Investigating hazardous factors affecting freeway crash injury severity incorporating real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors

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.

An application of the hybrid AHP-PROMETHEE approach to evaluate the severity of the factors influencing road accidents

The evaluation of the severity of the factors influencing road accidents with a detailed severity distribution is critical to plan evidence-based road safety improvements and strategies. However, currently available studies use statistical and machine learning (ML) models to evaluate the severity of factors causing road accidents without a detailed severity distribution. Further, most of these available models require significant pre-data processing and have certain data-centric limitations. However, the multi criteria decision-making (MCDM) techniques have the potential to combine expert opinions for robust analysis without any pre-data processing calculations. Thus, this study uses a hybrid analytic hierarchy process (AHP) and the preference ranking organisation method for enrichment evaluation (PROMETHEE) approach to analyse the severity of factors and characteristics that influence road accidents within the Gujarat state, using injury types as criteria and minor road accident influencing factors as alternatives. These 82 minor factors have been further characterised into 18 characteristics and 4 major factors. Further, AHP integrated 40 expert inputs to determine criterion weights, while PROMETHEE ranked all minor variables. Then, after applying k-mean clustering, each ranked factor has been classified as very severe, moderately severe, or severe. The result clearly highlights that overspeeding, male gender, and clear weather conditions have been concluded to be the highly severe factors for Gujarat state. Thus, by providing a clear severity analysis and distribution of factors influencing road accidents, the proposed research may help government stakeholders, researchers, and politicians build severity-based road safety reforms and strategies with clarity.

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.

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.

How do contract performance rates affect entrepreneurs’ risk-averse attitudes? Evidence from China

Introduction

Entrepreneurs’ attitudes toward risk is one of the most critical factors influencing business growth and economic development. Therefore, addressing the influencing factors and formation mechanisms of entrepreneurs’ risk attitudes has become a crucial research endeavor. In this paper, we examine how contract performance rates affect entrepreneurs’ risk attitudes through the mediating effect of subjective well-being as well as assess the moderating effect of the regional business environment on this relationship.

Methods

The ordered probit regression technique was employed to analyze the data obtained from 3,660 sampled respondents from the 2019 China Household Finance Survey. All analysis was performed using Stata 15.0.

Results

The empirical results show that contract performance rates have a substantial positive indirect effect on entrepreneurs’ degree of risk aversion through improved subjective well-being. The regional business environment plays a negative regulatory role in the relationship between contract performance rates and entrepreneurs’ risk aversion. Furthermore, urban–rural heterogeneity appears to consistently determine the extent of the influence of contract performance rates on entrepreneurs’ risk attitudes.

Conclusion

To reduce entrepreneurs’ risk aversion and enhance social and economic activity, the government should improve regional business environments by taking specific measures. Our study contributes to the empirical understanding of entrepreneurs’ investment decisions in the context of urban and rural environments.

Driver, roadway, and weather factors on severity of lane departure crashes in Maine

INTRODUCTION

In Maine, lane departure crashes account for over 70% of roadway fatalities. The majority of roadways in Maine are rural. Moreover, Maine has aging infrastructure, houses the oldest population in the United States, and experiences the third coldest weather in the United States.

METHODS

This study analyzes the impact of roadway, driver, and weather factors on the severity of single-vehicle lane departure crashes occurring from 2017 to 2019 on rural roadways in Maine. Rather than using police reported weather, weather station data were utilized. Four facility types: Interstates, minor arterials, major collectors, and minor collectors were considered for analysis. The Multinomial Logistic Regression model was used for the analysis. The property damage only (PDO) outcome was considered as the reference (or base) category.

RESULTS

The modeling results show that the odds of a crash leading to major injury or fatality (KA outcome) increases by 330%, 150%, 243%, and 266% for older drivers (65 or above) compared to young drivers (29 or less) on Interstates, minor arterials, major collectors, and minor collectors, respectively. During the winter period (October to April), the odds of KA severity outcome (with respect to the PDO) decreases by 65%, 65%, 65%, and 48% on Interstates, minor arterials, major collectors, and minor collectors, respectively, presumably due to reduced speeds during winter weather events.

CONCLUSION

In Maine, factors such as older drivers, operating under the influence, speeding, precipitation, and not wearing a seatbelt showed higher odds of leading to injury.

PRACTICAL APPLICATIONS

This study provides safety analysts and practitioners in Maine a comprehensive study of factors that influence the severity of crashes in Maine at different facilities to improve maintenance strategies, enhance safety using proper safety countermeasures, or increase awareness across the state.

Analysis of the Impact of Invisible Road Icing on Selected Parameters of a Minibus Vehicle

The measurement of acceleration during vehicle motion can be used to assess the driving styles and behaviours of drivers, to control vehicle traffic, to detect uncontrolled vehicle behaviour, and to prevent accidents. The measurement of acceleration during vehicle motion on an icy road can be used to warn the driver about changing conditions and the related hazards. This paper presents the results of testing the motion parameters of a Ford Transit adapted for passenger transport in critical traffic conditions. It can contribute to the improvement of road safety. Critical traffic conditions are deemed in the paper as sudden braking, rapid acceleration, and circular vehicle motion at maximum speed maintainable in the given conditions. The vehicle's acceleration and speed were measured during the tests. The tests were carried out with a TAA linear acceleration sensor and a Correvit S-350 Aqua optoelectronic sensor. The same test runs were conducted on a dry surface, a wet (after rain) surface and a surface covered with a thin, invisible ice layer. The objective of the tests was to determine the impact of invisible road icing, the so-called black ice, on the tested vehicle's braking, acceleration, and circular motion. It was demonstrated that a virtually invisible ice layer covering the road surface has a substantial impact on the tested vehicle's motion parameters, thereby affecting traffic safety. It substantially extends the braking and acceleration distances and requires the driver to reduce the vehicle's speed when performing circular motions. A clear wet surface, representing motion after rain, did not substantially affect the analysed parameters. The obtained results can be used in traffic simulations and to analyse the causes of accidents.

Exploring the effects of stationary camera spots on inferences drawn from real-time crash severity models

This study combined crash reports, land use, real-time traffic, and weather data to form an integrated database to analyze the severity of crashes taking place on rural highways. As the traffic cameras are placed at fixed locations, there is a wide range of measured distances between crashes and the selected nearest camera for extracting traffic variables. This may change the significance of traffic variables. For the first time, spacing was introduced as the distance around the detectors in which traffic characteristics are inferred to crashes. Classification and Regression Tree (CART) was employed as an interpretable tool to explore how spacing affects model performance and the significance of traffic variables. Twelve spacing scenarios from 250 to 3000 m were evaluated. Except for short spacings suffering from the low sample size issue, each model has a good predictive performance based on overall accuracy and F2 score in the 1000-3000 m spacings. In this range, three dominant rules emerged: (1) high deviations of speed on the roads surrounded by wastelands are associated with severe crashes; (2) faded markings in residential zones increase the likelihood of severe outcomes; (3) installation of barriers decrease the probability of severe crashes. Comparing the Variable Importance Measure (VIM) reveals that the total importance of traffic variables reduces as the spacing increases. Also, results indicate that average speed is significant until 1750 m; but speed deviation, traffic flow, and percent of heavy vehicles are more stable variables for further spacings. In conclusion, for the first time, spacing scenarios were evaluated systematically and proved that they have a remarkable impact on the significance of variables. This novel research provides guidance not only on the spacing but also on which real-time traffic variables have a greater impact on crash severity, along with design, land use, and environmental variables.

Comparative Analysis of Influencing Factors on Crash Severity between Super Multi-Lane and Traditional Multi-Lane Freeways Considering Spatial Heterogeneity

With the growth of traffic demand, the number of newly built and renovated super multi-lane freeways (i.e., equal to or more than a two-way ten-lane) is increasing. Compared with traditional multi-lane freeways (i.e., a two-way six-lane or eight-lane), super multi-lane freeways have higher design speeds and more vehicle interweaving movements, which may lead to higher traffic risks. However, current studies mostly focus on the factors that affect crash severity on traditional multi-lane freeways, while little attention is paid to those on super multi-lane freeways. Therefore, this study aims to explore the impacting factors of crash severity on two kinds of freeways and make a comparison with traditional multi-lane freeways. The crash data of the Guangzhou-Shenzhen freeway in China from 2016 to 2019 is used in the study. This freeway contains both super multi-lane and traditional multi-lane road sections, and data on 2455 crashes on two-way ten-lane sections and 13,367 crashes on two-way six-lane sections were obtained for further analysis. Considering the effects of unobserved spatial heterogeneity, a hierarchical Bayesian approach is applied. The results show significant differences that influence the factors of serious crashes between these two kinds of freeways. On both two types of freeways, heavy-vehicle, two-vehicle, and multi-vehicle involvements are more likely to lead to serious crashes. Still, their impact on super multi-lane freeways is much stronger. In addition, for super multi-lane freeways, vehicle-to-facility collisions and rainy weather can result in a high possibility of serious crashes, but their impact on traditional multi-lane freeways are not significant. This study will contribute to understanding the impacting factors of crash severity on super multi-lane freeways and help the future design and safety management of super multi-lane freeways.

Development of crash prediction models by assessing the role of perpetrators and victims: a comparison of ANN & logistic model using historical crash data

Road traffic injuries cost countries 3% of their annual GDP. In developing countries like India, every year around 150,000 people die on roads. The type of vehicles involved in a crash contribute majorly to the outcome of casualty (injury/death). Barring few studies, literature are less regarding the role of vehicle as perpetrator and victim on road crash fatalities. Historical crash data has been used in the present study to examine the role of vehicles (both as perpetrator & victim). The study reveals that victim's effect is more as compared to perpetrator/accused for determining the outcome of crash. Heavy vehicles as perpetrator, and self-hitting vehicles along with pedestrians as victims have higher fatality rates. Binary logistic regression and artificial neural network (ANN) has been utilized for developing prediction models. Binary logistic model predicted around 75% of outcomes correctly with default cut-off value (0.5). However, based on reported crash data, where 19% of total crashes lead to deaths, 0.19 has been proposed as cut-off value which increases the accuracy of the predictions. Accuracy of ANN technique directly depends on the number of crashes reported for a definite pair of perpetrator and victim and the type of validation technique used (Holdback/K-Fold) along with the type of hidden layer chosen for the study based on different types of sigmoid activation function. ROC curves in ANN suggest that the analysis can predict 75% of the outcomes which can be increased by deleting the pairs of vehicles which are present/have occurred in very less number. A comparison has been made between the two techniques based on their advantages and limitations. The developed models can be used as safety indicators based on composition of traffic flow on urban roads.

Does the energy consumption revolution improve the health of elderly adults in rural areas? Evidence from China

About 2.8 billion people around the world still depend on biomass as their primary energy source. This traditional custom affects the environment and severely impacts the health and life quality of the rural residents, especially in the elderly group. Based on the published data from the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2018, this paper carefully explores the impact of the energy consumption revolution on the health of rural elderly adults and its response mechanism. The results from the empirical analysis show that: (1) The positive response of rural families to the energy consumption revolution can significantly improve the elderly adults' health condition; (2) The energy consumption revolution can improve the rural elderly adults' health by enhancing the home environmental sanitation (home environmental effect) and life satisfaction (psychological effect); (3) In the process of energy consumption revolution affecting the rural elderly's health, the factor age plays a negative regulatory role, in other words, the older the elderly, the lower the marginal effect of energy consumption revolution on their health improvement. This paper uses the propensity score matching method to deal with the endogenous problem of the regression model, and uses a placebo test and the substitution estimation method to check the robustness of the empirical results. As well, this research puts forward some policy suggestions, such as increasing investment in energy infrastructure in rural areas, reducing the cost of using clean energy, combining environmental energy protection with increasing energy income, and improving medical security conditions in rural areas.