Crash Occurrence Research Articles

Analysis of rural road traffic crashes in Al-Diwaniyah province using artificial neural network

Traffic crash commonly referred to as a car crashes with another vehicle or other objects, such as pedestrians, animals, road barriers, or other immovable structures like a utility pole or wall. This can result in injuries, fatalities, vehicle and road damage. In latest years, the number of traffic crashes has increased in Iraq, particularly on rural roads. Because of the severity of the rural road crash problem in Iraq, it has become critical to investigate and understand the causes of rural road traffic crashes. Rural road traffic crashes in Al-Diwaniyah Province haven't been highlighted since 2003. According to the Planning Ministry's yearly statistical report, Iraq (2020) the number of crashes recorded in Iraq is 8186, 587 of them are in Al-Diwaniyah Governorate. The percentage of traffic crashes in 2020 inside the city was 47.01% and outside it was 52.99%. The goal of this research is to analyze traffic crash data to identify the crucial factors that impact the occurrence of vehicle crashes in Al-Diwaniyah Province.

Influence of Risky Driving Behavior and Road Section Type on Urban Expressway Driving Safety

The causes of traffic crashes are complex and uncertain, among which the risky driving behaviors of drivers and the types of road sections in high-crash areas are all critical influencing factors. We used ArcGIS software to draw traffic heat maps under different thresholds to prevent the occurrence of traffic crashes accurately and effectively according to the vehicle GPS data of urban expressways in Jinan City, Shandong Province. This paper studied the relationship between risky driving behaviors (rapid acceleration, rapid deceleration, and overspeed) and road types with traffic crashes. The traffic safety evaluation model of urban expressways based on ordered logistic was established to predict the safety level of the urban expressway. The model’s accuracy was 85.71%, and the applicability was good. The research results showed that rapid deceleration was the most significant influencing factor of crashes on urban expressways. When the vehicle deceleration was less than or equal to −4 m/s2, the probability of a crash was 22.737 times greater than when the vehicle deceleration was at −2 to −2.5 m/s2; when the vehicle acceleration was greater than or equal to 3 m/s2, the probability of a crash was 19.453 times greater than when the vehicle acceleration was at 1 to 1.5 m/s2. The likelihood of a crash at a road section with a ramp opening was 8.723 times greater than that of a crash at a non-ramp opening; the crash probability of a speeding vehicle was 7.925 times greater than that of a non-speeding vehicle; the likelihood of a crash on a curve was 6.147 times greater than that on a straight. The research results can provide adequate technical support for identifying high-risk sections of expressways and active early warning of traffic crashes.

Investigating factors affecting rural crash frequency at marginal areas around cities in Iran: a random parameter modeling approach

ABSTRACT A significant portion of rural crashes in Iran occur in the marginal areas around cities. Exclusive crash frequency models should be developed to identify the factors affecting the occurrence of crashes in these areas. In this study, the rural crash data of highways leading to the city of Isfahan were employed to develop a negative binomial (NB) model as a constant-parameter model, along with random effects negative binomial (RENB) and random parameters negative binomial (RPNB) models as the models based on the random parameter approach. The modeling results indicated the superiority of the RPNB model over the two other models. According to the findings of this study, the traffic volume of roads and the proportion of local and non-local vehicles, the proportion of longitudinal distance-limit violation, the way of controlling access points, and the access to rest areas affected the crash frequency in the marginal areas around cities.

Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia

Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN’s 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed.

Enhanced index of risk assessment of lane change on expressway weaving segments: A case study of an expressway in China

Vehicles frequently change lanes at weaving segments, and there is a high probability of collision. To assess the risk of lane change, this study proposes a novel lane change risk assessment index (LCRAI), which takes both exposure time and conflict severity into consideration. First, the generalized time-to-collision (GTTC) and exposure-to-risk index (ERI) are proposed to evaluate conflicts during lane change, and then a spatiotemporal overlap analysis is applied to exclude the cases when two vehicles arrive at the conflict area at different times. Second, the change of velocity after a crash occurrence is calculated and used to evaluate the severity risk (SRI). Finally, ERI and SRI are combined to compute the LCRAI. A case study of a weaving segment is conducted, and the weaving segment is divided into four sections to compare their risks. Conflict frequency on the section close to the exit is the largest and it is smallest after passing the exit, while conflict severity is most serious just after entering the main line on the middle lane. As for both conflict frequency and severity, LCRAI shows that the middle section is the most dangerous while the last 100 m section is the safest in the weaving segment. The index proposed will contribute to the risk assessment of lane change in connected environments and improve safety in weaving segments of expressway combined with real-time safety countermeasures.

Wasserstein Generative Adversarial Network to Address the Imbalanced Data Problem in Real-Time Crash Risk Prediction

Real-time crash risk prediction models aim to identify pre-crash conditions as part of active traffic safety management. However, traditional models which were mainly developed through matched case-control sampling have been criticised due to their biased estimations. In this study, the state-of-art class balancing method known as the Wasserstein Generative Adversarial Network (WGAN) was introduced to address the class imbalance problem in the model development. An extremely imbalanced dataset consisted of 257 crashes and over 10 million non-crash cases from M1 Motorway in United Kingdom for 2017 was then utilized to evaluate the proposed method. The real-time crash prediction model was developed by employing Deep Neural Network (DNN) and Logistic Regression (LR). Crash predictions were performed under different crash to non-crash ratios where synthetic crashes were generated by Wasserstein Generative Adversarial Network (WGAN), Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) sampling respectively. Comparisons were then made with algorithmic-level class balancing methods such as cost-sensitive learning and ensemble methods. Our findings suggest that WGAN clearly outperforms other oversampling methods in terms of handling the extremely imbalanced sample and the DNN model subsequently produces a crash prediction sensitivity of about 70% with a 5% false alarm rate. Based on the findings of this study, proactive traffic management strategies including Variable Speed Limit (VSL) and Dynamic Messing Signs (DMS) could be deployed to reduce the probability of crash occurrence.

Econometric and Machine Learning Methods to Identify Pedestrian Crash Patterns

Walking plays an important role in overcoming many challenges nowadays, and governments and local authorities are encouraging healthy and environmentally sustainable lifestyles. Nevertheless, pedestrians are the most vulnerable road users and crashes with pedestrian involvement are a serious concern. Thus, the identification of pedestrian crash patterns is crucial to identify appropriate safety countermeasures. The aims of the study are (1) to identify the road infrastructure, environmental, vehicle, and driver-related patterns that are associated with an overrepresentation of pedestrian crashes, and (2) to identify safety countermeasures to mitigate the detected pedestrian crash patterns. The analysis carried out an econometric model, namely the mixed logit model, and the association rules and the classification tree algorithm, as machine learning tools, to analyse the patterns contributing to the overrepresentation of pedestrian crashes in Italy. The dataset consists of 874,847 crashes—including 101,032 pedestrian crashes—that occurred in Italy from 2014 to 2018. The methodological approach adopted in the study was effective in uncovering relations among road infrastructure, environmental, vehicle, and driver-related patterns, and the overrepresentation of pedestrian crashes. The mixed logit provided a clue on the impact of each pattern on the pedestrian crash occurrence, whereas the association rules and the classification tree detected the associations among the patterns with insights on how the co-occurrence of more factors could be detrimental to pedestrian safety. Drivers’ behaviour and psychophysical state turned out to be crucial patterns related to pedestrian crashes’ overrepresentation. Based on the identified crash patterns, safety countermeasures have been proposed.

Applying Explainable Machine Learning Techniques in Daily Crash Occurrence and Severity Modeling for Rural Interstates

Conventional traffic crash analysis methods often use highly aggregated data, making it difficult to understand the effects of time-varying factors on crash occurrence. Although studies have used data with small aggregation intervals, they typically analyze the effect of a single factor on crash occurrence. In this study, we investigate the collaborative effect of roadway geometry, speed distribution, and weather conditions on crash occurrence and severity using explainable machine learning methods on daily level crash data. The data were collected on rural Interstate highways in Texas. Four machine learning methods: random forest, AdaBoost, XGBoost, and deep neural network, were tested on the dataset. The results showed that XGBoost performs the best on the imbalanced dataset. The study used the synthetic minority oversampling technique (SMOTE) method to mitigate the data imbalance issue. The XGBoost model was trained separately on all crash occurrences and severe crash occurrences. Finally, the SHAP (SHapley Additive exPlanation) method was applied to investigate the contribution of all variables to the model’s output. The results showed that weather condition factors have a significant contribution to all crash occurrences. Speed distribution factors have a stronger impact on severe crash occurrences.

Safety evaluation of urban roundabouts in India: a safety performance function-based approach

There is a lack of comprehensive research in roundabout safety under non-lane-based traffic conditions, mainly due to the absence of relevant crash data and effective tools for safety evaluation. Safety Performance Function (SPF) is a suitable tool for providing valuable information related to factors which can potentially contribute to the likelihood of increasing traffic crashes. Currently, very limited studies are available to explain the usefulness of SPF in the vicinity of roundabouts, especially at non-lane-based traffic conditions. This study aims to develop an SPF model for assessing the safety evaluation at roundabouts as a whole (intersection level) and the approach level. Data corresponding to crashes in nineteen roundabouts with different geometric and traffic characteristics was used for model formulation. Crash data for five years (2015–2019) was obtained from the State Crime Records Bureau. An SPF model was developed using a negative binomial model with a log-link function based on the number of crashes, traffic characteristics, and geometry characteristics of the roundabouts. The proportion of powered two-wheelers, percentage of heavy vehicles, entry-angle, and weaving-length were all significantly related with higher crash occurrences at roundabouts, according to the findings. In contrast, the number of circulatory lanes, inscribed circle diameter, and presence of road lane marking were negatively associated with the increased crash occurrences at the roundabout vicinity. In addition to this, the overall crash rate significantly varies across roundabout sections due to the asymmetric effects of geometric and traffic characteristics. The developed SPF would best explain the relationship between geometric and traffic characteristics and the crash occurrence rate in non-lane traffic conditions. The findings of this study support the need to relook at design parameters for better movement at the roundabouts, thereby improving the existing facilities to enhance road users' safety, especially in developing countries. The proposed SPF tool would help engineers examine the safety of roundabouts in terms of design adequacy, quantifying the risk factors, and future crash predictions.

Driver performance at horizontal curves: bridging critical research gaps to increase safety

Horizontal curve segments are locations of critical safety concern given their high crash rates. Extensive research has identified that behavioral factors influence the occurrence of such crashes. However, the most beneficial countermeasure for a curve is not always implemented or is implemented inefficiently due to a lack of full understanding of driver behavior at curves compared to tangent roadway segments. The aim of this research is to identify the conditions that impact safety at curve locations compared to tangent segment locations. This is completed through a literature review of current research relating to curve safety issues and a safety analysis of curve and tangent segment data using a novel dataset that includes curve data throughout a region. The results of this study revealed several factors that cause horizontal curves to have a higher crash rate related to driver performance, including the increased task load and demand required at curve segments compared to tangent segments, and that horizontal curve segments have an increased rate of crashes per mile with an increasing AADT compared to tangent segments. Further, horizontal curve segments along one-way operations are of increased safety concern for drivers compared to tangent segments and two-way operations. The results of this study present the conditions that can be more carefully considered in future studies and analyses to consider the human factor cause behind the increased safety issue at curve segments.

The transformation of the insurance industry and road safety by driver safety behaviour telematics

In contemporary motorized societies, human factors remain overwhelmingly responsible for road crash occurrence. Urged by considerable technological developments, insurers have integrated telematics to their operations to develop an array of Usage-Based-Insurance (UBI) schemes. The aim of the present research is to expand on the aforementioned topics based on trends from recently published research and discuss the transformation of the insurance industry by driver safety behaviour telematics. Firstly, an overview of the relevant technological advancements is provided, followed by the integration practices of telematics adopted by the insurance industry. A more in-depth examination of the tangible benefits of telematics and driver feedback in road safety is subsequently provided. The paper concludes with a discussion on the future directions of the transformation of the insurance industry, along with the related challenges and limitations.

The relationship of bicycle use and aggressive driving behaviour: a latent variable model approach

ABSTRACT Aggressive driving behaviour has been related to the occurrence of risky situations and crashes. The current paper focuses on investigating individual characteristics as determinants of aggressive driving behaviour, with a specific focus on bicycle use. The analysis is based on a questionnaire survey conducted in a number of parking sites in the city centre of Thessaloniki, Greece. Aggressive driving was approximated as a latent variable with reported lane-changing behaviour, use of emergency lane on motorways and maximum speed in urban areas as its indicators. Sociodemographic characteristics and bicycle use were used as explanatory variables. The results showed that increase of aggressiveness had a positive relation to all indicators. Female respondents and older drivers were less likely to report aggressive behaviour. Also, bicycle users were less likely to be aggressive drivers compared to car drivers only. This finding shows that cycling could reduce aggressive behaviour and improve road safety.

Light where it matters: IIHS headlight ratings are correlated with nighttime crash rates

Introduction: Vehicle headlights are the primary means of providing visibility illumination for drivers at night, when crash rates are several times higher than during the day. Based on research indicating a wide range of headlight performance in the passenger vehicle fleet and the absence of a comprehensive and objective consumer evaluation program, the Insurance Institute for Highway Safety (IIHS) began testing and rating headlight systems in 2015. The purpose of this study was to examine the relationship between headlight visibility, as quantified by IIHS, and real-world crash occurrence. Methods: Poisson regression was used to estimate the effects of the headlight rating and the underlying demerits on the rate of police-reported nighttime single-vehicle crashes per vehicle mile traveled. Results: Results indicate that vehicles with better headlight visibility have lower nighttime crash rates after controlling for differences in daytime rates and other factors. A reduction of 10 visibility demerits, the equivalent of one overall rating band, was estimated to reduce the nighttime crash rate by 4.6% (95% CI: 2.1%–7.0%). While statistical significance was limited by small sample sizes, good-rated headlights were estimated to reduce crash rates by 12 to 29% relative to those with poor ratings for the different types of single-vehicle crashes studied. Among different components of the IIHS rating, the assessments of low and high beam curve visibility were associated with the greatest crash rate reductions. Conclusions: This study demonstrates that the IIHS evaluation program encourages headlight designs that reduce the risk of nighttime single-vehicle crashes. Practical applications: Headlight systems have a meaningful effect on nighttime crash rates. Drivers can reduce their crash risk by selecting a vehicle with one of the best designs.

Evaluation of Direct and Indirect Safety Effects of Speed-Limit Reduction on Urban Networks

This study develops a set of crash modification factors (CMFs) to evaluate the effects of lowering urban-road speed limits on vehicle and pedestrian safety. Cross-sectional methods and observation before–after methods are used to develop CMFs. In general, a CMF estimates the expected change in the frequency of crashes after specific countermeasures are applied on the road. In this study, the safety improvement effect in the section adjacent to the applied section as well as the section for which the policy to lower the speed limit was applied were evaluated. The results indicate that lowering the speed limit is effective in reducing the number of crashes. In particular, the CMFs for crashes involving serious injury and death are 0.6656–0.7804 in the application sections and 0.7979–0.8273 in the adjacent sections. This means that lowering the speed limit can reduce not only the number of crashes but also the occurrence of serious crashes. This study can be used to promote safety by analyzing the effect of the policy to lower the speed limits in the future and can be applied to the evaluation of the effectiveness of various safety policies in cities.

Assessment of Indiana Unmanned Aerial System Crash Scene Mapping Program

Many public safety agencies in the US have initiated a UAS-based procedure to document and map crash scenes. In addition to significantly reducing the time taken to document evidence as well as ensuring first responder safety, UAS-based mapping reduces incident clearance time and thus the likelihood of a secondary crash occurrence. There is a wide range of cameras used on these missions, but they are predominantly captured by mid-priced drones that cost in the range of $2000 to $4000. Indiana has developed a centralized processing center at Purdue University that has processed 252 crash scenes, mapped using 29 unique cameras, from 35 public agencies over the past three years. This paper includes a detailed case study that compares measurements obtained from a traditional ground-based real-time kinematic positioning base station and UAS-based photogrammetric mapping. The case study showed that UAS derived scale errors were within 0.1 ft (3 cm) of field measurements, a generally accepted threshold for public safety use cases. Further assessment was done on the 252 scenes using ground control scale error as the evaluation metric. To date, over 85% of the measurement errors were found to be within 0.1 ft (3 cm). When substantial errors are identified by the Purdue processing center, they are flagged for further dialog with the agency. In most of the cases with larger errors, the ground control distance was incorrectly measured, which is easily correctable by returning to the scene and performing new distance control measurements.

Accounting for unobserved heterogeneity and spatial instability in the analysis of crash injury-severity at highway-rail grade crossings: A random parameters with heterogeneity in the means and variances approach

Crashes at highway-rail grade crossings often result in higher proportion of injury and fatality of the vehicle occupants as compared to other crash types, necessitating in-depth investigation to identify their causal factors. In this study, injury-severity outcomes from highway-rail grade crossing crashes are analyzed using crash data from Texas and California, which are the most vulnerable states in the United States, in terms of highway-rail grade crossing crash occurrences. The data are collected from the Federal Railroad Administration’s (FRA) Office of Safety Analysis, covering a period between 2012 and 2020. Such data often suffer from out-of-date or missing information due to cost and available resources limitations, which inevitably may lead to unobserved characteristics varying systematically across various aspects of the data. Unobserved heterogeneity is an important misspecification issue, that in turn introduces modeling bias. To address these limitations, the random parameters multinomial logit modeling framework with heterogeneity in the means and variances is employed for the econometric analysis in this paper, which effectively accounts for multilayered unobserved heterogeneity. Spatial instability of the factors affecting different injury-severity levels is investigated as well. The results indicate that the factors are not spatially stable across Texas and California, leading to the estimation of two separate state-specific models. The estimation results of the two state-specific models help identify several vehicle-, train-, vehicle driver-, weather- and crossing-specific factors affecting different injury severity outcomes. Moreover, the results also demonstrate the varying magnitude of the identified factors on injury-severity across the two states, indicating the presence of spatial instability. The findings of this study highlight the importance of accounting for unobserved heterogeneity and spatial instability to avert critical methodological issues and misleading inferences from the simple aggregation used in most econometric analysis of highway-rail grade crossing crashes.

A spatial regression modeling framework for examining relationships between the built environment and pedestrian crash occurrences at macroscopic level: A study in a developing country context

Researchers have been trying to identify the contributory factors behind pedestrian crash occurrences through studies at both microscopic and macroscopic levels. However, built environment-related factors have primarily been examined in developed countries, resulting in a limited understanding of the phenomenon in the context of developing countries. Methodologically, these studies mostly used global regression models, which failed to incorporate spatial autocorrelation and spatial heterogeneity. Additionally, some of these studies applied spatial regression models randomly without following a comprehensive logical framework behind their selections. Our study aimed to develop a comprehensive spatial regression modeling framework to examine the relationships between pedestrian crash occurrences and the built environment at the macroscopic level in a megacity, Dhaka, the capital of a developing country: Bangladesh. Using secondary pedestrian crash data, the study applied one global non-spatial model, two global spatial regression models, and two local spatial regression models following a comprehensive spatial regression modeling framework. The factors which significantly contributed to pedestrian crash occurrences in Dhaka were employed person density, mixed and recreational land use density, primary road density, major intersection density, and share of non-motorized modes. Except for the last factor, all the other ones were positively related to pedestrian crash density. Among the five models used in this study, the multiscale geographically weighted regression (MGWR) performed the best as it calibrated each local relationship with a distant spatial scale parameter. The findings and recommendations presented in this study would be useful for reducing pedestrian crashes and choosing the appropriate modeling technique for crash analysis.

Inferring heterogeneous treatment effects of work zones on crashes

The increasing number of work zone crashes has been a significant concern for road users, transportation agencies, and researchers. Crashes can be caused by work zones, and this effect changes across different work zone configurations, traffic volumes, roadway functional classifications, and weather conditions. This is typically represented by Crash Modification Functions (CMFunctions). However, current methods for developing work zone CMFunctions have two major limitations: (1) They focus on analyzing statistical associations and fail to mitigate the confounding bias due to possible unobservable roadway characteristics; and (2) They cannot address CMFunctions of multiple variables simultaneously, such as weather and traffic conditions, since they are represented using mixed data types (continuous and categorical) that could potentially affect the causal effect of work zones on crashes. In this study, we develop a method that utilizes causal forest with fixed-effect modeling to mitigate the confounding bias while identifying CMFunctions conditioning on various environmental characteristics, including work zone configurations, traffic volume, roadway functional classification, and weather conditions. The developed method was applied to 3378 work zones that occurred in Pennsylvania between 2015 and 2017. The results were validated via a series of robustness tests. The validations demonstrate that this method can mitigate the confounding bias and identify CMFunctions of multiple variables. The results also show that the causal effect of a work zone on crash occurrence is significantly positive (p<0.05) on roadways with high traffic volumes (e.g., > 20,000 vehicles per day) and on medium length (e.g., 2000 to 5000 m) work zones. It appears that having medium–long (e.g., between 6000 and 8000 m) work zones or long duration (e.g., longer than 4 h) work zones do not necessarily lead to extra crashes.

Exploring Traffic Crash Occurrence Mechanism toward Cross-Area Freeways via an Improved Data Mining Approach

Exploring Traffic Crash Occurrence Mechanism toward Cross-Area Freeways via an Improved Data Mining Approach

CRASH PREDICTION ON ROAD SEGMENTS USING MACHINE LEARNING METHODS

This study adopted the Highway Safety Information System’s (HSIS) data for crashes occurred on road segments to develop supervised machine learning prediction models. Five machine learning models are developed: Linear Regression (LR), Generalize Additive Model (GAM), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). A comparison among the five model was performed using the root mean square error (RMSE) and the mean absolute error (MAE) as quality model indicators. The results indicated that the RF model was found to produce the best crash prediction results. The findings suggested that the increase in Annual Average Daily Traffic (AADT) exponentially increased the number of crashes on highway segments. In addition, roadway segments with the higher design speed induced the lower number of crashes, compared to the segments with the lower design speed. For segments of shorter than 5-mile long, the number of crashes rapidly increased as the segment length increased. However, there was no substantial increase in the number of crashes as the segment length increased for segments of longer than 5 miles. Also, the greater number of lanes on a roadway segment, the greater chance for increasing the number of crashes. Finally, the moderate grades showed the highest risk for occurrences of crashes, respectively followed by flat and rolling grades. These findings are useful for transportation professionals to consider when designing highways.