Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process

Short-term prediction of traffic variables aims at providing information for travelers before commencing their trips. In this paper, machine learning methods consisting of long short-term memory (LSTM), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) are employed to predict traffic state, categorized into A to C for segments of a rural road network. Since the temporal variation of rural road traffic is irregular, the performance of applied algorithms varies among different time intervals. To find the most precise prediction for each time interval for segments, several ensemble methods, including voting methods and ordinal logit (OL) model, are utilized to ensemble predictions of four machine learning algorithms. The Karaj-Chalus rural road traffic data was used as a case study to show how to implement it. As there are many influential features on traffic state, the genetic algorithm (GA) has been used to identify 25 of 32 features, which are the most influential on models’ fitness. Results show that the OL model as an ensemble learning model outperforms machine learning models, and its accuracy is equal to 80.03 percent. The highest balanced accuracy achieved by OL for predicting traffic states A, B, and C is 89, 73.4, and 58.5 percent, respectively.

Download Full-text

Short-term prediction of traffic state, statistical approach versus machine learning approach

Scientia Iranica ◽

10.24200/sci.2021.57906.5469 ◽

2021 ◽

Vol 0 (0) ◽

pp. 0-0

Author(s):

Arash Rasaizadi ◽

Elahe Sherafat ◽

Seyedehsan Seyedabrishami

Keyword(s):

Machine Learning ◽

Statistical Approach ◽

Learning Approach ◽

Short Term ◽

Traffic State ◽

Term Prediction ◽

Machine Learning Approach ◽

Short Term Prediction

Download Full-text

Machine Learning Incorporated With Causal Analysis for Short-Term Prediction of Sea Ice

Frontiers in Marine Science ◽

10.3389/fmars.2021.649378 ◽

2021 ◽

Vol 8 ◽

Author(s):

Ming Li ◽

Ren Zhang ◽

Kefeng Liu

Keyword(s):

Machine Learning ◽

Sea Ice ◽

Lead Time ◽

Causal Analysis ◽

Classification And Regression Tree ◽

Support Vector ◽

Short Term ◽

Term Prediction ◽

Arctic Navigation ◽

Short Term Prediction

Accurate and fast prediction of sea ice conditions is the foundation of safety guarantee for Arctic navigation. Aiming at the imperious demand of short-term prediction for sea ice, we develop a new data-driven prediction technique for the sea ice concentration (SIC) combined with causal analysis. Through the causal analysis based on kernel Granger causality (KGC) test, key environmental factors affecting SIC are selected. Then multiple popular machine learning (ML) algorithms, namely self-adaptive differential extreme learning machine (SaD-ELM), classification and regression tree (CART), random forest (RF) and support vector regression (SVR), are employed to predict daily SIC, respectively. The experimental results in the Barents-Kara (B-K) sea show: (1) compared with correlation analysis, the input variables of ML models screened out by causal analysis achieve better prediction; (2) when lead time is short (<3 d), the four ML algorithms are all suitable for short-term prediction of daily SIC, while RF and SaD-ELM have better prediction performance with long lead time (>3 d); (3) RF has the best prediction accuracy and generalization ability but hugely time consuming, while SaD-ELM achieves more favorable performance when taking computational complexity into consideration. In summary, ML is applicable to short-term prediction of daily SIC, which develops a new way of sea ice prediction and provides technical support for Arctic navigation.

Download Full-text

Short-Term Prediction of Global Solar Radiation Energy Using Weather Data and Machine Learning Ensembles: A Comparative Study

Journal of Solar Energy Engineering ◽

10.1115/1.4049624 ◽

2021 ◽

Vol 143 (5) ◽

Author(s):

Rami Al-Hajj ◽

Ali Assi ◽

Mohamad Fouad

Keyword(s):

Machine Learning ◽

Solar Radiation ◽

Comparative Study ◽

Superior Performance ◽

Weather Data ◽

Support Vector ◽

Short Term ◽

Ensemble Techniques ◽

Term Prediction ◽

Short Term Prediction

Abstract The ability to predict solar radiation one-day-ahead is critical for the best management of renewable energy tied-grids. Several machine learning ensemble techniques have been proposed to enhance the short-term prediction of solar radiation strength. In general, finding an optimal ensemble model that consists of combining individual predictors is not trivial due to the need for tuning and other related issues. Few comparative studies have been presented to obtain optimal structures of machine learning ensemble that deal with predicting solar radiation. The contribution of the present research consists of a comparative study of various structures of stacking-based ensembles of data-driven machine learning predictors that are widely used nowadays to conclude the best stacking strategies in terms of performance to combine predictors of solar radiation. The base individual predictors are arranged to predict solar radiation intensity using historical weather and solar radiation records. Three stacking techniques, namely, feed-forward neural networks, support vector regressors, and k-nearest neighbor regressors, have been examined and compared to combine the prediction outputs of base learners. Most of the examined stacking models have been found capable to predict the solar radiation, but those related to combining heterogeneous models using neural meta-models have shown superior performance. Furthermore, we have compared the performance of combined models against recurrent models. The solar radiation predictions of the surveyed models have been evaluated and compared over an entire year. The performance enhancements provided by each alternative ensemble have been discussed.

Download Full-text