Impact of roadway geometric features on crash severity on rural two-lane highways

What would affect drivers' stop-and-go decisions at yellow dilemma zones? A driving simulator study in Hong Kong

Yellow dilemma, at which a driver can neither stop nor go safely after the onset of yellow signals, is one of the major crash contributory factors at the signal junctions. Studies have visited the yellow dilemma problem using observation surveys. Factors including road environment, traffic conditions, and driver characteristics that affect the driver behaviours are revealed. However, it is rare that the joint effects of situational and attitudinal factors on the driver behaviours at the yellow dilemma zone are considered. In this study, drivers' propensity to stop after the onset of yellow signals is examined using the driving simulator approach. For instances, the association between driver propensity, socio-demographics, safety perception, traffic signals, and traffic and weather conditions are measured using a binary logit model. Additionally, variations in the effect of influencing factors on driver behaviours are accommodated by adding the interaction terms for driver characteristics, traffic flow characteristics, traffic signals, and weather conditions. Results indicate that weather conditions, traffic volume, position of yellow dilemma in the sequence, driver age and safety perception significantly affect the drivers' propensity to stop after the onset of yellow signals. Furthermore, there are remarkable interactions for the effects of driver gender and location of yellow dilemma.

The safety impacts of paved shoulder width in Indian four-lane rural highways

The shoulder width, as a geometric element, plays a crucial role in enhancing highway safety. Research from high-income countries indicates that improving shoulders on highways leads to substantial safety benefits. However, the safety effectiveness of paved shoulders for low- and middle-income countries (LMICs) highway contexts has limited evidence. This study evaluated the safety effectiveness of the paved shoulder width on 61 km, four-lane, divided rural intercity highways in India. The first objective was to evaluate highway crash patterns using data from 2016 to 2019. The second objective was to evaluate the safety effectiveness of paved shoulder width using the case-control approach. The findings of this study demonstrate a consistent decline in the likelihood of crashes as the shoulder's width increases within the range of zero to 2.5 m for the 100 m segment length and zero to 1.7 m for the 500 m segment length. Nevertheless, model estimates indicate an increased crash risk for shoulders wider than 2.5 m. The results also suggested that the odds ratio for paved shoulder widths ranging from no shoulder to 2.5 m is likely to follow the crash modification factor from the highway safety manual. The findings of this study hold significant implications for the design policy of shoulder width on rural highways in LMICs.

Investigating effective factors on rural crash severity at marginal areas around cities in Iran: a partial proportional odds modelling approach

Over the past decade, rural crashes have been responsible for an average of 65% of crash-induced casualties in Iran. Evidence from prior studies reveals that a significant number of these rural crashes occur at marginal areas around cities. Thus, Exclusive crash severity models should be developed to identify the factors associated with higher injury and fatality probabilities in these areas. In this study, a partial proportional odds (PPO) model was formulated using the rural crash data collected from roads leading to the city of Isfahan. The PPO model holds the ordinal nature of crash observations and allows for different influences of independent variables on various crash severity levels. Insights derived from the results reveal that factors such as vehicle traffic maintaining an average speed exceeding 95 km/h, the occurrence of multi-vehicle crashes, the incidence of overturn-type crashes, the at-fault vehicle being a truck/trailer and at-fault or not-at-fault vehicle being a motorcycle, increase the likelihood of more severe rural crashes. Conversely, a foreign vehicle being at-fault, and the driver of the at-fault vehicle aged between 30 and 40 years, tend to diminish the occurrence of severe crashes at marginal areas around cities.

Modeling the lagged impacts of hourly weather and speed variation factors on the segment crash risk of rural interstate freeways: Applying a space-time-stratified case-crossover design

In the realm of traditional roadway crash studies, cross-sectional modeling methods have been commonly employed to investigate the intricate relationship between the crash risk of roadway segments and variables including roadway geometrics, weather conditions, and speed distribution. However, these methodologies assume that the explanatory variables and target variable are only associated within the same time period. Although this assumption is well-founded for static factors like roadway geometrics, it proves inadequate when dealing with highly time-varying variables related to weather conditions and speed variation. Recent investigations have unveiled that these time-varying variables may exhibit lagged impacts on segment crash risk, necessitating the adoption of more comprehensive time-series modeling methods. This study employs two interpretable statistical methods, namely the distributed lag model (DLM) and the distributed lag nonlinear model (DLNM), to elucidate meaningful and interpretable patterns of the lagged impacts of weather and speed variation factors on segment crash risk. Empirical evidence based on crash data collected from rural interstate freeways in the state of Texas demonstrates coherent and interpretable lagged impact patterns of these variables. This study's results serve as strong support for the existence of lagged impacts on roadway segment-level crash risk, emphasizing the need for considering time-series effects in future crash modeling research. Furthermore, these findings could offer practical implications for the design of real-time crash warning systems and the effective implementation of variable speed limits to enhance road safety.

A hierarchical multinomial logit model to examine the effects of signal strategies on right-turn crash injury severity at signalised intersections

The severity of right-turn crashes (or left-turn crashes for the roads in the US) at signalised intersections tends to be high because of the relatively high conflicting speeds and angle of impact. However, right-turn crash injury severity at signalised intersections was not sufficiently studied. In particular, the effects of signal control strategies on crash injury severity are not known. This study developed crash injury severity models for right-turn crashes at signalised intersections with a novel approach of linking crashes with signal strategies which enabled assessing the effects of signal strategies on crash injury severity. The study provided a comprehensive understanding of the impacts of signal strategies, intersection geometry and traffic factors on crash injury severity of right-turn crashes at signalised intersections. Crash injury severity models were estimated with crash data from 221 signalised intersections in Queensland from 2012 to 2018. To address the hierarchical structure of crash data, two-level hierarchical Multinomial Logit models were applied, hypothesising that the first level includes individual crash characteristics while the second level includes intersection characteristics. The applied hierarchical model accounts for the correlation among crashes within intersections. Results showed that crashes during Lagging right-turn and Diamond overlap turns are likely to be more severe than other signal strategies at intersections, with the Lagging right-turn signal being the most hazardous. The results also illustrate that the probability of severe injuries increases with the number of conflicting lanes, whereas the corresponding probability decreases with the occupancy of the conflicting lane.

A Hierarchical Multinomial Logit model to examine the effects of signal strategies on right-turn crash risks by crash movement configuration

Crash risk models relying on total crash counts are limited in their ability to extract meaningful insights regarding the context of crashes and to identify effective remedial measures. In addition to the typical classification of collisions noted in the literature (e.g., angle, head-on and rear-end), crashes can also be categorised according to vehicle movement configurations (Definitions for Coding Accidents or DCA codes in Australia). This classification presents an opportunity to extract useful insights into road traffic collision causes and contributing factors that are highly contextual. With this aim, this study develops crash-type models by DCA crash movement, with a focus on right-turn crashes (equivalent to left-turn crashes for right-hand traffic) at signalised intersections using a novel approach for linking crashes with signal control strategies. The modelling approach with contextual data enables quantification of the effect of signal control strategies on right-turn crashes, offering potentially unique and novel insights into right-turn crash causes and contributing factors. Crash-type models are estimated with the crash data of 218 signalised intersections in Queensland from 2012 to 2018. Multilevel (Hierarchical) Multinomial Logit Models with random intercepts are employed to capture the hierarchical influence of factors on crashes and unobserved heterogeneities. These models capture upper-level influences on crashes from intersection characteristics and lower-level influences from individual crash characteristics. The models specified in this way account for the correlation among crashes within intersections and influences on crashes across spatial scales. The model results reveal that the probabilities of the opposite approach crash type are significantly higher than the same direction and adjacent approach crash types for all right-turn signal control strategies at intersections except the split approach, for which the opposite is true. The results also suggest that the number of right-turning lanes and occupancy in conflicting lanes are positively associated with the likelihood of crashes for the same direction crash type.

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.

Automated vehicle data pipeline for accident reconstruction: New insights from LiDAR, camera, and radar data

As automated vehicles are deployed across the world, it has become critically important to understand how these vehicles interact with each other, as well as with other conventional vehicles on the road. One such method to achieve a deeper understanding of the safety implications for Automated Vehicles (AVs) is to analyze instances where AVs were involved in crashes. Unfortunately, this poses a steep challenge to crash-scene investigators. It is virtually impossible to fully understand the factors that contributed to an AV involved crash without taking into account the vehicle's perception and decision making. Furthermore, there is a tremendous amount of data that could provide insight into these crashes that is currently unused, as it also requires a deep understanding of the sensors and data management of the vehicle. To alleviate these problems, we propose a data pipeline that takes raw data from all on-board AV sensors such as LiDAR, radar, cameras, IMU's, and GPS's. We process this data into visual results that can be analyzed by crash scene investigators with no underlying knowledge of the vehicle's perception system. To demonstrate the utility of this pipeline, we first analyze the latest information on AV crashes that have occurred in California and then select two crash scenarios that are analyzed in-depth using high-fidelity synthetic data generated from the automated vehicle simulator CARLA. The data visualization procedure is demonstrated on the real-world Kitti dataset by using the YOLO object detector and a monocular depth estimator called AdaBins. Depth from LIDAR is used as ground truth to calibrate and assess the effect of noise and errors in depth estimation. The visualization and data analysis from these scenarios clearly demonstrate the vast improvement in crash investigations that can be obtained from utilizing state-of-the-art sensing and perception systems used on AVs.

A comprehensive analysis on the effects of signal strategies, intersection geometry, and traffic operation factors on right-turn crashes at signalised intersections: An application of hierarchical crash frequency model

Right-turn movements (equivalent to left turn movements for countries that drive on the right) at intersections are among the most complex driving maneuvers and require a high level of attention for turning across (potentially) oncoming traffic by accepting a safe gap. Not surprisingly, right-turn-involved crashes are one of the most frequent collision types at intersections (e.g., 42% of all signalised intersection crashes in Queensland, Australia). Unfortunately, the causes and contributing factors to right-turn crashes are not well understood, particularly the effect of right-turn signal strategies on the crash risk. In the safety literature, signal strategies are coarsely considered in two generic categories-protected right-turns and permitted right-turns. In reality, right-turn signal strategies could be of various types (usually 5) based on the level of intersection complexity and potential traffic conflicts. The effects of these signal strategies, along with the geometric and traffic factors, have not been well studied. To fill this gap, this study investigates the effects of right-turn signal strategies, intersection geometry and traffic operations factors on right-turn crashes at signalised intersections. To achieve this aim, crash frequency models were estimated using crash data from 221 signalised intersections in Queensland from the years spanning 2012 to 2018. Hierarchical Poisson Regression Models (random intercept models) were employed to capture the hierarchical structure of influences on crashes, with upper-level capturing intersection characteristics and lower-level capturing approach characteristics. The hierarchical model structure, disaggregate exposure variables, and signal strategies examined in this study give rise to an entirely unique study in the literature.

Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach

In many related works, nominal classification algorithms ignore the order between injury severity levels and make sub-optimal predictions. Existing ordinal classification methods suffer rank inconsistency and rank non-monotonicity. The aim of this paper is to propose an ordinal classification approach to predict traffic crash injury severity and to test its performance over existing machine learning classification methods. First, we compare the performance of the neural network, XGBoost, and SVM classifiers in injury severity prediction. Second, we utilize a severity category-combination method with oversampling to relieve the class-imbalance problem prevalent in crash data. Third, we take advantage of probability calibration and the optimal probability threshold moving to improve the prediction ability of ordinal classification. The proposed approach can satisfy the rank consistency and rank monotonicity requirement and is proved to be superior to other ordinal classification methods and nominal classification machine learning by statistical significance test. Important factors relating to injury severity are selected based on their permutation feature importance scores. We find that converting severity levels into three classes, minor injury, moderate injury, and serious injury, can substantially improve the prediction precision.

Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions, road characteristics and accident types

INTRODUCTION

Freeway accidents are a leading cause of death in China, which also triggers substantial economic loss and an emotional burden to society. However, the internal mechanism of how microscopic kinetic parameters of vehicles influenced by road characteristics determine the occurrence of different types of accidents has not been explicitly studied. This research aimed to explore the "link role" of tire microscopic kinetic parameters in road characteristic variables and traffic accidents to aid in facilitating the traffic design and management, and thus to prevent traffic accident.

METHOD

A mountain freeway in Zhejiang Province, China was used as the research object and the data used in this paper were obtained through a real-time vehicle experiment. Multiple estimation models, including the standard ordered logit (SOL) model, fixed parameters logit (FPL) model, and random parameters logit (RPL) model were established.

RESULTS

The findings show that road characteristics will affect the longitudinal kinetic characteristics of the vehicle and, consequently, map the level of risk of rear-end accidents. Driving compensation effects were also identified in this paper (i.e., the drivers tend to be more cautious in complicated driving circumstances). Another finding relating to the mountain freeway is that different tunnel characteristics (e.g., tunnel entrance and tunnel exit) have different effects on different types of traffic accidents. Practical Applications: The framework proposed in this article can provide new insight for researchers to enlarge the research subjects of both explanatory and outcome variables in accident analysis. Future research could be implemented to consider more driving conditions.

Investigating factors affecting bus/minibus accident severity in a developing country for different subgroup datasets characterised by time, pavement, and light conditions

Developing countries are primarily associated with poor roadway and lighting infrastructure challenges, which has a considerable effect on their traffic accident fatality rates. These rates are further increased as bus/minibus drivers indulge in risky driving, mainly during weekends when traffic and police surveillance is low to maximise profits. Although these factors have been mentioned in the literature as key indicators influencing accident severity of buses/minibuses, there is currently no study that explored the complex mechanisms underpinning the simultaneous effect of pavement and light conditions on the generation of accident severity outcomes while considering weekly temporal stability of the accident-risk factors. This study seeks to investigate the variations in the effect of contributing factors on the severity of bus/minibus accidents in Ghana across various combinations of pavement and light conditions and to identify the exact effects of weekdays and weekends on severity outcomes using a random parameter ordered logit model with heterogeneity in the means to account for unobserved heterogeneity in the police-reported data. Preliminary analysis demonstrated that accident-risk factors used in the models were temporally unstable, warranting the division of the data into both weekend and weekday time-periods. A wide variety of factors such as sideswipes, median presence, merging, and overtaking had significantly varying effects on bus/minibus accident severities under different combinations of pavement and light conditions for both weekdays and weekends. Insights drawn from this study, together with the policy recommendations provided, can be employed by engineers and policymakers to improve traffic safety in developing nations.

Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study

A better understanding of injury severity risk factors is fundamental to improving crash prediction and effective implementation of appropriate mitigation strategies. Traditional statistical models widely used in this regard have predefined correlation and intrinsic assumptions, which, if flouted, may yield biased predictions. The present study investigates the possibility of using the eXtreme Gradient Boosting (XGBoost) model compared with few traditional machine learning algorithms (logistic regression, random forest, and decision tree) for crash injury severity analysis. The data used in this study was obtained from the traffic safety department, ministry of transport (MOT) at Riyadh, KSA, and contains 13,546 motor vehicle collisions along 15 rural highways reported between January 2017 to December 2019. Empirical results obtained using k-fold (k = 10) for various performance metrics showed that the XGBoost technique outperformed other models in terms of the collective predictive performance as well as injury severity individual class accuracies. XGBoost feature importance analysis indicated that collision type, weather status, road surface conditions, on-site damage type, lighting conditions, and vehicle type are the few sensitive variables in predicting the crash injury severity outcome. Finally, a comparative analysis of XGBoost based on different performance statistics showed that our model outperformed most previous studies.

GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance

Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals.

Injury severity analysis of familiar drivers and unfamiliar drivers in single-vehicle crashes on the mountainous highways

Mountainous highways suffer from high crash rates and fatality rates in many countries, and single-vehicle crashes are overrepresented along mountainous highways. Route familiarity has been found greatly associated with driver behaviour and traffic safety. This study aimed to investigate and compare the contributory factors that significantly influence the injury severities of the familiar drivers and unfamiliar drivers involved in mountainous highway single-vehicle crashes. Based on 3037 cases of mountainous highway single-vehicle crashes from 2015 to 2017, the characteristics related to crash, environment, vehicle and driver are included. Random-effects generalized ordered probit (REGOP) models were applied to model injury severities of familiar drivers and unfamiliar drivers that are involved in the single-vehicle crashes on the mountainous highways, given that the single-vehicle crashes had occurred. The results of REGOP models showed that 8 of the studied factors are found to be significantly associated with the injury severities of the familiar drivers, and 10 of the studied factors are found to significantly influence the injury severities of unfamiliar drivers. These research results suggest that there is a large difference of significant factors contributing to the injury severities between familiar drivers and unfamiliar drivers. The results shed light on both the similar and different causes of high injury severities for familiar and unfamiliar drivers involved in mountainous highway single-vehicle crashes. These research results can help develop effective countermeasures and proper policies for familiar drivers and unfamiliar drivers targetedly on the mountainous highways and alleviate injury severities of mountainous highway single-vehicle crashes to some extent. Based on the results of this study, some potential countermeasures can be proposed to minimize the risk of single-vehicle crashes on different mountainous highways, including tourism highways with a large number of unfamiliar drivers and other normal mountainous highways with more familiar drivers.

Identifying heterogeneous factors for driver injury severity variations in snow-related rural single-vehicle crashes

Snowy weather is consistently considered as a hazardous factor due to its potential leading to severe fatal crashes. A seven-year crash dataset including rural highway single vehicle crashes from 2010 to 2016 in Washington State is applied in the present study. Pseudo elasticity analysis is conducted to investigate significant impact factors and the temporal stability of model specifications is tested via a likelihood ratio test. The proposed model based on the seven-year dataset is able to capture the individual-specific heterogeneity across crash records for four significant factors, i.e., surface ice, male, and airbag combine deployment for minor injury, and male for serious injury and fatality. Their estimated parameters were found to be normal distribution instead of fixed value over the observations. Other significant impact factors with fixed effects are: inroad object, animal, overturn, surface wet, surface snow, unusual horizontal design, medium and high speed limits, driver age, impaired condition, no belt usage, vehicle type, airbag deployment. Especially, when compared to significant factors for crashes under other weather conditions, male indicator and impaired condition show significant higher effects in snow-related crashes. The results of temporal stability test show that the model specification is generally not temporally stable for driver injury severity model based on the years of crash data that were used, especially for longer period (more than 3-year dataset). Models that allow the explanatory variables to track temporal heterogeneity, are of great interest and can be explored in future research.

Applying hierarchical logistic models to compare urban and rural roadway modeling of severity of rear-end vehicular crashes

A rear-end crash is a widely studied type of road accident. The road area at the crash scene is a factor that significantly affects the crash severity from rear-end collisions. These road areas may be classified as urban or rural and evince obvious differences such as speed limits, number of intersections, vehicle types, etc. However, no study comparing rear-end crashes occurring in urban and rural areas has yet been conducted. Therefore, the present investigation focused on the comparison of diverse factors affecting the likelihood of rear-end crash severities in the two types of roadways. Additionally, hierarchical logistic models grounded in a spatial basis concept were applied by determining varying parameter estimations with regard to road segments. Additionally, the study compared coefficients with multilevel correlation model and those without multilevel correlation. Four models were established as a result. The data used for the study pertained to rear-end crashes occurring on Thai highways between 2011 and 2015. The results of the data analysis revealed that the model parameters for both urban and rural areas are in the same direction with the larger number of significant parameter values present in the rural rear-end crash model. The significant variables in both the urban and rural road segment models are the seat belt use, and the time of the incident. To conclude, the present study is useful because it provides another perspective of rear-end crashes to encourage policy makers to apply decisions that favor rules that assure safety.

Applying the colocation quotient index to crash severity analyses

Examining the spatial relationships among crashes of various severity levels is essential for gaining a better understanding of the severity distribution and potential contributing factors to collisions. However, relatively few scholars have focused on analyzing this type of data. Therefore, in this study, we utilized a new index, the colocation quotient, to measure the spatial associations among crashes of various severities that occurred in College Station, Texas. This new method has been widely used to define the colocation pattern of categorized data in various fields, but it has not yet been applied to crash severity data. According to our findings, (1) crashes tended to be at the same injury level as those of neighboring ones, which was most significant for fatal crashes and second most significant for non-injury crashes; (2) the colocation quotient matrix tended to be symmetrical in non-injury crashes versus injury crashes (minor injury, major injury, and fatal); and, (3) DWIs (driving while intoxicated) and hit-and runs did not show a strong pattern. These colocation quotient results could be helpful for predicting crash severity and by providing traffic engineers with more effective traffic safety measures.

The Dilemma of Road Safety in the Eastern Province of Saudi Arabia: Consequences and Prevention Strategies

Road traffic crashes (RTCs) are one of the most critical public health problems worldwide. The WHO Global Status Report on Road Safety suggests that the annual fatality rate (per 100,000 people) due to RTCs in the Kingdom of Saudi Arabia (KSA) has increased from 17.4 to 27.4 over the last decade, which is an alarming situation. This paper presents an overview of RTCs in the Eastern Province, KSA, from 2009 to 2016. Key descriptive statistics for spatial and temporal distribution of crashes are presented. Statistics from the present study suggest that the year 2012 witnessed the highest number of crashes, and that the region Al-Ahsa had a significantly higher proportion of total crashes. It was concluded that the fatality rate for the province was 25.6, and the mean accident to injury ratio was 8:4. These numbers are substantially higher compared to developed countries and the neighboring Gulf states. Spatial distribution of crashes indicated that a large proportion of severe crashes occurred outside the city centers along urban highways. Logistic regression models were developed to predict crash severity. Model estimation analysis revealed that crash severity can be attributed to several significant factors including driver attributes (such as sleep, distraction, overspeeding), crash characteristics (such as sudden deviation from the lane, or collisions with other moving vehicles, road fences, pedestrians, or motorcyclists), and rainy weather conditions. After critical analysis of existing safety and infrastructure situations, various suitable crash prevention and mitigation strategies, for example, traffic enforcement, traffic calming measures, safety education programs, and coordination of key stakeholders, have been proposed.

Evaluating Pedestrians’ Safety on Urban Intersections: A Visibility Analysis

Overall visibility plays a key role in the safety of pedestrians. Despite its importance, verifying the right provisioning of sufficient available sight distances among pedestrians and vulnerable road users (VRUs) is not a prevalent practice. On top of that, the pursuit for more sustainable modes of transportation has promoted the establishment of different shared mobility services which are prone to increase walking and, thus, the number of pedestrians and other VRUs in urban settings. With the intention of verifying how car-centered designs perform for non-motorized users, a 3D procedure that evaluates the visibility of pedestrians and other users is presented and applied to specific cases in Madrid, Spain. The proposed solution employs virtual trajectories of pedestrians with mobility impairments and without them, cyclists, and personal transportation device riders. Their visibility was assessed around the functional area of urban intersections, including zones where possible jaywalking practices might occur. The evaluation was performed three-dimensionally, making use of LiDAR data, GIS tools, and 3D objects. Results show the impact of street furniture location on visibility, the distinctive influence of vegetation on the lines of sight of each observer, and how design parameters that were intended to improve motorized traffic could affect VRU.

The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data

While the cost of crashes exceeds $1 Trillion a year in the U.S. alone, the availability of high-resolution naturalistic driving data provides an opportunity for researchers to conduct an in-depth analysis of crash contributing factors, and design appropriate interventions. Although police-reported crash data provides information on crashes, this study takes advantage of the SHRP2 Naturalistic Driving Study (NDS) which is a unique dataset that allows new insights due to detailed information on driver behavior in normal, pre-crash, and near-crash situations, in addition to trip and vehicle performance characteristics. This paper investigates the role of pre-crash driving instability, or driving volatility, in crash intensity (measured on a 4-point scale from a tire-strike to an injury crash) by analyzing microscopic vehicle kinematic data. NDS data are used to investigate not only the vehicle movements in space but also the instability of vehicles prior to the crash and their contribution to crash intensity using path analysis. A subset of the data containing 617 crash events with around 0.18 million temporal trajectories are analyzed. To quantify driving instability, microscopic variations or volatility in vehicular movements before a crash are analyzed. Specifically, nine measures of pre-crash driving volatility are calculated and used to explain crash intensity. While most of the measures are significantly correlated with crash intensity, substantial positive correlations are observed for two measures representing speed and deceleration volatilities. Modeling results of the fixed and random parameter probit models revealed that volatility is one of the leading factors increasing the probability of a severe crash. Additionally, the speed prior to a crash is highly correlated with intensity outcomes, as expected. Interestingly, distracted and aggressive driving are highly correlated with driving volatility and have substantial indirect effects on crash intensity. With volatile driving serving as a leading indicator of crash intensity, given the crashes analyzed in this study, early warnings and alerts for the subject vehicle driver and proximate vehicles can be helpful when volatile behavior is observed.

Age-Related Differences in Motor-Vehicle Crash Severity in California

With ongoing changes in the age distribution of drivers in the United States, it is important to obtain insights on how to make the roadways equally safe for drivers across different age groups. In light of this, the objective of this study is to examine various crash characteristics and make recommendations on how to potentially improve roadway safety for all age groups. Using the Highway Safety Information System (HSIS) data, this study investigates the factors influencing motor-vehicle crash injury severity for young (aged 16–25), middle-aged (aged 26–64), and older drivers (above 64) in the state of California. A multinomial logit model was used to separately model crashes involving each age group and to evaluate the weight of different predictor variables on driver injury severity. The predictor variables were classified into four—driver, roadway, accident and environmental characteristics. Results suggest that there are close relationships between severity determinants for young and middle-aged drivers. However, older drivers tend to be most cautious among all age groups under all environmental and roadway conditions. Young drivers are more likely to explore their driving skills due to newness to driving. Middle-aged drivers are familiar with driving and tend to demonstrate less cautious behaviors, especially male drivers. Another insight obtained from this study is that older driver behavior is less dynamic compared to other age groups; their driving pattern is usually regular regardless of the surrounding conditions.