Roadway Characteristics Research Articles

Spatiotemporal instability analysis considering unobserved heterogeneity of crash-injury severities in adverse weather

Adverse weather could potentially increase the probability of driving errors and hazardous driving actions and it is necessary to explicitly understand the endogenous and exogenous mechanism of how adverse weather-related determinants influence crash-injury severities and explore their spatiotemporal stability. To investigate the heterogeneity and spatiotemporal stability of adverse-weather-related crash severity determinants, this paper estimated two groups of random parameters multinomial logit models with heterogeneity in the means and variances. Crash data from Ohio and California were utilized between January 1, 2013 and December 31, 2016. Three crash injury severity categories were investigated including no injury, minor injury, and severe injury, in terms of multiple factors that could be categorized as roadway characteristics, environmental characteristics, crash characteristics, temporal characteristics, vehicle characteristics and driver characteristics significantly influencing adverse weather-related crash injury outcomes. Additionally, the temporal stability and space transferability of the models were investigated through a series of likelihood ratio tests. Marginal effects were also adopted to analyze the spatiotemporal stability of the explanatory variables. The findings exhibited an overall spatiotemporal instability while some indicators were also observed to be of relative spatial or temporal stability such as insurance, overturning, proceeding and early morning over the four-year period considered. This paper provided some immediate recommendations targeted at preventing crashes under adverse weather conditions across different regions and could potentially facilitate the development of crash injury mitigation policies. More regions could be considered to provide observations for spatial instability tests in future research.

A comprehensive analysis of factors that influence interstate highway crash severity in Alabama

This paper identifies factors that influence the severity of interstate crash outcomes and how they vary depending on the location and manner of collision. Four separate injury severity models were developed to explore the differences and similarities in crash factors between single-and multi-vehicle crashes that occurred in rural and urban areas of the state. Random parameters multinomial logit with heterogeneity in means and variances modeling approach was used to account for unobserved heterogeneity in the crash data. The model estimation results show that some driver behavioral factors such as speeding, aggressive driving, failure to use seatbelt, and driving without a valid license were found to significantly contribute to some form of injury outcome. The influence of roadway features such as type of opposing lane separation, collision type, temporal and lighting conditions on crash outcomes were also explored. Some differences and similarities in the associations between these factors and crash injury severity based on the manner and location of crash were unraveled. These findings are expected to guide the implementation of crash countermeasures on interstates. The findings of this study further support the evidence for the analysis of subsets of crash data to unravel underlying complex relationships within factors that influence crash injury severity.

An Analysis of Factors Affecting the Severity of Cycling Crashes Using Binary Regression Model

The increasing use of bicycles rises the interest in investigating the safety aspects of daily commuting. In this investigation, more than 14,000 cyclists’ injuries were analyzed to determine the relationship between severity, road infrastructure characteristics, and surface conditions using binary regression. Minor and major severity categories were distinguished. A binary equation consists of 28 factors is extracted. It has been found that each factor related to roadway characteristics has its negative and positive impacts on cyclist severity such as traffic control, location type, topography, and roadway divisions. Regarding the road surface components, good, paved, and marked roads are associated with a higher probability of major injuries due to the expected greater frequencies of cyclists on roads with good conditions. In conclusion, probabilities of major injuries are higher in urban areas, higher speed limits, signalized intersections, inclined topographies, one-way roads, and during the daytime which require more attention and better considerations.

Mixed logit models for examining pedestrian injury severities at intersection and non-intersection locations

Pedestrian injury has become a national traffic-safety concern as the share of pedestrian fatality continues to increase in the last decade. Pedestrian injury severities are influenced by many factors that include driver, pedestrian, vehicle, roadway, temporal, and environmental characteristics. Results indicate that some of the factors affecting pedestrian injury severity at intersection and non-intersection locations are statistically different and using the same model to perform the estimate at both locations may result in biased results. However, few studies have been conducted to explore different contributing factors at such locations. Mixed logit models are developed to independently identify the contributing factors to pedestrian injury severity resulting from crashes at intersections and non-intersections. The estimation shows factors such as male driver, alcohol, pedestrian above 65, truck, and higher speed limit significantly increase the probability of pedestrian serious injury severities in both locations. However, the impacts tend to be more severe at intersections. Urban and wet road surfaces decrease the likelihood of suffering fatal injury at intersections. Furthermore, crash time only has impacts at intersections, while traffic control, severe weather, and day-of-week only have impacts at non-intersections. The results provide insights on developing more effective countermeasures to promote pedestrian safety.

A spatial assessment of high-resolution drainage characteristics and roadway safety during wet conditions

Much of the research regarding traffic crashes considers various geometric roadway features; however, ever-evolving urban watersheds and climate change increasingly impact roadway conditions. Little research has focused on the relationship between high-resolution drainage characteristics and the spatial distribution of crashes. This study incorporated local environmental and drainage risk factors in assessing network safety performance using spatial analysis techniques. Kernel density surfaces and the Local Getis Ord Gi* statistics were used to identify and visualize locations prone to experiencing crashes during wet conditions. Spatial regression modelling was used to link crashes to environmental and traffic risk factors across a citywide network. Proof-of-concept for this framework is demonstrated in the City of Philadelphia, Pennsylvania using publicly available spatial data. The results of this study show a relationship between local drainage, environmental characteristics, and wet crash distribution, providing novel insight into roadway safety during wet conditions.

Exploring characteristics and influencing factors of crash duration on urban arterials and collectors

While traffic crash duration on freeways and its association with crash characteristics have been studied extensively, limited research exists regarding the characteristics of crash duration in urban arterials and collectors and their influential factors. To better understand this relationship, this study examines the effects of crash characteristics, roadway features, temporal variables, and other variables on crash duration. To this aim, the study applies the accelerated failure time (AFT) hazards-based model on principal arterials, minor arterials, major collectors, and minor collectors using Miami-Dade County in Florida (USA) as the study area. Results show that the average crash durations in minor arterials are longer than in all other road types. When it comes to the total time duration of crashes, different types of roads have different characteristics. Specifically, when the road crashes were cleared up within 76 min, principal arterials take a shorter time than the other three types of roads to clean the scene. On the contrary, when the crash endured more than 76 min for cleanups, the crash durations in collectors were shorter than in arterials. Consistent with findings from previous studies in freeways, the results show that severe crashes are associated with increased crash durations in most urban arterials and collectors. Moreover, the impact of crash characteristics on crash duration varies across road types. A greater number of vehicles and passengers involved in crashes is associated with longer durations in all but minor collectors. With regard to roadway features, the effects on crash durations vary by road type. In addition, early morning was more likely to be associated with longer crash durations than other periods, specifically strongly affecting the crash durations in minor collectors. These findings indicate that the effects of factors on crash durations vary depending on road type and may require different actions for their effective resolution.

Predicting Livelihood Indicators from Community-Generated Street-Level Imagery

Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world. We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourced street-level imagery. Such imagery can be cheaply collected and more frequently updated compared to traditional surveying methods, while containing plausibly relevant information for a range of livelihood indicators. We propose two approaches to learn from the street-level imagery: (1) a method that creates multi-household cluster representations by detecting informative objects and (2) a graph-based approach that captures the relationships between images. By visualizing what features are important to a model and how they are used, we can help end-user organizations understand the models and offer an alternate approach for index estimation that uses cheaply obtained roadway features. By comparing our results against ground data collected in nationally-representative household surveys, we demonstrate the performance of our approach in accurately predicting indicators of poverty, population, and health and its scalability by testing in two different countries, India and Kenya. Our code is available at https://github.com/sustainlab-group/mapillarygcn.

Investigation of crashes at pedestrian hybrid beacons: Results of a large-scale study in Arizona

Introduction: The pedestrian hybrid beacon (PHB) is a traffic control device used at pedestrian crossings. A recent Arizona Department of Transportation research effort investigated changes in crashes for different severity levels and crash types (e.g., rear-end crashes) due to the PHB presence, as well as for crashes involving pedestrians and bicycles. Method: Two types of methodologies were used to evaluate the safety of PHBs: (a) an Empirical Bayes (EB) before-after study, and (b) a long-term cross-sectional observational study. For the EB before-after evaluation, the research team considered three reference groups: unsignalized intersections, signalized intersections, and both unsignalized and signalized intersections combined. Results: For the signalized and combined unsignalized and signalized intersection groups, all crash types considered showed statistically significant reductions in crashes (e.g., total crashes, fatal and injury crashes, rear-end crashes, fatal and injury rear-end crashes, angle crashes, fatal and injury angle crashes, pedestrian-related crashes, and fatal and injury pedestrian-related crashes). A cross-sectional study was conducted with a larger number of PHBs (186) to identify relationships between roadway characteristics and crashes at PHBs, especially with respect to the distance to an adjacent traffic control signal. The distance to an adjacent traffic signal was found to be significant only at the α = 0.1 level, and only for rear-end and fatal and injury rear-end crashes. Conclusions: This analysis represents the largest known study to date on the safety impacts of PHBs, along with a focus on how crossing and geometric characteristics affect crash patterns. The study showed the safety benefits of PHBs for both pedestrians and vehicles. Practical Applications: The findings from this study clearly support the installation of PHBs at midblock or intersection crossings, as well as at crossings on higher-speed roads.

Exploring analytical, simulation-based, and hybrid model structures for multivariate crash frequency modeling

In safety literature, there are two ways to incorporate the potential correlation between multiple crash frequency variables: (1) simulation-based approach and (2) analytical closed-form approach. The current research effort undertakes a comparison between simulation-based multivariate model and copula based closed-form approach to analyze zonal level crash counts for different crash types. Further, the research builds on earlier copula based models by incorporating random parameters thus proposing a hybrid (combination of analytical and simulation based system) approach to incorporating unobserved heterogeneity. Within the proposed hybrid copula model, the empirical analysis involves estimation of count models using four different copula structures which cover a wide range of dependency structures, including radial symmetry and asymmetry, and asymptotic tail independence and dependence. Further, to the best of authors’ knowledge, this study is the first of its kind to incorporate attribute variability (random parameters) effect within the copula framework. The empirical analysis is based on traffic analysis zone (TAZ) level crash count data for both motorized and non-motorized crashes from Central Florida for the year 2016. A comprehensive set of exogenous variables including roadway, built environment, land-use, traffic, socio-demographic and spatial spillover characteristics are considered for the analysis. The resulting data fit and prediction performance offered by the proposed approach clearly highlights the hybrid model – Random Parameter Copula based approach’s superiority over the purely simulation-based multivariate model in our study context. The comparison exercise is further augmented by undertaking an in-depth comparison for different count events across different crash types and a correct classification analysis. The estimated results further reinforce the improved performance of the Random Parameter Copula-based multivariate approach. The applicability of the model for hot spot identification is illustrated by generating plots identifying high-crash and low- crash zones by crash type in the Central Florida region.

Investigating Head-On Crash Severity Involving Commercial Motor Vehicles in Kentucky

This study examines various features affecting the severity associated with commercial motor vehicle (CMV, i.e., large truck and bus) head-on collisions on Kentucky highways. Recent five-year (2015–2019) crash data and variables rarely explored before (e.g., presence of centerline rumble strips, type of passing zone, and terrain type) were collected and prepared using Google Maps. A total of 378 CMV-related head-on collisions were analyzed. The generalized ordered probit (GOP) model was employed to identify the significant factors affecting the severity level resulting from CMV head-on collisions. The model allows the coefficients to vary across the injury severity categories for reliable parameter estimations. From the preliminary investigation, rolling terrains had the highest share of severe CMV head-on crashes (62% and 71% for multilane and two-lane roadways, respectively). The presence of centerline rumble strips could reduce severe crash outcomes along multilane and two-lane facilities. The GOP model identified various significant predictors of minor and severe injuries from CMV head-on crashes. Occupants wearing seatbelt were 39.3% less likely to sustain severe head-on crash injuries. From the roadway characteristics, presence of median cable and concrete barriers could significantly reduce the probability of severe head-on crash injuries, with median cables being more effective. With regard to the driver characteristics, drug impairment and speeding increased the risk of sustaining fatal/serious injuries by 39.5% and 26.4%, respectively. Necessary safety recommendations are proposed to reduce the severity of CMV head-on-related collisions. One example is installing median cable barriers along roadway stretches with a history of head-on CMV-related crashes.

Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters

Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

Assessment of Commercial Truck Driver Injury Severity as a Result of Driving Actions

The disaggregate modeling approach is a new trend in the literature to analyze the injury severity of truck-involved crashes. The assessment of truck driver injury severity based on driver action is still missing in the literature. This paper presents an extensive exploratory analysis that highlights significant variability in the severity of truck drivers’ injuries based on various action types (i.e., aggressive driving, failure to keep proper lane, driving too fast, and no improper driving). Binary logistic regression with the Bayesian random intercept approach was developed to examine the factors contributing to fatal or any injuries of truck drivers using 10 years (2007–2016) of historical crash data in Wyoming. Log-likelihood ratio tests were performed to justify that separate models by various driving action types are warranted. The results demonstrated the effects of various vehicle, driver, crash, and roadway characteristics, combined with truck driver-specific action, on the corresponding severity of driver injury. The gross vehicle weight, age and gender of the driver, time of day, lighting condition, and the presence of junctions were found to have significantly different impacts on the severity of truck driver injury in various driving action-related crashes. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (27%–33%) of intra-crash correlation in driver injury severity within the same crash. Finally, based on the findings of this study, several recommendations are made.

Real-time detection and information-based construction of roadways passing through faults: a case study

During the period of working face passing through fault, the ground pressure behavior law of roadway in fault zone is complex, and study on roadway instability characteristics induced by fault-slip is a major concern for safety mining in underground coal mines. To investigate the distribution characteristics of mining stress under the influence of faults, a physical test was carried out with two fault structures. The results suggest that the slip and dislocation of faults induced by coal mining could cause rock strata to be impacted and crushed, which has a significant impact on the law of mine pressure behavior. The real-time detection including roof separation, anchor cable and bolt load was also carried out to evaluate the rationality of supporting scheme. The results showed that the roof separation was rapidly expanded within 10 m ahead of the working face with abrupt characteristics, reaching its peak within 2–6 h. The influence range of faults on roof anchor cable was within 30 m, and the anchor cable force was less than 40 kN under the influence of mining. The influence range of fault on roof bolts was less than 15 m, and on rib bolts was about 10 m. Field monitoring showed that 90% of the roof bolts pre-tightening force was less than 15 kN, which lead to insufficient support effect of bolts. The stability of the roadway was effectively controlled by adjusting the inter-row spacing of bolts and cables and increasing pre-tightening force of bolts. The research findings provide a basis for the optimization of support scheme, and also provide a reference for the support design of roadways under similar conditions.

Automatic Roadway Features Detection with Oriented Object Detection

Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.

Machine Learning Approach for Predicting Lane-Change Maneuvers using the SHRP2 Naturalistic Driving Study Data

Accurate lane-change prediction information in real time is essential to safely operate Autonomous Vehicles (AVs) on the roadways, especially at the early stage of AVs deployment, where there will be an interaction between AVs and human-driven vehicles. This study proposed reliable lane-change prediction models considering features from vehicle kinematics, machine vision, driver, and roadway geometric characteristics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. Several machine learning algorithms were trained, validated, tested, and comparatively analyzed including, Classification And Regression Trees (CART), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naïve Bayes (NB) based on six different sets of features. In each feature set, relevant features were extracted through a wrapper-based algorithm named Boruta. The results showed that the XGBoost model outperformed all other models in relation to its highest overall prediction accuracy (97%) and F1-score (95.5%) considering all features. However, the highest overall prediction accuracy of 97.3% and F1-score of 95.9% were observed in the XGBoost model based on vehicle kinematics features. Moreover, it was found that XGBoost was the only model that achieved a reliable and balanced prediction performance across all six feature sets. Furthermore, a simplified XGBoost model was developed for each feature set considering the practical implementation of the model. The proposed prediction model could help in trajectory planning for AVs and could be used to develop more reliable advanced driver assistance systems (ADAS) in a cooperative connected and automated vehicle environment.

Considering Roadway Context in Setting Posted Speed Limits

The National Cooperative Highway Research Program (NCHRP) Project 17-76 investigated factors that influence operating speed and safety through a review of the literature and an analysis of the relationships for speed, safety, and roadway characteristics on urban/suburban streets. That knowledge, along with a review of existing speed limit setting practices, was used to develop a Speed Limit Setting Procedure (SLS-Procedure) as well as a user manual to explain the SLS-Procedure. In addition, the SLS-Procedure was automated via a spreadsheet-based Speed Limit Setting Tool (SLS-Tool). These products will permit engineers to make informed decisions about the setting of speed limits. The SLS-Procedure is fact based and transparent, relying on a set of decision rules that consider both driver speed choice and safety associated with the roadway. The SLS-Procedure was designed to be applicable across different roadway types and contexts by having a set of unique decision rules for four combinations of roadway types and contexts: limited-access, undeveloped, developed, and full-access facilities. The SLS-Procedure uses the operating speed distribution as a starting point for the suggested speed limit, with the resulting suggested value based on consideration of roadway type, context, safety performance, and other characteristics.

Hot-spot analysis of motorcyclist crashes involving fixed objects using multinomial logit and data mining tools

There has been a significant increase in the number of motorcyclists in the past years. According to crash statistics, motorcyclist crashes involving roadside fixed objects and safety systems have a higher probability of resulting in a fatality and serious injury. Therefore, as the number of motorcyclists on the road increases, there is an urgent need to consider motorcyclist safety as an important factor when designing roadside safety systems. In this paper, we are developing a hot-spot detection methodology to identify the design characteristics of locations associated with severe motorcyclist crashes for improving roadside safety systems. For this purpose, we are using the multinomial logistic regression and data mining tools such as random forests and decision trees. The results of both data mining and regression analysis show that roadside safety devices and fixed objects have a significant impact on RwD motorcyclist crash severity. Additionally, roadway characteristics (horizontal and vertical curvature, lane width, urban-rural classification) and operational factors (traffic volume and posted speed limit) are found to be associated with the RwD motorcyclist crash severity. Hence, to identify the high-risk locations (i.e., locations with potential for improvement), these roadway elements need to be accounted for.

Large Deformation Control of Deep Roadways in Fractured Hard Rock Based on Cracking-Restraint Method

Large deformations often occur during and after the construction of deep roadways, particularly in fractured hard rock. With persistent deformation, there may be mechanistically unexplained, repeated failure phenomena—they are difficult problems to analyse and address. One strategy is to analyse the failure characteristics of roadways and internal cracking characteristics of the surrounding rock which can be analysed with a digital borehole televiewer and three-dimensional laser scanning equipment. Here, we used this approach combined with research on geostress, joint information, and mechanical properties of rock to reveal the preliminary large deformation mechanism of deep fractured roadways. Based on a cracking-restraint method for engineering design, a control technique is proposed to prevent and control large deformations. The depth and degree of cracking are considered to design parameters and determine the appropriate installation time of the support system. To test this approach, the proposed control technique was applied to a roadway project more than 700 meters deep. The monitoring results demonstrated that this control strategy effectively restrained the cracking of surrounding rocks, improved the quality index of the rock mass, and reduced surface displacement. In addition, due to the improved deformation capacity of the bolt structure, an anti-deformation and energy-absorbing bolt can bear large deformations, preventing premature bolt failures in mining environments.

Temporal analysis of crash severities involving male and female drivers: A random parameters approach with heterogeneity in means and variances

There is a great necessity to understand the endogenous and exogenous mechanism of how male and female driver characteristics determine the crash severity. Consequently, this paper estimated a group of random paramters logit models with heterogeneity in means and variances to investigate the heterogeneity and temporal stability of how male and female drivers affect crash severity. Using the crash data from California between January 1, 2013 and December 31, 2017, an extensive body of factors that could potentially affect crash injury severity were examined. Additionally, the temporal stability and gender transferability of the models were investigated through a series of likelihood ratio tests. Marginal effects were also adopted to analyze the temporal stability of the explanatory variables. Three crash injury severity categories were investigated including fatal injury, severe injury, and minor injury, in terms of roadway characteristics, environmental characteristics, crash characteristics, temporal characteristics and driver characteristics significantly influencing crash injury outcomes. Remarkable differences were observed between crashes involving male and female drivers, and both male and female related crashes exhibited statistically significant temporal instability over the five-year period considered. This paper could potentially be utilized to ameliorate highway safety aimed at male and female drivers respectively and facilitate the development of crash injury mitigation policies. Spatial stability may be also a valuable issue that should be further investigated in future research.

Case Study of Roadway Deformation Failure Mechanisms: Field Investigation and Numerical Simulation

The safety of underground roadways is a major issue in mining engineering, with economic impacts and potential threats to the lives of workers. Elucidating the deformation failure mechanisms is necessary to solve these problems. The deformation failure modes and characteristics of roadways buried at various depths were investigated using a detailed field survey in the Jinchuan nickel mine. At greater depths, roadway deformation was more serious, the creep phenomena were more prominent, and support structures were more prone to failure. Numerical simulations were performed on the roadways under various geo-stresses and rock mass structures, which indicated that the roadway deformation mode was mainly controlled by a rock mass structure in a lower stress environment and the control effect was weakened with the gradual increase of ground stress. Six deformation failure types were proposed to examine roadway deformation failure mechanisms. Field representation of each failure type was characterized under natural or induced conditions. The findings provide a reference for stability evaluation and support the design of roadway engineering under similar geological conditions.