An Ensemble-Based Machine Learning Model for Forecasting Network Traffic in VANET

Amiri, Parvin Ahmadi Doval; Pierre, Samuel

doi:10.1109/access.2023.3253625

Cited by 13 publications

(6 citation statements)

References 49 publications

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“…Our proposed system utilizes in-vehicle data, aligning with various studies that have explored the integration of machine learning (ML) applications with such data. For instance, research [17,18] employed SVM and neural network algorithms to identify unsafe driving behavior using in-vehicle sensor data such as vehicle speed, engine speed, and brake pedal pressure, achieving over 90% accuracy with both classifiers. Another study [19,20] developed a model to identify dangerous driving events using random forests and recurrent neural networks, analyzing data such as acceleration and engine RPM.…”

Section: Related Workmentioning

confidence: 99%

Improving Driving Style in Connected Vehicles via Predicting Road Surface, Traffic, and Driving Style

Jawad,

Nitulescu

2024

Applied Sciences

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This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study’s central methodology is the voting classifier ensemble method, which integrates predictions from multiple machine learning models to improve overall predictive performance. Specifically, the ensemble method combines insights from random forest, decision tree, and K-nearest neighbors models, leveraging their individual strengths while compensating for their weaknesses. This approach resulted in high accuracy rates of 94.67% for driving style, 99.10% for road surface, and 98.80% for traffic predictions, demonstrating the robustness of the ensemble technique. Additionally, our research emphasizes the importance of model explanation ability, employing the tree interpreter tool to provide detailed insights into how different features influence predictions. This paper proposes a model based on the algorithm GLOSA for sharing data between connected vehicles and the algorithm CTCRA for sending road information to navigation application users. Based on prediction results using ensemble learning and similarity in driving styles, road surface conditions, and traffic conditions, an ensemble learning approach is used. This not only contributes to the predictions’ transparency and trustworthiness but also highlights the practical implications of ensemble learning in improving real-time decision-making and vehicle safety in intelligent transportation systems. The findings underscore the significant potential of advanced ensemble methods for addressing complex challenges in vehicular data analysis.

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Section: Related Workmentioning

confidence: 99%

Improving Driving Style in Connected Vehicles via Predicting Road Surface, Traffic, and Driving Style

Jawad,

Nitulescu

2024

Applied Sciences

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“…Our main objective in this study is to develop and evaluate a crash prediction model that can predict road traffic collisions and their patterns. We perform accident analysis by applying a two-layer ensemble stacking method using logistic regression as a metaclassifier, and the four most popular supervised machine learning algorithms (NB, k-NN, DT, and AdaBoost) because of their proven accuracy in this field [12][13][14]. Datasets for this study were acquired from a fixed-base driving simulator [15].…”

Section: Introductionmentioning

confidence: 99%

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm

Oyoo,

Wekesa,

Ogada

2024

ASI

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Road traffic collisions are among the world’s critical issues, causing many casualties, deaths, and economic losses, with a disproportionate burden falling on developing countries. Existing research has been conducted to analyze this situation using different approaches and techniques at different stretches and intersections. In this paper, we propose a two-layer ensemble machine learning (ML) technique to assess and predict road traffic collisions using data from a driving simulator. The first (base) layer integrates supervised learning techniques, namely k- Nearest Neighbors (k-NN), AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method, using logistic regression as a meta-classifier. In addition, the synthetic minority oversampling technique (SMOTE) was performed to handle the data imbalance before training the model. To simplify the model, the particle swarm optimization (PSO) algorithm was used to select the most important features in our dataset. The proposed two-layer ensemble model had the best outcomes with an accuracy of 88%, an F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost. The proposed two-layer ensemble model can be used in the future for theoretical as well as practical applications, such as road safety management for improving existing conditions of the road network and formulating traffic safety policies based on evidence.

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“…In order to improve the accuracy and real-time performance of the prediction results, this paper uses the construction of an adaptive network framework combined with ensemble learning as the model of driving behavior prediction LightGBM and NN was employed as feature selection methods to construct the most informative features from the extracted dataset [4]. The key contributions of this study are as follows:…”

Section: Introductionmentioning

confidence: 99%

Vehicle Driving Behavior Prediction Under Different Weather Conditions Based on the Adaptive Network

Qian,

Gu,

Cheng

et al. 2024

Advances in Transdisciplinary Engineering

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Driving behavior is typically influenced to some extent by weather conditions, which is one of the important factors for the identification of driving behavior of adjacent vehicles by drivers during road travel. In this study, LightGBM and NN were employed as ensemble feature selection methods to consider the attributes with the highest information content in the driving behavior dataset. A stacked ensemble design of machine learning models was proposed under the framework of adaptive networks. The framework of the proposed model in this study consists of two layers, including the adaptive layer and the stacked layer. The performance of the model was evaluated based on classification metrics and compared with the most popular predictive models. The experimental results show that the prediction of the ensemble learning model under the adaptive framework is more stable and shows better comprehensive performance. It can achieve high accuracy and reliability in identifying driver’s driving behavior.

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An Ensemble-Based Machine Learning Model for Forecasting Network Traffic in VANET

Cited by 13 publications

References 49 publications

Improving Driving Style in Connected Vehicles via Predicting Road Surface, Traffic, and Driving Style

Improving Driving Style in Connected Vehicles via Predicting Road Surface, Traffic, and Driving Style

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm

Vehicle Driving Behavior Prediction Under Different Weather Conditions Based on the Adaptive Network

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