Research Article
Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model
Issue:
Volume 12, Issue 5, October 2024
Pages:
72-79
Received:
29 July 2024
Accepted:
2 September 2024
Published:
10 October 2024
Abstract: For the development of the national economy, transportation is strategically significant. As the increasing ownership of automobiles, traffic jams are a common occurrence. Accurate prediction of traffic flow contributes to diverting traffic effectively and improving the quality of urban traffic, in turn improving the operation of the overall transportation system. The rapid development of artificial intelligence technologies, especially machine learning and deep learning, has provided effective methods for accurate prediction of traffic flow. Based on the above, in order to improve the accuracy of the prediction and to extend the application of machine learning and deep learning in the prediction of traffic flow, this study proposed a bagging-based ensemble learning model. Firstly, normalization method is used to preprocess the data. Subsequently, base prediction models including decision tree, random forest, logistic regression, convolution neural network, long short-term memory and multilayer perceptron are selected for training the prediction model, respectively. Finally, bagging-based ensemble learning method is used to integrate these base prediction models to further predict traffic flow. The results of comparison between the single base prediction models and the bagging-based ensemble learning model on the five evaluation indicators show that, for predicting the traffic flow, the bagging-based ensemble learning model outperforms the base prediction models. Meanwhile, this study explores the potential in the application of machine learning, deep learning, and especially bagging-based ensemble learning to predict traffic flow.
Abstract: For the development of the national economy, transportation is strategically significant. As the increasing ownership of automobiles, traffic jams are a common occurrence. Accurate prediction of traffic flow contributes to diverting traffic effectively and improving the quality of urban traffic, in turn improving the operation of the overall transp...
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Research Article
Effect of Life Expectancy on Economy Growth for High-Income Nations
Kayode Okunola*
,
Bolanle Okunola,
Oladimeji Adewuyi
Issue:
Volume 12, Issue 5, October 2024
Pages:
80-89
Received:
6 May 2024
Accepted:
13 August 2024
Published:
12 November 2024
DOI:
10.11648/j.sjams.20241205.12
Downloads:
Views:
Abstract: The global age distribution has undergone substantial changes in recent years due to a rise in life expectancy. Based on projections, the global population of those aged 60 and beyond is expected to reach 2 billion by 2050, representing almost 25% of the total population. By the year 2050, it is expected that the proportion of adults aged 80 years and older will rise by 1% to 4% of the global population. Because of this trend, economic growth may be hampered. The growing reliance on elderly people results in an increase in taxation, while political pressures may cause public funding to be redirected to adult social care. If this option is made, it could be detrimental to both growth and investment. The present study uses panel data from high-income countries to determine if life expectancy is a favorable predictor of economic growth using Granger causality and panel regression. The Hausman test was used to evaluate pooled, random, and fixed effect models in order to determine which model was the most appropriate. Based on the results, the fixed effect model tends to perform better, as indicated by the p-value being less than 0.05. Furthermore, the findings convey that life expectancy has a negative impact on economic growth.
Abstract: The global age distribution has undergone substantial changes in recent years due to a rise in life expectancy. Based on projections, the global population of those aged 60 and beyond is expected to reach 2 billion by 2050, representing almost 25% of the total population. By the year 2050, it is expected that the proportion of adults aged 80 years ...
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