Explore the vast range of titles available.

Identifying critical road sections that require prompt attention is essential for road agencies to prioritize monitoring, maintenance, and rehabilitation efforts and improve overall road conditions and safety. This study suggests a decision matrix with a hierarchical structure that factors in the pavement deterioration rate, infrastructure safety, and crash history to identify these sections. A Markov mixed hazard model was used to assess each section’s deterioration rate. The safety of the road sections was rated with the International Road Assessment Program star rating protocol considering all road users. Early detection of sections with fast deterioration and poor safety conditions allows for preventive measures to be taken and to reduce further deterioration and traffic crashes. Additionally, including crash history data in the decision matrix helps to understand the possible causes of a crash and is useful in developing safety policies. The proposed method is demonstrated using data from 4725 road sections, each 100 m, in Addis Ababa, Ethiopia. The case study results show that the proposed decision matrix can effectively identify critical road sections which need close attention and immediate action. As a result, the proposed method can assist road agencies in prioritizing inspections, maintenance, and rehabilitation decisions and effectively allocate budgets and resources.

To create a safe bicycle infrastructure system, this article develops an intelligent embedded learning system using a combination of deep neural networks. The learning system is used as a case study in the Northumbria region in England’s northeast. It is made up of three components: (a) input data unit, (b) knowledge processing unit, and (c) output unit. It is demonstrated that various infrastructure characteristics influence bikers’ safe interactions, which is used to estimate the riskiest age and gender rider groups. Two accurate prediction models are built, with a male accuracy of 88 per cent and a female accuracy of 95 per cent. The findings concluded that different infrastructures pose varying levels of risk to users of different ages and genders. Certain aspects of the infrastructure are hazardous to all bikers. However, the cyclist’s characteristics determine the level of risk that any infrastructure feature presents. Following validation, the built learning system is interoperable under various scenarios, including current heterogeneous and future semi-autonomous and autonomous transportation systems. The results contribute towards understanding the risk variation of various infrastructure types. The study’s findings will help to improve safety and lead to the construction of a sustainable integrated cycling transportation system.

This paper presents the results of research conducted to develop an automated system capable of determining parameters for horizontal curves. The system presented in this article could calculate the actual course of a road by means of a two-stage positioning of recorded points along the road. In the first stage, measurements were taken with a Real-Time Network (RTN) receiver installed in a research vehicle. In the second stage, pictures from three cameras, also installed in the vehicle, were analyzed in order to correct the accuracy of the location of the measurement points along the road. The RTN messages and the pictures from the cameras were sent to a mobile workstation which integrated the received signals in an ArcGIS (Esri) environment. The system provides a way to quickly accumulate highly accurate data on the actual geometric parameters of a road. The computer scripts developed by the authors on the basis of the acquired data could automatically determine the parameters of the horizontal curves. The solution was tested in the field and some comments on its advantages and disadvantages are presented in this paper. The automation of data acquisition with regards to the run of a road provides effective data input for mathematical models that include the effect of horizontal curve parameters on road safety. These could be used to implement more effective ways of improving road safety.

In Poland, road inspections were implemented in June 2014 on all national roads. Previous traffic surveys mainly looked at the technical condition of roads, signs and markings; other safety issues were overlooked. The main problem of the inspections is that the qualitative assessment is subjective which affects the classification of the sources of hazard on the road. The paper presents an analysis of the variability of the qualitative assessments of road defects when they are assessed by different teams of inspectors. On this basis, guidelines were developed for the classification of risks based on the relationship between sources of road hazard and the personal and economic losses involved in road accidents. These relationships are quantified using mathematical models to simulate the effect of hazard variability on the consequences of selected road accident causes on sections of the road network.

This paper proposes that road safety investment can be optimised by the development of a road safety management model. Road safety strategies typically include a basket of engineering, enforcement and education/training measures but there does not appear to be any management model which permits the optimisation of road safety investment. The proposed model utilises linear programming to predict changes in road safety resulting from safety interventions. It is mainly based on research in the areas of engineering and enforcement since there is little published research on the correlation between education and accident reduction. The model output provides the accident reduction and associated costs resulting from feasible road safety strategies. This should benefit policy makers when allocating resources.

In the field of freeway traffic safety research, there is an increasing focus in studies on how to reduce the frequency and severity of traffic crashes. Although many studies divide factors into “human-vehicle-road-environment” and other dimensions to construct models whichshowthe characteristic patterns of each factor's influence on crash severity, there is still a lack of research on the interaction effect of road and environment characteristics on the severity of a freeway traffic crash. This research aims to explore the influence of road and environmental factors on the severity of a freeway traffic crash and establish a prediction model towards freeway traffic crash severity. Firstly, the obtained historical traffic crash data variables were screened, and 11 influencing factors were summarized from the perspective of road and environment, and the related variables were discretized. Furthermore, the XGBoost (eXtreme Gradient Boosting) model was established, and the SHAP (SHapley Additive exPlanation) value was introduced to interpret the XGBoost model; the importance ranking of the influence degree of each feature towards the target variables and the visualization of the global influence of each feature towards the target variables were both obtained. Then, the Bayesian network-based freeway traffic crash severity prediction model was constructed via the selected variables and their values, and the learning and prediction accuracy of the model were verified. Finally, based on the data of the case study, the prediction model was applied to predict the crash severity considering the interaction effect of various factors in road and environment dimensions. The results show that the characteristic variables of road side protection facility type (RSP), road section type (LAN), central isolation facility (CIF), lighting condition (LIG), and crash occurrence time (TIM) have significant effects on the traffic crash prediction model; the prediction performance of the model considering the interaction of road and environment is better than that of the model considering the influence of single condition; the prediction accuracy of XGBoost-Bayesian Network Model proposed in this research can reach 89.05%. The identification and prediction of traffic crash risk is a prerequisite for safety improvement, and the model proposed and results obtained in this research can provide a theoretical basis for related departments in freeway safety management.

People who drive as part of their work, as opposed to commuting to and from a workplace, are subject to the same accident risk factors as all road users. Yet, given their increased exposure to road environments, they are significantly more likely to be involved in a collision. As the number of workers who are required to drive for work is increasing, so are the road network demands, making work related road risk (WRRR) a critical area for injury prevention and occupational safety research. The aim of this research is to understand drivers’ perspectives on road safety and gather insights into self-reported driving behaviour that can inform proactive safety strategies. Online questionnaires were conducted to collect quantitative data with a sample size of 111 respondents. The respondents were all people who drive as part of their work in England, excluding commuting. The questionnaire focused on examining drivers’ self-reported safety behaviours, workplace conditions, and policies, such as compulsory training, vehicle roadworthiness, break regulations, and satisfaction with safety measures. Preliminary descriptive statistics revealed that only 53% of respondents identify themselves as professional drivers. This finding suggests a gap in the drivers’ perception and this lack of recognition that may contribute to lower engagement with safety practices and regulations. Ongoing statistical analysis aims to identify patterns, factors that affect certain behaviours and targeted interventions. The findings of this project will showcase statistical models that reveal relationships between safety-related factors with the aim to support evidence-based countermeasures, that enhance preparedness, promote a culture of safety, and reduce work-related road collisions and injuries.

Egypt's National Road Project is a large infrastructure project which presently aims to upgrade 2500 kilometers of road networks as well as construct 4000 kilometers of new roads to meet today's need. This leads to an increase in the number of work zones on highways and therefore a rise in hazardous traffic conditions. This is why highways agencies are shifting towards night construction in order to reduce the adverse traffic impacts on the public. Although many studies have investigated work zone crashes, only a few studies provide comparative analysis of the difference between nighttime and daytime work zone crashes. Data from Egyptian long-term highway work zone projects between 2010 and 2016 are studied with respect to the difference in injury severity between nighttime and daytime crashes by using separate mixed logit models. The results indicate that significant differences exist between factors contributing to injury severity. Four variables are found significant only in the nighttime model and four other variables significant in the daytime model. The results show that older and male drivers, the number of lane closures, sidewise crashes, and rainy weather have opposite effects on injury severity in nighttime and daytime crashes. The findings presented in this paper could serve as an aid for transportation agencies in development of efficient measures to improve safety in work zones.

Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers' performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California's Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles.

Background Road traffic crashes represent a major public health concern, so it is of significant importance to understand the factors associated with the increase in injury severity of its interveners when involved in a road crash. Determining such factors is essential to help decision-making in road safety management, improving road safety, and reducing the severity of future crashes. Artificial Intelligence is probably the most important topic of research nowadays. Machine learning is a very prominent and successful approach in modern AI and is used in the work to predict crash injury severity. Methods For this study, all police-reported road accidents involving at least one injured passenger car occupant that occurred in Portugal over twelve years, from 2010 to 2022, were considered. The information present in the data (a total of 117 variables) can be divided into 3 parts: accident information (50 variables), occupants’ information (43 variables), and a third part related to the other driver involved (24 variables), when this exists. The size of the crash database is very large containing about 6000 fatalities, 20000 severe injuries and 600000 slight injuries. A full exploratory analysis of the data was performed for all variables in the dataset. To begin with, the exploratory analysis was performed on the original dataset. Afterwards, the continuous variables were discretized, and considering the previous exploration analysis, some classes were grouped within the different variables. A new exploration analysis was performed on this new discretized and regrouped dataset. Results Different machine learning methods are investigated and discussed in this study, namely Decision Tree (DT), Ordered Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) . To compare the models’ performance the following metrics were used: sensitivity, specificity, precision. Key messages • AI with machine learning methods provides a suitable framework to build a proper predictive model, allowing researchers and practitioners to evaluate more accurately the risk factors of crashes. • Car accidents account for the majority of road victims in the world, and tools that can predict injuries are very important for taking effective prevention measures. Topic Injury prediction, Machine Learning, Traffic crashes.