Prediction of Traffic Accident Severity Based on Random Forest

Yang, Jianjun; Han, Siyuan; Chen, Yimeng

doi:10.1155/2023/7641472

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…RF has found extensive use in transportation-related fields for both classification and regression tasks, such as identifying travel mode choices, predicting road traffic conditions, and estimating incident durations [16]- [18]. According to [19], factors like old age, overtaking, speeding, religious beliefs, poor braking performance, and faulty tires were identified as the primary human factors contributing to and resulting in fatalities of plants and animals in traffic accidents. Also, other studies have explored the use of data mining techniques such as association rules, clustering, and outlier detection [4].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Adefabi,

Olisah,

Obunadike

et al. 2023

IJCI

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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 States.

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Section: Literature Reviewmentioning

confidence: 99%

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

Adefabi,

Olisah,

Obunadike

et al. 2023

IJCI

View full text Add to dashboard Cite

show abstract

“…It distinguishes itself by its dynamic, data-centric approach to traffic prediction, diverging from traditional methodologies reliant on historical data and statistical models [2], [3]. The rapid advancement in data accessibility and efficient processing of extensive datasets has propelled the evolution of deep learning theories, exploring their potential in predicting urban traffic dynamics, including indicators such as speed, throughput, and accident risk [4], [5]. Recent investigations have underscored the indispensable role of deep learning in managing burgeoning vehicle volumes within intelligent transportation systems, diverging significantly from conventional machine learning models like support vector machines (SVM) and artificial [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Elevating smart city mobility using RAE-LSTM fusion for next-gen traffic prediction

Rafalia,

Moumen,

Raji

et al. 2024

IJEECS

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The burgeoning demand for efficient urban traffic management necessitates accurate prediction of traffic congestion, spotlighting the essence of time series data analysis. This paper delves into the utilization of sophisticated deep learning methodologies, particularly long short-term memory (LSTM) networks, convolutional neural networks (CNN), and their amalgamations like Conv-LSTM and bidirectional-LSTM (Bi-LSTM), to elevate the precision of traffic pattern forecasting. These techniques showcase promise in encapsulating the intricate dynamics of traffic flow, yet their efficacy hinges upon the quality of input data, emphasizing the pivotal role of data preprocessing. This study meticulously investigates diverse preprocessing techniques encompassing normalization, transformation, outlier detection, and feature engineering. Its discerning implementation significantly heightens the performance of deep learning models. By synthesizing advanced deep learning architectures with varied preprocessing methodologies, this research presents invaluable insights fostering enhanced accuracy and reliability in traffic prediction. The innovative RD-LSTM approach introduced herein harnesses the hybridization of a reverse AutoEncoder and LSTM models, marking a novel contribution to the field. The implementation of these progressive strategies within urban traffic management portends substantial enhancements in efficiency and congestion mitigation. Ultimately, these advancements pave the way for a superior urban experience, enriching the quality of life within cities through optimized traffic management systems.

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“…This dataset is then utilized to predict layout plans for unknown scenarios based on the available scenario data. In this paper, five machine learning algorithms are introduced, namely the decision tree algorithm [23], random forest algorithm [24], backpropagation neural network algorithm [25], support vector regression algorithm [26], and linear regression neural network algorithm [27]. The fitting effects of these algorithms are compared, and the algorithm with better performance is selected for prediction.…”

Section: Introductionmentioning

confidence: 99%

Optimal Layout Method for Roadside LiDAR and Camera

Li,

Zhang,

Cheng

et al. 2024

IEEE Access

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With the widespread application of LiDAR and camera, the integration of LiDAR and camera has become an urgent issue. In this study, we proposed a optimal layout method for roadside LiDAR and camera. Firstly, the experimental design phase took into consideration various application scenarios, such as curved road sections and gradient road sections. Secondly, the video data and point cloud data collected from different experimental setups were subjected to object detection and recognition using YOLOv5s weights and PointPillars weights, respectively. These weights are applied to the video data under different layout schemes to output the mAP value of each scheme. By comparing the mAP values under different layout schemes, the optimal layout scheme for the road scene is determined. Thirdly, the four road scene parameters and six installation parameters from all scenarios were collected into a database. Furthermore, five machine learning algorithms were employed for optimal selection. Finally, the three regression algorithms with the highest accuracy are selected for the final prediction model based on different control groups. Through the field experiment, the results show that the optimized layout method can significantly reduce blind spot detection and vehicle occlusion problems for camera and LiDAR. The optimal deployment method can increase the Mean Average Precision (MAP) by over 4% through adjusting the installation parameters. The regression algorithm used can predict the optimal deployment scheme for roadside LiDAR and cameras in unknown scenarios with over 95% accuracy. This optimal deployment method improves the detection accuracy of roadside devices for road vehicles by changing the installation parameters and provides guidance for the future installation of roadside LiDAR and camera.

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Prediction of Traffic Accident Severity Based on Random Forest

Cited by 8 publications

References 28 publications

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

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

Elevating smart city mobility using RAE-LSTM fusion for next-gen traffic prediction

Optimal Layout Method for Roadside LiDAR and Camera

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