. In-depth investigation of HCLs highlighted single-vehicle distraction-related crashes as the most common collision type. Countermeasures were proposed to help reduce severe distraction-related crashes, for example, installing chevron signs along rural two-lane roads.

Spatiotemporal instability analysis in single-vehicle crash severity on different roadway classifications

ABSTRACT The severity of crashes on national/provincial roads, urban roads, and rural roads was analyzed. Random parameters logit models with heterogeneity in means and variances under different time periods of the day and roadway classifications were employed. Analysis of factors influencing injury severity of single-vehicle crashes in the driver, vehicle, road, and environment to explore correlation and difference between influencing factors and crash severity. The results showed that the effect of factors that determine injury severity varied significantly across different roadway classifications. Specifically, driver age (<30) and summer were only significantly associated with single-vehicle crashes on urban roads. Dawn/dusk was only significant in single-vehicle crashes on rural roads. Meanwhile, overall spatiotemporal instability was observed in the study. While several indicators were also reported to show relative spatial or temporal stability such as male, driver age (>55), motorcycle, traffic control, and visibility (<100 m).

Temporal instability of factors affecting injury severity in single-vehicle crashes on rural highways

ABSTRACT To study the time-of-day variations and temporal stabilities of factors influencing single-vehicle crashes on rural highways, random parameters logit models with heterogeneity in means and variances under different time periods of the day and from year to year were estimated to identify significant factors. The potential crash-influencing factors in drivers, vehicles, roads, and the environment were analyzed to dissect the correlation and variability between the influencing factors and crash injury severity. Likelihood ratio tests were conducted to assess the transferability of model estimation results from different times of the day and from year to year. The results showed that the effect of factors that determine injury severity varied significantly across time-of-day/time-period combinations. Overall temporal instability was observed in the study. However, several explanatory variables showed temporally stable effects in terms of their impact on resulting injury severities. Such as male, driver age (<30), truck, non-dry road surface, and visibility (100–200 m).

Leveraging Machine Learning Algorithms to Predict and Analyze Single-Vehicle and Multi-Vehicle Crash Occurrences on Motorways

Leveraging Machine Learning Algorithms to Predict and Analyze Single-Vehicle and Multi-Vehicle Crash Occurrences on Motorways

Impact of COVID-19 on road crashes in Thailand

The main goal of this study is to investigate the impact of COVID-19 on road crashes in Thailand using time series and interrupted time series analysis. To achieve the goal, road crash data from the Department of Highway (DOH), which includes total crashes, single vehicle crashes, fatalities, fatal crashes, speeding crashes, and drunk driving crashes, was obtained to conduct Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models and Interrupted Time Series (ITS) models. SARIMA models were applied to forecast the number of crashes in the absence of COVID-19 then compare them to the observed values to identify the difference. The impact of a policy change aimed at addressing the spread of COVID-19 was assessed using ITS models on a time series accident dataset. The goal was to ascertain if the intervention had a meaningful and causative impact on the outcome. The result showed that the first wave of COVID-19 caused a significant reduction in all road crash indicators instead of skyrocketing to a peak. After releasing the lockdown measures from the first wave of spreading, an increase was found in all of the crash indicators as well. However, the third wave of COVID-19, which lasted longest for nearly 7 months, also caused a decrease in the number of crashes, but not as much as the first wave of the outbreak. Moreover, the result from the interrupted time series also revealed that curfews and the closure of entertainment places are associated with a significant decrease in the number of speeding crashes and drunk driving crashes from 10 p.m. to 4 a.m., respectively. It can be observed that the COVID-19 countermeasures, such as curfews and bans on the sales of alcoholic beverages, led to a drop in the number of speeding and drunk driving crashes.

Investigating the contributing factors of crashes on interstate bridges in Louisiana using latent class clustering and association rule mining

Drivers on long bridges on interstates often face unique challenges, such as restricted lane width, inadequate shoulders, and a lack of clear zones for safe recoveries. Studies on understanding the factors that contribute to crash severity on such high-risk sections of interstates are limited. This research study applies latent class clustering (LCC) to detect homogeneous clusters while accounting for unobserved heterogeneity in a dataset of 10,036 crashes that occurred over a 6-year period (2015–2020) on eight selected bridges. Utilizing the LCC method, the research identifies four optimal clusters in bridge crashes, characterized by attributes such as ‘4-lane,’ ‘6-lane,’ ‘single-vehicle crashes,’ and ‘unknown driver.’ The Association Rule Mining (ARM) approach is used to identify the important collective factors to visible injury (KAB – fatal, severe and moderate) and property damage only (PDO or no injury). In Cluster 1 (4-lane), KAB and PDO crashes differ in collision type and visibility conditions, with rear-end crashes linked to KAB and sideswipe crashes to PDO. Cluster 2 (6-lane) shows similar distinctions but lacks specific lighting associations for PDO. In Cluster 3 (single-vehicle crashes), KAB involves moderate traffic and low visibility, while PDO has lower speed limits and non-dry surfaces. Cluster 4 (unknown driver), despite overrepresenting hit-and-run cases, underscores challenges in injury crash data collection in high-volume mobility scenarios. The discussions of the findings on the severity factors in this study are expected to help traffic safety engineers, policymakers, and planners to identify effective safety countermeasures on major elevated sections.

Pedestrian injury severities resulting from vehicle/pedestrian intersection crashes: An assessment of COVID-contributing temporal shifts

Pedestrian mobility has become an increasingly important concern in transportation system analysis because of its positive impacts on the environment and healthy lifestyles. However, pedestrian safety in a vehicle-dominated transportation network remains a concern and potential barrier to pedestrian mobility, with pedestrian intersection safety being of particular concern. In addition, it is important to understand how pedestrian safety has been affected by the COVID-19 pandemic, perhaps permanently shifting pedestrian injury risks. This research seeks to provide insight into how pedestrian injury risks at intersections have changed as a result of the pandemic by estimating a series pedestrian injury severity models. To do so, unconstrained and partially constrained random parameters multinomial logit models with heterogeneity in the means of random parameters were estimated. Using Florida data, two one-year periods (one year before and one year after the COVID-19 pandemic) were defined based on vehicle miles traveled. The sample includes 3,780 single pedestrian-single vehicle crashes (2,348 from daytime and 1,432 from nighttime). A broad range of variables was considered to assess how the parameters may have shifted between the before and after periods. A series of likelihood ratio tests were conducted to examine the stability of model parameter estimates across the predefined time periods as well as to determine the differences between the daytime and nighttime crash injury severity outcomes. The results show that the nighttime crashes experienced more temporal shifts relative to daytime crashes. The findings also showed that both pedestrian and driver behavior played key temporally-shifting roles before and after the COVID-19 pandemic period. Finally, the out-of-sample simulations suggest that pedestrian injuries have become more severe after the pandemic.

An investigation of heterogeneous impact, temporal stability, and aggregate shift in factors affecting the driver injury severity in single-vehicle rollover crashes

Single-vehicle rollover crashes have been acknowledged as a predominant highway crash type resulting in serious casualties. To investigate the heterogeneous impact of factors determining different injury severity levels in single-vehicle rollover crashes, the random parameters logit model with unobserved heterogeneity in means and variances was employed in this paper. A five-year dataset on single-vehicle rollover crashes, gathered in California from January 1, 2013, to December 31, 2017, was utilized. Driver injury severities that were determined to be outcome variables include no injury, minor injury, and severe injury. Characteristics pertaining to the crash, driver, temporal, vehicle, roadway, and environment were acknowledged as potential determinants. The results showed that the gender indicator specified to minor injury was consistently identified as a significant random parameter in four years’ models and the joint five-year model, excluding the 2016 crash model where the night indicator associated with no injury was observed to produce the random effect. Additionally, two series of likelihood ratio tests were conducted to assess the year-to-year and aggregate-to-component temporal stability of model estimation results. Marginal effects of explanatory variables were also calculated and compared to analyze the temporal stability and interpret the results. The findings revealed an overall temporal instability of model specifications across individual years, while there is no significant aggregate-to-component variation. Injury severities were observed to be stably affected by several variables, including improper turn indicator, under the influence of alcohol indicator, old driver indicator, seatbelt indicator, insurance indicator, and airbag indicator. Furthermore, the year-to-year and aggregate-to-component shift was quantified and characterized by calculating the differences in probabilities between within-sample observations and out-of-sample predictions. The overall results imply that continuing to expand and refine the model to incorporate more comprehensive datasets can result in more robust and stable injury severity prediction, thus benefiting in mitigating the associated driver injury severity.

Assessing distracted driving crash severities at New York City urban roads: A temporal analysis using random parameters logit model

Distracted driving poses one of the most significant risks to road safety. The current study aims to provide a deeper understanding of the factors affecting the severity of distracted driving crashes in New York City and to explore the temporal stability in the effects of different variables on crash outcomes in 2016 to 2019 period by adopting a post-crash perspective. The police-reported data of single-vehicle distraction-related crashes of private cars on urban roads of New York City was used for this study. Three injury categories were considered: no injury, minor injury, and severe injury. To investigate crash severities and identify unobserved heterogeneities, a random parameters logit model was conducted. The results revealed that a wide variety of variables including driver traits, vehicle and temporal characteristics, and crash attributes were found to be significant in explaining distracted-related crash severities. Furthermore, a series of likelihood ratio tests were conducted to identify the temporal shifts of estimated variables during the period. The results of the temporal analysis showed that the estimated variables of the random parameters model were unstable during the 4-year period, which may be the result of shifting trends such as the development of in-vehicle technologies, and new sources of distraction. However, the complex nature of distracted-related crashes and changes in driver behavior should be considered for further interpretation. This research provides a set of policy implications for planners and policymakers, aiming at facing factors contributing to a higher level of injury severity in distracted driving crashes. This includes providing targeted information on distracted driving to high-risk groups, such as younger drivers, and also combining education, awareness programs, higher penalties, and intense patrolling. Engineering measures such as enhanced roadside illumination and audible edge lines can be effective, especially in reducing late-night distracted driving crashes.

Analysis of E-Scooter Crashes in the City of Bari

The remarkable impact that e-scooters have had on the transportation system drives research on this phenomenon. The widespread use of e-scooters also poses several new safety issues, which should be necessarily studied. The aim of this paper points in this direction, investigating the main contributing factors, causes, and patterns of recorded e-scooter crashes, considering also different crash types and severity, using the City of Bari (Italy) as a case study. The crash dataset based on police reports and referring to the period July 2020–November 2022 (i.e., the first period of e-scooter implementation in the City of Bari) was investigated. Crashes were clustered according to several variables. No fatal crashes occurred, even though crashes mostly resulted in injuries (70%). Considering road type, divided roads were found to be less safe than undivided ones, due to higher mean speeds than on other roads and to a less constrained e-scooter driving behavior. Calm (off-peak) daytime hours seem to lead to more frequent e-scooter crashes with respect to both peak and nighttime hours, even if the latter hours are associated with an increased severity. Once controlled for exposure, season, lighting conditions, and the private/sharing ratio do not seem influential. E-scooters are more prone to be involved in single-vehicle and pedestrian crashes at segments than other vehicles, but they show similar crash trends than other vehicles (i.e., angle crashes) at intersections. As emerged from traffic surveys, not all e-scooter users were found to use cycle paths. Combining this information with crash data, it seems that not using cycle paths is considerably less safe than using them. Besides engineering measures and policies, awareness campaigns should be promoted to elicit safe users’ behavior and to tackle the several violations and misbehaviors emerging from the crash data.

Assessing the effects of geometry and non-geometry related factors in work-zone crashes

Objective Work zones are unique in geometry and traffic management, utilizing special traffic signs, standard channelizing devices, appropriate barriers, and pavement markings. These configurations can introduce unexpected driving conditions, potentially posing risks to drivers. This analysis aims to explore potential differences in contributing factors between work-zone crashes where geometry was identified as a factor and those where it was non-geometry factor. To gain insights into driver injury severities in single-vehicle work-zone crashes, this study analyzed work zone crash data from Florida. Method This study employed random parameters logit models, accommodating potential variations in parameter estimates’ means and variances. The dataset encompassed a wide array of factors known to influence driver injury severity, encompassing crash characteristics, vehicle attributes, roadway features, prevailing traffic volume, driver profiles, and spatial and temporal considerations. Results This analysis yielded significantly distinct parameters for work-zone crashes, distinguishing between geometry-related and non-geometry-related factors (primarily the human factors). This distinction suggests a complex interplay between these factors. Notably, the marginal effects of individual parameter estimates exhibited marked differences between these two categories – geometry and non-geometry factors. Conclusion These findings contribute to the growing body of research indicating that geometric restrictions within work zones introduce a distinct set of risk factors compared to non-geometry-related factors. Recognizing the significance of geometric restrictions, beyond typical driving conditions, holds the implications for enhancing safety within various work zone configurations and offers valuable insights for crash scene investigators to pinpoint contributing factors accurately.

The value of Association Rule Analysis in understanding serious and fatal road traffic crashes - a case study of the N4 toll road between 2015 and 2019

In spite of the fact that the road traffic crash fatality rate in South Africa is significantly higher than the global average, the characteristics and causes of road traffic crashes are still not well understood. Without a clear understanding of the characteristics and causes, intelligence-led countermeasures to reduce crashes cannot be developed or applied. The analysis of local South African road crash data is of particular importance in identifying the underlying problems responsible for the high crash rates across the country. While descriptive analyses can be used to present a snapshot of the crash problem, they are largely inadequate when it comes to throwing light on the underlying patterns and relationships between the contributing factors and causes of the crashes themselves. This paper presents a study of the use of Association Rule Analysis (ARA) on data related to crashes on the N4 between Pretoria and the Mozambican border, between 2015 and 2019, to see what added value ARA may offer to standard descriptive analyses of crash data. Apriori Association Rule algorithm was employed on the dataset to identify what contributory factors were not initially evident in the occurrence of serious and fatal crashes. ARA revealed that pedestrians, negligent driving, overtaking and single-vehicle crashes are common factors likely to be present in a serious crash. However, this study found that descriptive statistical analysis and ARA methods are not mutually exclusive, but rather complementary. Where descriptive statistical analysis focuses on summarising and describing data, ARA focuses on identifying relationships and patterns within the data.

Severity Analysis of Secondary Crashes on High-Speed Roadways: Pattern Recognition Using Association Rule Mining

Secondary crashes (SCs) are a major concern, posing additional safety threats to both non-involved vehicles and incident responders. The objective of this study was to identify the affiliated factors contributing to SCs on roadways with a speed limit of 55 mph or above. Traditional police-investigated crash dataset was analyzed, spanning more than four years (January 2016–February 2020) for the entire state of Alabama. As the crash database did not directly include information on SCs and did not allow for linking a primary crash with a subsequent SC, a data extraction process was developed to identify SCs and understand their characteristics. Association rule mining (ARM) was applied to identify crash patterns based on maximum injury severity levels. The generated rules were filtered based on support, confidence, and lift, and then validated by the lift increase criterion. The results revealed complex relationships between risk factors and severity of SCs. In relation to SCs with injuries, single-vehicle crashes were frequently observed during peak hours and when drivers swerved to avoid objects/persons/vehicles. In contrast, concerning SCs with possible/no injuries, single-vehicle collisions were more likely to occur when drivers failed to notice objects/persons/vehicles and were involved in speeding. On urban interstates, single-vehicle SCs were frequently associated with injuries, while rear-end SCs were often linked to possible/no injuries. The findings of this study can be helpful in enhancing existing traffic incident management programs to mitigate the occurrence of SCs.

Modeling of single-vehicle and multi-vehicle truck-involved crashes injury severities: A comparative and temporal analysis in a developing country

Truck-involved crashes persist as a significant concern, yielding noteworthy human casualties and causing economic ramifications, particularly in developing countries. This paper aims to undertake a comprehensive analysis of the associated factors influencing injury severity in truck-involved crashes, with a particular emphasis on discerning variations between single-vehicle and multi-vehicle incidents, as well as accounting for heterogeneity and temporal stability. The data analysis involves a meticulous examination of crash data spanning the entirety of Thailand from 2017 to 2020. Employing three distinct levels of injury severities, namely PDO injury, moderate injury, and severe injury, the study employs a series of mixed logit models that account for unobserved heterogeneity in both means and variances. Results revealed significant instability in injury risk determinants over time among both single and multi-vehicle events. Aligning predictive assessments further spotlighted fluctuations in projected burdens across models and years – collectively underscoring the imperative to integrate temporal considerations into modeling and prevention. Several crash-type distinctions and priorities emerged. For single-truck events, key risks included roadway alignments and geometry, speeding, fatigue, and lighting conditions. However multi-truck collisions concentrated around exposure factors like highway traits, sightline limitations, and vulnerable road users. Ultimately, the technique permitted responsive countermeasure targeting and recalibration opportunities keyed to each crash form’s evolving landscapes. While it is indeed noteworthy that several variables have exhibited instability in their effects, it is equally important to acknowledge the existence of certain variables that maintain a relative degree of temporal stability. This underscores their pivotal role in shaping the foundation of enduring strategies aimed at enhancing traffic safety in the long run. The multifaceted investigation constitutes an invaluable reference for diverse transportation stakeholders seeking to curb rising truck fatalities through evidence-based improvements in policy, engineering, usage protocols, and technologies. It provides a blueprint for nimble safety planning within complex modernizing road systems.

Unraveling the differences in distracted driving injury severities in passenger car, sport utility vehicle, pickup truck, and minivan crashes

Distracted driving poses a significant risk on the roadway users, with the level of distraction and crash outcomes varying depending on the type of vehicle. Drivers of passenger cars, sport utility vehicles (SUVs), pickup trucks, minivans experience distinct levels of distraction, leading to potential crashes. This study investigates into the severity of driver injuries resulting from distracted driving in these vehicle categories, shedding light on the variations in single-vehicle crashes. Focusing on single-vehicle crashes in Florida during 2019, involving passenger cars, SUVs, pickup trucks, and minivans caused by distracted driving, the study examines various distractions such as, electronic communication devices (cell phones), electronic devices (navigation systems, music players), internal and external disturbances, texting, and inattentive driving. To analyze the severity of injuries resulting from distracted driving in passenger cars, SUVs, pickup trucks, and minivans, the study employs random parameter multinomial logit models with heterogeneity in means and variances. The model estimates highlight thirty-five significant factors influencing the severity of driver injuries resulting from distracted driving. Notably, the impact of these factors varies significantly depending on the vehicle type (i.e., passenger cars, SUVs, pickup trucks, and minivans). While many explanatory variables are specific to each vehicle type, only one factor (restraint belt usage) is common across all vehicle types, with varying magnitudes in injury outcomes. The likelihood ratio tests indicate that injury severity must be analyzed and modeled separately for passenger cars, SUVs, pickup trucks, and minivans. Vehicle characteristics play a crucial role in driver distraction and crash outcomes. Analyzing a year of crash data, categorized by four vehicle types, has provided valuable insights into distracted driving patterns in passenger cars, SUVs, pickup trucks, and minivans, influencing potential prevention strategies. To combat against distracted driving effectively, priority should be given to driver education and training, roadway design, vehicle technology, enforcement, and automobile insurance. The automobile industry, especially for passenger cars, SUVs, pickup trucks, and minivans, should consider implementing advanced in-vehicle technologies tailored to the specific characteristics of each vehicle type (e.g., advanced driver assistance systems (ADAS)) to proactively prevent driver distraction. These proactive measures will contribute significantly to enhancing road safety and reducing the risks associated with distracted driving.

A Poisson Lognormal-Lindley model for simultaneous estimation of multiple crash-types: Application of multivariate and pooled univariate models

Challenges addressing overdispersion, unobserved heterogeneity, the preponderance of zeros, and correlation in the dependent variables of crash count models are of significant interest. Accounting for all these data issues simultaneously is few and far between. This study proposes a new mixing distribution model that accounts for overdispersion and the preponderance of zeros in crash count models. The proposed mixing distribution model extends to the multivariate structure to account for correlations between dependent variables and unobserved heterogeneity. The empirical analysis is conducted on crash data of Bruce highway involving single-vehicle and multi-vehicle crash types by “fatal and severe injury” and “moderate and minor injury” severity levels on aggregated data over three analysis years (2016, 2017, and 2018). The study demonstrates superior goodness of fit of the proposed multivariate random parameters Poisson lognormal-Lindley model compared to its restricted models. Moreover, pooling the crash data as repeated measures of crash types helped formulate a pooled-univariate random parameters Poisson-Lindley model to estimate multiple crash types by severity. The results showed the pooled-univariate model offers comparable goodness of fit and averaged marginal effects as the complex multivariate modeling structure. Moreover, the proposed pooled-univariate model reduced the model complexity to a one-dimensional integral and offered more efficient parameter estimates. In the empirical context, the modeling results showed that single-vehicle and multi-vehicle crashes by severity are linked with different causality.

Efficient and robust estimation of single-vehicle crash severity: A mixed logit model with heterogeneity in means and variances

This study delves into the factors that contribute to the severity of single-vehicle crashes, focusing on enhancing both computational speed and model robustness. Utilizing a mixed logit model with heterogeneity in means and variances, we offer a comprehensive understanding of the complexities surrounding crash severity. The analysis is grounded in a dataset of 39,788 crash records from the UK’s STATS19 database, which includes variables such as road type, speed limits, and lighting conditions. A comparative evaluation of estimation methods, including pseudo-random, Halton, and scrambled and randomized Halton sequences, demonstrates the superior performance of the latter. Specifically, our estimation approach excels in goodness-of-fit, as measured by ρ2, and in minimizing the Akaike Information Criterion (AIC), all while optimizing computational resources like run time and memory usage. This strategic efficiency enables more thorough and credible analyses, rendering our model a robust tool for understanding crash severity. Policymakers and researchers will find this study valuable for crafting data-driven interventions aimed at reducing road crash severity.