An Ensemble Cascading Extremely Randomized Trees Framework for Short-Term Traffic Flow Prediction

Abstract

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

“…Bai et al utilized the Extra Trees algorithm to predict short-term traffic flow in a non-stationary environment. The obtained result showed higher precision compared to existing methods [133]. To the best of our knowledge, this algorithm has not been proposed before in the context of photovoltaic fault diagnosis.…”

“…Additionally, the Extra Trees algorithm exhibits high precision, lower computational complexity, and variance compared to other models such as decision trees, support vector machines, artificial neural network (ANN), Random Forest, and decision trees [40]. This is an opportunity to propose the Extra Trees model as an effective algorithm that addresses the inadequacies of other models, such as decision trees, the AdaBoost model, SVM, DT, KNN, and FKNN [133]. The execution algorithm for the Extra Trees model is presented in Figure 19.…”

An Ontology Based Test Case Prioritization Approach in Regression Testing

“…Performance indicators are based on the confusion matrix of each dataset. A confusion matrix gives information on how a certain behavior is correctly detected, and how it is classified as another behavior [69,70]. Evaluation of the classifiers' performance implicitly describes the efficiency of OTCP approach, because correct classification of classes and their execution for TCP is essentially focused on in this study.…”

PGA: An Efficient Adaptive Traffic Signal Timing Optimization Scheme Using Actor-Critic Reinforcement Learning Algorithm

“…It is of great importance to design and implement advanced signal timing scheme to reduce traffic congestion and pollution, and to achieve optimization and coordination of urban traffic. In recent decades, the blooming development of artificial intelligence makes the remarkable progress of modern intelligent transportation systems (ITS), e.g., intelligent signal control [1], big data analysis [2], [3], traffic flow prediction [4], pedestrian detection [5], etc.…”