An Ensemble based Machine Learning Approach for Traffic Prediction in Smart City

Jenifer, J; Priyadarsini, R. Jemima

doi:10.1109/icaeca52838.2021.9675631

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…The way to cover a single ML model's problem to be more beneficial and powerful is to combine different ML models, which is called EL [11]. Previous related works [15]- [29] presented different ML, DL, EL and optimized approaches to enhance the performance of their model for traffic prediction on a road or in other networks or other domains. Most of them focus on improving accuracy.…”

Section: Background and Related Workmentioning

confidence: 99%

“…EL methods are applicable for minimizing bias, enhancing predictions and being robust to overfitting [27]. EL methods can achieve better results than standalone ML models in traffic prediction [29]. However, time consumption is one of their main problems [27].…”

Section: Background and Related Workmentioning

confidence: 99%

“…The first layer is constructed from selective ML algorithms, which are called base learners. The combination of the base learners is based on a comparative performance analysis of the most popular ML models used in previous studies [15]- [29] for traffic prediction. The second layer considers one algorithm called the meta learner and is responsible for the final prediction of the whole model.…”

Section: B Overview Of the Proposed Stk-ebm Model Architecturementioning

confidence: 99%

“…We stacked a set of high-performance heterogeneous base learners in the first layer. These base learners were selected according to the performance analysis of the most popular ML prediction models [15]- [29]. In this way, we reduced the complexity of stacking the useless and unsuitable models.…”

Section: ) Stacking Heterogeneous Ensemble Model Structurementioning

confidence: 99%

See 3 more Smart Citations

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

Amiri¹,

Pierre²

2023

IEEE Access

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Vehicular Ad-hoc Networks (VANETs), as the most significant element of the Intelligent Transportation Systems (ITS), have the potential to enhance traffic efficiency and road safety by making the transportation system smarter and are still at the initial point of development. In this paper, we propose an ensemble-based machine learning model for network traffic prediction in VANET. We take advantage of Ensemble Learning (EL), which combines different Machine Learning (ML) models to achieve better performance and improve accuracy. We consider the most informative attributes of the VANET dataset using Boruta and LightGBM as ensemble feature selection methods. Our proposed model is based on Stacking Ensemble Learning with Booster Model (STK-EBM) designed with a stacking ensemble of heterogeneous ML models. The framework of the proposed model consists of two layers, including a base layer and a meta layer. The first layer integrates Random Forest (RF), K-Nearest Neighbor (KNN) and XGBoost as a booster of the base learners. An optimized Logistic Regression (LR) employs as our meta learner in the second layer. We evaluate the performance of our model considering classification metrics and then compare it with the most popular traffic predictive models. Simulation results show that the STK-EBM model gives a more stable prediction than the single algorithm, as well as better overall performance in terms of prediction accuracy and execution time.

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Section: Background and Related Workmentioning

confidence: 99%