Correlated mixed logit modeling with heterogeneity in means for crash severity and surrogate measure with temporal instability

Young novice drivers' road crash injuries and contributing factors: A crash data investigation

OBJECTIVE

Collisions are a significant cause of injury and fatality among young novice drivers. Using real crash data, this study further explores the multifaceted and complex nature of young novice drivers' crash injury risk by synthesizing different driver attributes and crash scenarios in order to update and validate previous research findings and provide more feasible recommendations for preventive measures.

METHODS

Detailed data on traffic crash of young novice drivers were extracted from the National Automobile Accident In-Depth Investigation System (NAIS) in China, and a mixed research methodology using a Random Forest and multinomial logit modeling framework was used in order to explore and study the important influences on traffic crash injuries of young novice drivers in Songjiang District, Shanghai, during the period from 2018 to 2022.

RESULTS

The results of the study showed that human, vehicle, road and environmental characteristics contributed 36.83%, 22.65%, 17.07% and 23.45% respectively to the prediction of crash injury level of novice drivers. Among the various single factors, driver negligence was the most important factor affecting the crash injury level of novice drivers. Age of the vehicle, crash location, road signal condition and time of crash all had a significant effect on the crash injury level of young novice drivers (95% of the confidence level).

CONCLUSIONS

The study comprehensively analyzed young novice driver crash data to reveal the crash injury risk and its severity faced by young novice drivers in different contexts, and suggested targeted safety improvements. There are similarities and differences with the results of previous studies, in which there are new contributions to understanding the driving risks of young novice drivers in daytime and nighttime.

Visual hazardous models: A hybrid approach to investigate road hazardous events

Road users (drivers, passengers, pedestrians, and Animals) are exposed to hazardous events during their commute. With 23 % of global fatalities among pedestrians, their safety continues to be a principal interest for policymakers worldwide. Owing to limited budgets available, there is a growing emphasis on data-driven stochastic models to decide on policies. However, statistical models have limitations due to crash data having redundant features, inherent heterogeneity, and unobserved characteristics. The random parameter model framework addresses the unobserved heterogeneity, but redundant features and inherent heterogeneity among the data's characteristics still compute the biased estimates. This is further complicated if the data has spatiotemporal attributes. To address this, we developed two visual hazardous (VH) models: (i) addresses the unobserved heterogeneity in the data, and (ii) addresses the dimensionality, inherent heterogeneity among the characteristics and unobserved heterogeneity in the collected data after spatiotemporal pattern identification. The feature selection model reduces the dimensionality, whereas latent class clustering classifies the data into maximum heterogeneity between classes. This integration reduces bias in the estimates. As a use-case, pedestrian crosswalk crashes for a decade (2009-2018) in the Indian state of Tamil Nadu extracted from the Road Accident Database Management System (RADMS) was used to understand model performance. This data comprises the crash location, road, vehicle, driver, pedestrian, and environment details. Results show that visual hazardous model 2 allows for generating crash scenarios with five homogeneous sub-classes and the magnitude with marginal effects of contributing factors impacting it. For example, pedestrians during their crosswalks are likely to sustain 82% more chance of fatal/grievous injuries on expressways (posted speed limit: 100 km per hour) in annual hazardous zone locations. Working pedestrian age group (25-64 years), an older pedestrian (>64 years), the pedestrian position on a pedestrian crossing and not in the centre of the road, pedestrian action: walking along the edge of the road, multiple lanes, two lanes, paved shoulder, straight and flat road, motorcycle, bus, truck, medium-duty vehicle, illegal driver (<=17 years), going ahead/ overtaking, high speed, expressways, and rural region were statistically significant (positively) contributing to the fatal/grievous injury pedestrian crashes during their crosswalk. This technique serves as a structure for engineers, researchers, and policymakers to formulate effective countermeasures that enhance road safety.

How do drunk-driving events escalate into drunk-driving crashes? An empirical analysis of Beijing from a spatiotemporal perspective

Drunk-driving events often escalate into drunk-driving crashes, however, the contributing factors of this progression remain elusive. To mitigate the likelihood of crashes stemming from drunk-driving events, this paper introduces the notion of 'the severity of drunk-driving event' and examines the complex relationship between the severity and its contributing factors, considering spatiotemporal heterogeneity. The study utilizes a Geographically and Temporally Weighted Binary Logistic Regression (GTWBLR) model to conduct spatiotemporal analysis based on police-reported drunk-driving events in Beijing, China. The results show that most factors passed the non-stationary test, indicating their effects on the severity of drunk-driving event vary significantly across different spatial and temporal domains. Notably, during non-workday, drunk-driving events in northeast of Beijing are more likely to escalate into crashes. Furthermore, severe weather during winter in the northwest of Beijing is associated with high risk of drunk-driving crashes. Based on these insights, the authorities can strengthen drunk-driving checks in the northeast region of Beijing, particularly during non-workdays. And it is crucial to promptly clear accumulated snow on the roads during severe winter weather to improve road safety. These insights and recommendations are highly valuable for reducing the risk of drunk-driving crashes.

Analyzing the factors influencing speeding behavior based on quasi-induced exposure and random parameter logit model with heterogeneity in means

INTRODUCTION

Speeding behavior is a major threat to road traffic safety, which can increase crash risks and result in severe injury outcomes. Although several studies have been conducted to analyze speeding crashes and relevant influential factors, the heterogeneity of variables has not been fully explored. Based on the traffic crash data extracted from the Crash Report Sampling System, the study aims to identify the factors that influence speeding driving with the consideration of variable heterogeneity.

METHOD

Quasi-induced exposure technique is adopted to identify the disparities in the propensities of speeding for various driving cohorts. The random parameter logit model with heterogeneity in means is employed to examine the factors impacting speeding behavior.

RESULTS

Results indicate that: (a) driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving; (b) the propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads; and (c) the random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means.

CONCLUSIONS

Speeding behavior can be influenced by various factors in terms of driver-vehicle characteristics, physical condition, driving actions, and environmental conditions.

PRACTICAL APPLICATIONS

The findings could serve to develop effective countermeasures to reduce speeding behavior and improve traffic safety.

Impact of real-time weather conditions on crash injury severity in Kentucky using the correlated random parameters logit model with heterogeneity in means

The present study investigated the impact of real-time weather (air temperature, relative humidity, precipitation, wind speed, and solar radiation) on crash injury severity. Recent crash data (January 2016 to April 2021) on Interstate-75 in the state of Kentucky were merged with real-time weather information (retrieved from Kentucky Mesonet stations) at the 1-hour level. The severity index "SI" (i.e., the ratio of percent severe crashes to percent exposure of a specific weather state during the crash period) was introduced to evaluate the impact of different real-time weather states on fatal and severe injury crashes. Furthermore, the standard mixed logit (MXL), correlated mixed logit (CMXL), and correlated mixed logit with heterogeneity in means (CMXLHM) models were fitted and compared to identify the risk factors contributing to crash injury severity while accounting for unobserved heterogeneity. The results showed that the CMXLHM model was statistically superior to the CMXL and MXL models based on various goodness-of-fit measures (e.g., Akaike information criterion "AIC" and McFadden pseudo R-squared). Results from the SI analysis and CMXLHM model showed that real-time weather-related factors (e.g., air temperature ≥ 70 0F and relative humidity ≥ 90 %) were significantly associated with higher severe injury likelihood. Further, driving under the influence (DUI), young drivers, and vehicle travel speed were associated with greater injury severities. On the other hand, presence of horizontal curve, passenger cars, and hourly traffic volume were associated with lower injury severity likelihood. The study outcomes can help in incident management by suggesting specific real-time weather-related states to feed to dynamic message signs (DMS) to enhance travelers' safety along the interstates.

A hybrid approach of random forest and random parameters logit model of injury severity modeling of vulnerable road users involved crashes

Vulnerable road users (VRUs) involved crashes are a major road safety concern due to the high likelihood of fatal and severe injury. The use of data-driven methods and heterogeneity models separately have limitations in crash data analysis. This study develops a hybrid approach of Random Forest based SHAP algorithm (RF-SHAP) and random parameters logit modeling framework to explore significant factors and identify the underlying interaction effects on injury severity of VRUs-involved crashes in Shenyang (China) from 2015 to 2017. The results show that the hybrid approach can uncover more underlying causality, which not only quantifies the impact of individual factors on injury severity, but also finds the interaction effects between the factors with random parameters and fixed parameters. Seven factors are found to have significant effect on crash injury severity. Two factors, including primary roads and rural areas produce random parameters. The interaction effects reveal interesting combination features. For example, even though rural areas and primary roads increase the likelihood of fatal crash occurrence individually, the interaction effect of the two factors decreases the likelihood of being fatal. The findings form the foundation for developing safety countermeasures targeted at specific crash groups for reducing fatalities in future crashes.

Evaluating alternate discrete outcome frameworks for modeling riders' red light running behavior

This paper aims to empirically evaluate the ordered and unordered discrete outcome frameworks to approach riders' red-light running (RLR) decisions and compare the differences in influencing factors between riders' risk-taking and opportunistic RLR behaviors. A total of 2057 cyclist samples approaching the intersections during red signals were observed by video in Beijing, China. To better capture the unobserved heterogeneity, apart from the traditional models, three advanced models including the random thresholds random parameters hierarchical ordered logit (RTRPHOL) model, the random parameters logit model with heterogeneity in means and variances (RPLHMV) model, and the correlated random parameters logit model with heterogeneity in means (CRPLHM), are developed. Results show that: 1) the unordered framework statistically outperformed its ordered counterparts, and the RPLHMV and CRPLHM models are statistically better than others. 2) The female and e-bicycle indicators produce a heterogeneity-in-means effect, and the low-volume and left-side indicators produce a heterogeneity-in-variances effect. 3) e-bike riders and riders from the right side are more inclined to have risk-taking behavior than opportunistic behavior, and both RLR behaviors of cyclists are most susceptible to the number of violating individual indicator. Findings illustrate that multilayer unobserved heterogeneity should be adequately considered in developing precise micro-simulation and practical guidance in traffic safety.

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.

Analyzing the impact of curve and slope on multi-vehicle truck crash severity on mountainous freeways

Many studies examine the road characteristics that impact the severity of truck crash accidents. However, some only analyze the effect of curves or slopes separately, ignoring their combination. Therefore, there are nine types of the combination of curve and slope in this study. The combination of curve and slope factor that affected the injury severity of truck crashes on mountainous freeways was examined using a correlated random parameter logit model. This method is applied to evaluate the correlation between the random parameters and those that exhibit unobserved heterogeneity. Also, the multinomial logit model and traditional random parameter logit model are used. The study's data were collected from multi-vehicle truck crashes on mountainous freeways in China. The results showed that the correlated random parameters logit model was better than the others. In addition, they demonstrated a correlation between the random parameters. Based on the estimation coefficients and marginal effects, the combination of curve and slope has a great influence on the injury severity of truck crashes. The main finding is that curve with medium radius and medium slope will significantly increase the probability of medium severity comparing to curve with high radius and flat slope. On the other hand, the injury severity of truck accidents was significantly impacted by crash type, vehicle type, surface condition, time of day, season, lighting condition, pavement type, and guardrail. Variables such as sideswipe, head-on, medium trucks, morning, dawn or dusk and summertime reduced the probability of truck crashes. Rollover, winter, gravel, and guardrail variables increased the risk of truck crashes. Correlations were also discovered between a rollover and dry surface condition and rollover and gravel pavement type. The research findings will help traffic officials determine effective countermeasures to decrease the severity of truck crashes on mountainous freeways.