2,277 research outputs found

    Hotspot Location Identification Using Accident Data, Traffic and Geometric Characteristics

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    Determining the criterion for critical limits is always one of the essential challenges for traffic safety authorities. The purpose of identifying accident hotspots is to achieve high-priority locations in order to effectively allocate the safety budgets as well as to promote more efficient and faster safety at the road network level. In recent years, human, vehicle, road and environment have been recognized as the three main effective elements of the road transportation in the occurrence of accidents. In the present study, with combining the parameters related to accidents, geometric parameters of the accident location and traffic parameters, hotspots were identified by using the superior methods of Poisson regression and negative binomial distribution and based on the combined criteria of frequency and severity of accidents and equivalent damage factors. Then using Time Series Models in ANN, result were compared and validated. The results of ANN models demonstrate that the frequency method of accidents tends toward places with high traffic volume. MATLAB and STATA software were used. Non-native plumbing, curvature, slope, section length and residential area had more significance, and their coefficients indicated the significant effect of these parameters on the occurrence of the frequency and severity of accidents in hotspot locations

    Developing Multi Linear Regression Models for Estimation of Marshall Stability

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    Nowadays, asphalt roads are exposed to increasing traffic loads in recent times. It is important to obtain a quality and healthy asphalt road covering when considering the conditions of our country where freight and passenger transportation are carried out by roads. One of the most important issues in asphalt road design is the determination of the optimum percentage of bitumen. The Marshall stability test is utilized for optimum percent bitumen determination. In our work, instead of the long and laborious Marshall experiment process, Multi Linear Regression (MLR) Models are developed as an alternative. Models were developed for Marshall experiment result for Marshall stability prediction. In order to construct stability estimation models, pre-made test parameters are used. These parameters are; the bitumen penetration (P),weight of the sample in the weather (H), the temperature (C), the bitumen weight (G), the sample heights (Y), the bitumen percentage (W), weight of the sample in water (S), the stability (ST). In the performance evaluation of the models, the correlation coefficient (R), the mean percentage errors (MPE) and the meansquare errors (MSE) are used. It is seen that the model with the highest performance value is composed of six variable model in this study formed by the MLR. The R value of the best model is 0.571.The MSE value of the best model is 14841,81. The MPE value of the best model is 9.58

    Accident prediction using machine learning:analyzing weather conditions, and model performance

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    Abstract. The primary focus of this study was to investigate the impact of weather and road conditions on the severity of accidents and to determine the feasibility of machine learning models in accurately predicting the likelihood of such incidents. The research was centered on two key research questions. Firstly, the study examined the influence of weather and road conditions on accident severity and identified the most related factors contributing to accidents. We utilized an open-source accident dataset, which was preprocessed using techniques like variable selection, missing data elimination, and data balancing through the Synthetic Minority Over-sampling Technique (SMOTE). Chi-square statistical analysis was performed, suggesting that all weather-related variables are more or less associated with the severity of accidents. Visibility and temperature were found to be the most critical factors affecting the severity of road accidents. Hence, appropriate measures such as implementing effective fog dispersal systems, heatwave alerts, or improved road maintenance during extreme temperatures could help reduce accident severity. Secondly, the research evaluated the ability of machine learning models including decision trees, random forests, naive bayes, extreme gradient boost, and neural networks to predict accident likelihood. The models’ performance was gauged using metrics like accuracy, precision, recall, and F1 score. The Random Forest model emerged as the most reliable and accurate model for predicting accidents, with an overall accuracy of 98.53%. The Decision Tree model also showed high overall accuracy (95.33%), indicating its reliability. However, the Naive Bayes model showed the lowest accuracy (63.31%) and was deemed less reliable in this context. It is concluded that machine learning models can be effectively used to predict the likelihood of accidents, with models like Random Forest and Decision Tree proving the most effective. However, the effectiveness of each model may vary depending on the dataset and context, necessitating further testing and validation for real-world implementation. These findings not only provide insight into the factors affecting accident severity but also open a promising avenue in employing machine learning techniques for proactive accident prediction and mitigation. Future studies can aim to refine the models further and potentially integrate them into traffic management systems to enhance road safety

    Forecasting the Accident Frequency and Risk Factors: A Case Study of Erzurum, Turkey

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    Nowadays, life is intimately associated with transportation, generating several issues on it. Numerous works are available concerning accident prediction techniques depending on independent road and traffic features, while the mix parameters including time, geometry, traffic flow, and weather conditions are still rarely ever taken into consideration. This study aims to predict future accident frequency and the risk factors of traffic accidents. It utilizes the Generalized Linear Model (GLM) and Artificial Neural Networks (ANN) approaches to process and predict traffic data efficiently based on 21500 records of traffic accidents that occurred in Erzurum in Turkey from 2005 to 2019. The results of the comparative evaluation demonstrated that the ANN model outperformed the GLM model. The study revealed that the most effective variable was the number of horizontal curves. The annual average growth rates of accident occurrences based on the ANNꞌs method are predicted to be 11.22% until 2030

    Analysis of vehicle pedestrian crash severity using advanced machine learning techniques

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    In 2015, over 17% of pedestrians were killed during vehicle crashes in Hong Kong while it raised to 18% from 2017 to 2019 and expected to be 25% in the upcoming decade. In Hong Kong, buses and the metro are used for 89% of trips, and walking has traditionally been the primary way to use public transportation. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. Most studies on crash severity ignored the severity correlations between pedestrian-vehicle units engaged in the same impacts. The estimates of the factor effects will be skewed in models that do not consider these within-crash correlations. Pedestrians made up 17% of the 20,381 traffic fatalities in which 66% of the fatalities on the highways were pedestrians. The motivation of this study is to examine the elements that pedestrian injuries on highways and build on safety for these endangered users. A traditional statistical model's ability to handle misfits, missing or noisy data, and strict presumptions has been questioned. The reasons for pedestrian injuries are typically explained using these models. To overcome these constraints, this study used a sophisticated machine learning technique called a Bayesian neural network (BNN), which combines the benefits of neural networks and Bayesian theory. The best construction model out of several constructed models was finally selected. It was discovered that the BNN model outperformed other machine learning techniques like K-Nearest Neighbors, a conventional neural network (NN), and a random forest (RF) model in terms of performance and predictions. The study also discovered that the time and circumstances of the accident and meteorological features were critical and significantly enhanced model performance when incorporated as input. To minimize the number of pedestrian fatalities due to traffic accidents, this research anticipates employing machine learning (ML) techniques. Besides, this study sets the framework for applying machine learning techniques to reduce the number of pedestrian fatalities brought on by auto accidents

    Estimation of Road Accident Risk with Machine Learning

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    Road accidents are an important issue for our societies, responsible for millions of deaths and injuries every year representing a very high cost for society. In this thesis, we evaluate how machine learning can be used to estimate the risk of accidents in order to help address this issue. Previous studies have shown that machine learning can be used to identify the times and areas of a road network with increased risk of road accidents using road characteristics, weather statistics, and date-based features. In the first part of this thesis, we evaluate whether more precise models estimating the risk for smaller areas can still reach interesting performances. We assemble several public datasets and build a relatively accurate model estimating the risk of accidents within an hour on a road segment defined by intersections. In the second part, we evaluate whether data collected by vehicle sensors during driving can be used to estimate the risk of accidents of a driver. We explore two different approaches. With the first approach, we extract features from the time series and attempt to estimate the risk based on these features using classical algorithms. With the second approach, we design a neural network directly using the time series data to estimate the risk. After extensively tuning our models, we managed to reach encouraging performances on the validation set, however, the performances of our two models on the test set were disappointing. This led us to believe that this task might not be feasible, at least with the dataset used

    Work Zone Safety Analysis, Investigating Benefits from Accelerated Bridge Construction (ABC) on Roadway Safety

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    The attributes of work zones have significant impacts on the risk of crash occurrence. Therefore, identifying the factors associated with crash severity and frequency in work zone locations is of important value to roadway safety. In addition, the significant loss of workers’ lives and injuries resulting from work zone crashes indicates the emergent need for a comprehensive and in-depth investigation of work zone crash mechanisms. The cost of work zone crashes is another issue that should be taken into account as work zone crashes impose millions of dollars on society each year. Applying innovative construction methods like Accelerated Bridge Construction (ABC) dramatically decreases on-site construction duration and thus improves roadway safety. This safe and cost-effective procedure for building new bridges or replacing/rehabilitating existing bridges in just a few weeks instead of months or years may prevent crashes and avoid injuries as a result of work zone presence. The application of machine learning techniques in traffic safety studies has seen explosive growth in recent years. Compared to statistical methods, MLs are more accurate prediction models due to their ability to deal with more complex functions. To this end, this study focuses on three major areas: crash severity at construction work zones with worker presence, crash frequency at bridge locations, and assessment of the associated costs to calculate the contribution of safety to the benefit-cost ratio of ABC as compared to conventional methods. Some key findings of this study can be highlighted as in-depth investigation of contributing factors in conjunction with the results from statistical and machine learning models, which can provide a more comprehensive interpretation of crash severity/frequency outcomes. The demonstration of work zone crashes needs to be modeled separately by time of day for severity analysis with a high level of confidence. Investigation of the contributing factors revealed the nonlinear relationship between crash severity/frequency and contributing factors. Finally, the results showed that the safety benefits from a case study in Florida consisted of 43% of the total ABC implementation cost. This indicates that the safety benefits of ABC implementation consist of a considerable portion of its benefit-cost ratio

    Safety Hazard and Risk Identification and Management In Infrastructure Management

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    Infrastructure such as transportation networks improves the condition of everyday lives by facilitating public services and systems necessary for economic activity and growth. However, constructing and maintaining transportation infrastructure poses safety hazards and risks to those working at the sharp end, leading to serious injuries and fatalities. Therefore, the identification of hazards and managing the risks they create is integral towards continually improving safety levels in Infrastructure Management. This work seeks to fully understand this problem and highlight past, present and future issues concerning safety in a comprehensive literature review. A decision support tool is proposed to improve the safety of transportation workers by facilitating hazard identification and management of associated control measures. This Tool facilitates the extraction of safety knowledge from real paper-based safety documents, capturing existing worker’s knowledge and experiences from industrial ‘corporate memory’. The Tool suggests the most appropriate control measures for new scenarios based on existing knowledge from previous work tasks. This is achieved by classifying work tasks using a new method based on unilateral UK legislation (Reporting of Injuries, Diseases and Dangerous Occurrences (1995) Regulations) and the innovative use of Artificial Intelligence method Case Based Reasoning. Case Based Reasoning (CBR) allows transparency in the Tool processes and has many benefits over other safety tools which may suffer from ‘black box’ stigmatism. The Tool is populated with knowledge extracted from a real transportation project and is hosted via the internet (www.Total-Safety.com). The end product of the Tool is the generation of bespoke method statements detailing appropriate control measures. These generated paper documents are shown to have financial and quality control benefits over traditional method statements. The Tool has undergone testing and analysis and is shown to be robust. Finally, the overall conclusions and opportunities for further research are presented and progress of the work against each of the five research objectives is assessed
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