Predicting Accident Severity: An Analysis Of Factors Affecting Accident Severity Using Random Forest Model

Road accidents have significant economic and societal costs, with a small number of severe accidents accounting for a large portion of these costs. Predicting accident severity can help in the proactive approach to road safety by identifying potential unsafe road conditions and taking well-informed actions to reduce the number of severe accidents. This study investigates the effectiveness of the Random Forest machine learning algorithm for predicting the severity of an accident. The model is trained on a dataset of accident records from a large metropolitan area and evaluated using various metrics. Hyperparameters and feature selection are optimized to improve the model's performance. The results show that the Random Forest model is an effective tool for predicting accident severity with an accuracy of over 80%. The study also identifies the top six most important variables in the model, which include wind speed, pressure, humidity, visibility, clear conditions, and cloud cover. The fitted model has an Area Under the Curve of 80%, a recall of 79.2%, a precision of 97.1%, and an F1 score of 87.3%. These results suggest that the proposed model has higher performance in explaining the target variable, which is the accident severity class. Overall, the study provides evidence that the Random Forest model is a viable and reliable tool for predicting accident severity and can be used to help reduce the number of fatalities and injuries due to road accidents in the United StatesComment: 15 page

Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting

Predicting traffic incident duration is a major challenge for many traffic centres around the world. Most research studies focus on predicting the incident duration on motorways rather than arterial roads, due to a high network complexity and lack of data. In this paper we propose a bi-level framework for predicting the accident duration on arterial road networks in Sydney, based on operational requirements of incident clearance target which is less than 45 minutes. Using incident baseline information, we first deploy a classification method using various ensemble tree models in order to predict whether a new incident will be cleared in less than 45min or not. If the incident was classified as short-term, then various regression models are developed for predicting the actual incident duration in minutes by incorporating various traffic flow features. After outlier removal and intensive model hyper-parameter tuning through randomized search and cross-validation, we show that the extreme gradient boost approach outperformed all models, including the gradient-boosted decision-trees by almost 53%. Finally, we perform a feature importance evaluation for incident duration prediction and show that the best prediction results are obtained when leveraging the real-time traffic flow in vicinity road sections to the reported accident location

A Comparative Analysis of Machine Learning Methods for Lane Change Intention Recognition Using Vehicle Trajectory Data

Accurately detecting and predicting lane change (LC)processes can help autonomous vehicles better understand their surrounding environment, recognize potential safety hazards, and improve traffic safety. This paper focuses on LC processes and compares different machine learning methods' performance to recognize LC intention from high-dimensionality time series data. To validate the performance of the proposed models, a total number of 1023 vehicle trajectories is extracted from the CitySim dataset. For LC intention recognition issues, the results indicate that with ninety-eight percent of classification accuracy, ensemble methods reduce the impact of Type II and Type III classification errors. Without sacrificing recognition accuracy, the LightGBM demonstrates a sixfold improvement in model training efficiency than the XGBoost algorithm.Comment: arXiv admin note: text overlap with arXiv:2304.1373

A CNN-LSTM-based Deep Learning Approach for Driver Drowsiness Prediction

Abstract: The development of neural networks and machine learning techniques has recently been the cornerstone for many applications of artificial intelligence. These applications are now found in practically all aspects of our daily life. Predicting drowsiness is one of the most particularly valuable of artificial intelligence for reducing the rate of traffic accidents. According to earlier studies, drowsy driving is at responsible for 25 to 50% of all traffic accidents, which account for 1,200 deaths and 76,000 injuries annually. The goal of this research is to diminish car accidents caused by drowsy drivers. This research tests a number of popular deep learning-based models and presents a novel deep learning-based model for predicting driver drowsiness using a combination of convolutional neural networks (CNN) and Long-Short-Term Memory (LSTM) to achieve results that are superior to those of state-of-the-art methods. Utilizing convolutional layers, CNN has excellent feature extraction abilities, whereas LSTM can learn sequential dependencies. The National Tsing Hua University (NTHU) driver drowsiness dataset is used to test the model and compare it to several other current models as well as state-of-the-art models. The proposed model outperformed state-of-the-art models, with results up to 98.30% for training accuracy and 97.31% for validation accuracy

City Indicators for Geographical Transfer Learning: An Application to Crash Prediction

The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we tackle the problem of predicting the crash risk of a car driver in the long term. This is a very challenging task, requiring a deep knowledge of both the driver and their surroundings, yet it has several useful applications to public safety (e.g. by coaching high-risk drivers) and the insurance market (e.g. by adapting pricing to risk). We model each user with a data-driven approach based on a network representation of users’ mobility. In addition, we represent the areas in which users moves through the definition of a wide set of city indicators that capture different aspects of the city. These indicators are based on human mobility and are automatically computed from a set of different data sources, including mobility traces and road networks. Through these city indicators we develop a geographical transfer learning approach for the crash risk task such that we can build effective predictive models for another area where labeled data is not available. Empirical results over real datasets show the superiority of our solution

A CNN-LSTM-based Deep Learning Approach for Driver Drowsiness Prediction

Abstract: The development of neural networks and machine learning techniques has recently been the cornerstone for many applications of artificial intelligence. These applications are now found in practically all aspects of our daily life. Predicting drowsiness is one of the most particularly valuable of artificial intelligence for reducing the rate of traffic accidents. According to earlier studies, drowsy driving is at responsible for 25 to 50% of all traffic accidents, which account for 1,200 deaths and 76,000 injuries annually. The goal of this research is to diminish car accidents caused by drowsy drivers. This research tests a number of popular deep learning-based models and presents a novel deep learning-based model for predicting driver drowsiness using a combination of convolutional neural networks (CNN) and Long-Short-Term Memory (LSTM) to achieve results that are superior to those of state-of-the-art methods. Utilizing convolutional layers, CNN has excellent feature extraction abilities, whereas LSTM can learn sequential dependencies. The National Tsing Hua University (NTHU) driver drowsiness dataset is used to test the model and compare it to several other current models as well as state-of-the-art models. The proposed model outperformed state-of-the-art models, with results up to 98.30% for training accuracy and 97.31% for validation accuracy