Probability Of Crash Research Articles

The Design of Traffic Signals at Intersection

The traffic volume at intersections has been arising a problem which leads to accidents, conflicts and congestions. An efficient traffic control at intersections helps in reduction of delays of all vehicles and the probability of crashes. The movement of vehicles efficiency is increased by providing traffic signals, roundabouts, channelized islands at an intersection. Signal timing is most important if we use traffic signals in which green time decides the efficiency of movement of vehicles along with pedestrian walk timing. At an intersection, traffic volume studies are done with the movement and classification of vehicles along with the geometric elements of the road. The video graphic method is used to collect the volume of vehicles for every 15- minutes interval and converted into PCU units. Webster’s and IRC code method are used for signal design. Saturation flow, lost times and passenger car units, width of the road are the significant parameters used to optimize the signal timing in planning, design and control of signalized intersection.

Multilevel Mixed-Effect Logit Models of Influencing Factors on Expressway Crash Probability by Vehicle Type

Multilevel Mixed-Effect Logit Models of Influencing Factors on Expressway Crash Probability by Vehicle 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.

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.

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.

A Data-Driven Predictive Model for Speed Control in Automotive Safety Applications

The importance of transportation to society and daily life makes safe driving an important responsibility to reduce the occurrence of accidents. Among the accident factors, overspeeding led to a maximum probability of road crashes and death. As the number of vehicles increases, manually monitoring the overspeed offense is laborious and time-consuming. The existing context-aware-based speed control strategy is effective but pricey. Therefore, in this article, a cost-effective, compact, and simple design-oriented speed recommendation and control mechanism is designed to avoid accidents caused by excessive speed. The proposed system leverages the potential of the decision tree regression model to do data analysis and find out the recommended speed for a particular location at a specific time. In the implementation process, the trained regression model is stored in the Heroku web server to obtain the speed. To support the recommendation, model of a new dataset is created from the Gray Spot dataset and stored in the Storage bucket of Firebase (Open Source), which comprises the time, location, weather, and speed attributes. The vehicle user receives speed recommendations from the Heroku server for every location; consequently, the speed controller regulates the speed of the motor through a motor driver’s electrical signal. The proposed decision tree regressor achieved 92.3% prediction accuracy during processing with 30 locations. The efficiency of the model showed that this model is a good assistant to car drivers and an optimal solution to reduce accidents on a larger level.

Evolutionary game analysis of pedestrian-autonomous vehicle interactions at unsignalized road sections: a policy intervention perspective

ABSTRACT Focusing on the crossing scenarios of pedestrians and autonomous vehicles (AVs) at unsignalized road sections, this study proposes an evolutionary game model considering the related benefits of AVs, pedestrians and traffic managers, and it explores the impact of policy intervention. The game analysis results indicate that strategies of the three agents are closely associated with pedestrians’ trust in driverless technology, human-vehicle communication and government regulation. In cases where regulation exists, increasing the AV supervision threat would facilitate the equilibrium to evolve toward pedestrians crossing and AVs yielding. In addition, we unexpectedly find that the introduction of crash probability information cannot boost the equilibrium outcomes in the expected direction, which may be related to information overload. Finally, this study verifies the validity of the model through numerical simulations and provides managerial implications with respect to driverless technology promotion, communication device design and regulatory policy formulation.

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

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

Ground Versus Air: Which Mode of Emergency Medical Service Transportation Is More Likely to Crash?

ObjectiveWe analyzed helicopter emergency medical services (HEMS) and ground emergency medical services (GEMS) crash data in the United States during 1983 to 2020 to compare incidences of total, fatal, and injury crashes. MethodsHEMS and GEMS total, fatal, and injury crashes during 1983 to 2020 and 1988 to 2020, respectively, were analyzed in this retrospective study. Data were obtained from the National Transportation Safety Board and the National Highway Traffic Safety Administration. Additional data from the Federal Aviation Administration, the National Emergency Medical Services Information System, and prior literature were used for rate calculations. A Poisson regression model was used to determine rate ratios with 95% confidence intervals comparing total, fatal, and injury crash rates from 2016 to 2020. ResultsHEMS crash rates decreased since 1983. Total GEMS crashes have increased since 1988. Of the total crashes, 33% (HEMS) and 1% (GEMS) were fatal, and 20% (HEMS) and 31% (GEMS) resulted in injury. During 2016 to 2020, GEMS crash rates were 11.0 times higher than HEMS crash rates (95% confidence interval, 5.2-23.3; P < .0001). ConclusionHEMS has a lower crash probability than GEMS. The availability of data is a limitation of this study. National GEMS transportation data could be useful in studying this topic further.

Maximum likelihood method of estimating the conflict-crash relationship

The fundamental matters of how traffic conflicts are connected to crashes and how to estimate this connection with traffic conflict data is an active subject of research and refinements. There are still open questions about traffic events that can be analytically extrapolated to related crashes, and how to efficiently estimate the probability of crash associated with such events to enable conversion of observed events to the corresponding expected number of crashes. There are two important uses of a working estimation method: (1) rapid assessment of safety at specific roads locations and evaluation of countermeasures by safety engineers, (2) modeling of safety effects by analysts based on relatively short observations at multiple locations or at limited number of locations but during extended periods.This paper focuses on the application of traffic conflicts by safety engineers where the method practicality is important. The paper first recalls the OLS method of estimating the shape parameter of the underlying Lomax distribution proposed in (Tarko, 2018). Then, the ML method is introduced and the Lomax-based crash estimates obtained with the two methods are compared. Both the methods assume the scale parameter to estimate the shape parameter. The effect of assuming the scale parameter on estimates of the expected number of crashes is evaluated. To bring the scale parameter’s effect into a meaningful perspective, it is compared to two other effects: (1) type of driver, and (2) limited number of observations.Finally, re-parametrized Lomax distribution is pointed out as a potential way to address the difficulties with estimating the two distribution parameters simultaneously. The summary of the results closes the paper.

Investor heterogeneity and negative skewness in stock returns: Evidence from institutional investors

We examine the relation between the probability of future stock price crash and investors’ investment horizons. Using negative skewness as a proxy for firm-specific crash risk, we document a positive association between institutional ownership and stock price crash risk. The relation is, however, driven by short-term institutional investors, while the presence of long-term institutional investors has a negative effect on stock price crash risk. In addition, we find that the presence of short-term institutional investors induces corporate risk-taking behavior. Our results are robust to alternative model specifications, endogeneity concerns, and different measures of crash risk and proxies of investors’ horizons.

Price Efficiency, Bubbles, Crashes and Crash Risk: Evidence from Chinese Stock Market

When there is bad news hoarding from managers, returns of stocks are no longer efficient. We hypothesize that a proxy for efficient returns predicts stock price bubbles, crashes and crash risk. We find evidence in support of our hypotheses. Lagged price efficiency significantly predicts bubbles, crashes and crash risk in multivariate linear regressions and logit regressions, as predicted by our hypotheses. We also find that the lagged probability of bubbles is only correlated with future returns. In contrast, the lagged probability of crashes is correlated with both future returns and fundamental values of stocks. This result validates our explanation for the formation of bubbles and crashes. Finally, the out-of-sample accuracy ratio of our bubble and the crash prediction model is higher than in previous studies. Our results provide alternative explanations of the mechanics of stock price bubbles and crashes and are helpful for academicians, investors and policymakers.

Crash prediction for freeway work zones in real time: A comparison between Convolutional Neural Network and Binary Logistic Regression model

Safety of drivers in freeway work zones has been a problem. Real-time crash prediction helps prevent crashes before they happen. This paper looks at real-time crash prediction in freeway work zones by using machine learning approaches. Both the Convolutional Neural Network and the Binary Logistic Regression model are introduced. For training and testing the models, crash data and traffic data from several freeways in D7 zone, Los Angeles, California, were used. Crash data were collected from California Highway Patrol Incident System, and traffic data were obtained from the Caltrans Performance Measurement System. Data processing and matching were conducted. Both the two models were trained and tested. Results show that the Convolutional Neural Network performed slightly better over the Binary Logistic Regression model in predicting crashes with a global accuracy of 79.50%. Despite this, the main merit of the Binary Logistic Regression model is that it is able estimate the impact of affecting variables on the probability of crashes and can help identify the factors related to risks in work zones. Machine learning approaches applied in this study perform well in crash prediction. In general, machine learning techniques and reliable real-time crash prediction applications can be promising in helping drivers and transportation engineers make timely responses to potential crashes on freeways.

Injury severity analysis of electric bike crashes in Changsha, Hunan Province: taking different lighting conditions into consideration

Abstract With the increasing use of electric bikes, electric bike crashes occur frequently. Analysing the influencing factors of electric bike crashes is an effective way to reduce mortality and improve road safety. In this paper, spatial analysis is performed by geographic information system (GIS) to present the hot spots of electric bike crashes during daylight and darkness in Changsha, Hunan Province, China. Based on the Ordered Probit (OP) model, we studied the risk factors that led to different severities of electric bike crashes. The results show that the main influencing variables include age, illegal behaviour, collision type and road factors. During daylight and darkness, elderly electric bike riders over the age of 65 years have a higher probability of fatal crashes. Not following traffic signals and reverse driving are significantly related to the severity of riders' injuries. In darkness, frontal collisions are significant factors causing rider injury. In daylight, more serious crashes will be caused in bend and slope road segments. In darkness, roads with no physically separated bicycle lanes increases the risk of riders being injured. Electric bike crashes are mainly concentrated in the commercial, public service and residential areas in the main urban area. In suburbs at darkness, electric bike riders are more likely to be seriously injured. Adding protection measures, such as improved lighting, non-motorized lane facilities and interventions targeting illegal behaviour in the hot spot areas can effectively reduce the number of electric bike crashes in complex traffic.

Weather impacts on various types of road crashes: a quantitative analysis using generalized additive models

Adverse weather conditions can have different effects on different types of road crashes. We quantify the combined effects of traffic volume and meteorological parameters on hourly probabilities of 78 different crash types using generalized additive models. Using tensor product bases, we model non-linear relationships and combined effects of different meteorological parameters. We evaluate the increase in relative risk of different crash types in case of precipitation, sun glare and high wind speeds. The largest effect of snow is found in case of single-truck crashes, while rain has a larger effect on single-car crashes. Sun glare increases the probability of multi-car crashes, in particular at higher speed limits and in case of rear-end crashes. High wind speeds increase the probability of single-truck crashes and, for all vehicle types, the risk of crashes with objects blown on the road. A comparison of the predictive power of models with and without meteorological variables shows an improvement of scores of up to 24%, which makes the models suitable for applications in real-time traffic management or impact-based warning systems. These could be used by authorities to issue weather-dependent driving restrictions or situation-specific on-board warnings to improve road safety.

A data-driven Bayesian network for probabilistic crash risk assessment of individual driver with traffic violation and crash records

In recent years, individual drivers‘ crash risk assessments have received much attention for identifying high-risk drivers. To this end, we propose a probabilistic assessment method of crash risks with a reproducible long-term dataset (i.e., traffic violations, license, and crash records). In developing this method, we used 7.75 million violations and crashes of 5.5 million individual drivers in Seoul, South Korea, from June 2013 to June 2017 (four years). The stochastic process of the Bayesian network (BN), whose structure is optimized by tabu-search, successfully evaluates individual drivers' crash and violation probability. In addition, the cluster analysis classifies drivers into five distinctive groups according to their estimated violation and crash probabilities. As a result, this study found that the estimated average crash rate within a cluster converges with the actual crash rate by the proposed framework without privacy issues. We also confirm that violation records and expected crash probability are strongly correlated, and there is a direct relationship between a driver’s previous violations and crash record and the future at-fault crash. The proposed assessment method is valuable in developing proactive driver education programs and safety countermeasures, including adjusting the penalty system and developing user-based insurance by recognizing dangerous drivers and identifying their properties.

Injury profiles and epidemiology of single vehicle motorcycle fatalities in Tamil Nadu, India, 2009-2017

This article investigates single-vehicle motorcycle crashes and crash risk where a rider is killed or injured in Tamil Nadu, India. Hospital data linked with police-reported crash data, including demographic information, injury characteristics, and environmental and road characteristics, were analysed for all single-vehicle motorcycle crashes between January 2009 and December 2017. A total of 16,541 single-vehicle motorcycle crashes resulted in injuries (fatal, serious, and minor injury) during the period, among which 7,447 were fatal crashes. The majority of fatal crashes involved male motorcyclists aged between 26 and 64 years (5,287). Those aged 18 and 25 years (1,897) were the next most frequent group of fatally injured motorcyclists. Among the fatal injured motorcyclists, 66.6% were wearing a helmet. When reported, 33.8% of motorcyclists did not have a valid driving licence at the time of the incident. Motorcyclists injured most commonly sustained head injuries (51.3%), followed by multiple injuries (20.1%), leg injuries (3.3%), and hand injuries (3.2%). Logistic regression results suggest that specific road characteristics and environmental factors increase the probability of fatal crashes: urban roads, express highways, multiple lane roads, night time, absence of median divider, cross and T-junctions, junctions with four arms, under-aged (&lt;18 years) riders, not holding a valid licence and non-use of helmets. Appropriate countermeasures to mitigate motorcyclist trauma are recommended.

Impact of driving style, behaviour and anger on crash involvement among Iranian intercity bus drivers

In developing countries such as Iran, due to the inadequate infrastructure for rail and air transportation facilities, intercity buses are the most common type of transportation for long distances. Because of the long hours of driving, bus driving is considered a challenging job. Moreover, given the high capacity of these vehicles, a small error from the driver could endanger many passengers' health. So, studying drivers' behaviours can be a key factor in decreasing the risk factors of crash involvement in these drivers. However, few studies have focused on intercity bus drivers' behaviours. This research uses a sample of 254 professional drivers that answered a self-report questionnaire on driving style (MDSI), driving behaviour (DBQ), and driving anger (DAS). A structural equation modelling (SEM) is used to investigate the psychometric properties of these questionnaires. The results show a positive correlation between maladaptive driving styles and driving behaviour, and a negative correlation between adaptive styles and driving behaviour. Significant differences are observed among drivers with and without crash history on their maladaptive driving styles and their driving anger scale. A binary logistic regression model is also developed to predict traffic crashes as a function of driving misbehaviour. The results suggest that factors related to driving anger are the main factors that increase the probability of misbehaviour and traffic crashes. The results also suggest that driving style and driving behaviour significantly predict crash risk among bus drivers. Aggressive driving is associated with an increased probability of crash involvement among intercity bus drivers. The findings can be used to inform the health promotion policies and provide regular interventions designed to improve driving safety among intercity bus drivers.

Bitcoin and Digital Currency: Difficulties, Open Doors and Future Works

Bitcoin and other conspicuous digital forms of money definitely stand out enough to be noticed since the most recent quite a while. All around the world known as advanced coin and virtual cash, this cryptographic money is acquired and exchanged inside the blockchain framework. The blockchain innovation taken on in utilizing the digital currency has caused a stir inside the financial area, government, partners and individual financial backers. The ascent of the cryptographic money inside this long time starting from the beginning of Bitcoin in 2009 has surprised the market. Cryptographic money is expected as the future cash that would supplant the ongoing paper money around the world. Despite the fact that the interest has grabbed the eye of clients, many don't know about its chances, disadvantages and difficulties for what's to come. Explores on cryptographic forms of money are as yet missing nevertheless at its outset stage. In giving significant aid and view to the scholarly field and clients, this paper will examine the valuable open doors in digital money, for example, the security of its innovation, low exchange cost and high venture return. The inventiveness of this paper is on the conversation inside regulation and guideline, high energy utilization, probability of crash and air pocket, and goes after on network. The future endeavors of cryptographic money and its application will be methodically evaluated in this paper.

A study on the probabilistic quantification of heavy-truck crash risk under the influence of multi-factors

In order to manage and reduce a specific risk, its quantitative analysis is necessary. The key objective of this study is to illustrate the prevalence of multi-factors in fatal crashes involving heavy trucks and to quantify the crash risk under the influence of multi-factors. Data from a recent, nationally representative sample of fatal crashes was investigated to identify the risk factors contributing to crash causations and a novel risk index was obtained to develop a criterion for crash risk quantification. Then, based on the mutual information theory, the mutual dependence between risk factors was calculated to quantify the crash risk under different risk factor combinations. The results reveal that most heavy-truck fatal crashes are the result of co-occurring multi-factors rather than a single factor, and are mainly caused by simultaneous occurrence of two or three contributing factors. Moreover, crash risk increases with the increase of the number of risk factors influencing the driver. Furthermore, multi-factor interaction between certain risk factors, such as environmental and vehicular factors, makes incremental contribution to the crash risk by further increasing the crash probability. Specifically, when driver’s aberrant behaviors (errors and/or violations) are exposed to both environmental and vehicular factors, driver’s likelihood of being involved in a fatal crash increases significantly. This suggests that in addition to the number of risk factors, the crash risk also depends on the multi-factor interactions between different risk factors. Therefore, the effects of individual risk factors should be controlled at the outset to prevent the incremental effects of multi-factors on crash risk, in turn enabling risk minimization..