دورية أكاديمية
Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms
| العنوان: | Implications of Spatiotemporal Data Aggregation on Short-Term Traffic Prediction Using Machine Learning Algorithms |
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| المؤلفون: | Rivindu Weerasekera, Mohan Sridharan, Prakash Ranjitkar |
| المصدر: | Journal of Advanced Transportation, Vol 2020 (2020) |
| بيانات النشر: | Wiley, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Transportation engineering LCC:Transportation and communications |
| مصطلحات موضوعية: | Transportation engineering, TA1001-1280, Transportation and communications, HE1-9990 |
| الوصف: | Short-term traffic prediction is a key component of Intelligent Transportation Systems. It uses historical data to construct models for reliably predicting traffic state at specific locations in road networks in the near future. Despite being a mature field, short-term traffic prediction still poses some open problems related to the choice of optimal data resolution, prediction of nonrecurring congestion, and the modelling of relevant spatiotemporal dependencies. As a step towards addressing these problems, this paper investigates the ability of Artificial Neural Networks, Random Forests, and Support Vector Regression algorithms to reliably model traffic flow at different data resolutions and respond to unexpected traffic incidents. We also explore different feature selection methods to identify and better understand the spatiotemporal attributes that most influence the reliability of these models. Experimental results indicate that data aggregation does not necessarily achieve good performance for multivariate spatiotemporal machine learning models. The models learned using high-resolution 30-second input data outperformed the corresponding baseline ARIMA models by 8%. Furthermore, feature selection based on Recursive Feature Elimination resulted in models that outperformed those based on linear correlation-based feature selection. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 0197-6729 2042-3195 |
| Relation: | https://doaj.org/toc/0197-6729; https://doaj.org/toc/2042-3195 |
| DOI: | 10.1155/2020/7057519 |
| URL الوصول: | https://doaj.org/article/d86270e92a3b45bea1e8e8598f46d3d1 |
| رقم الأكسشن: | edsdoj.86270e92a3b45bea1e8e8598f46d3d1 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 01976729 20423195 |
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| DOI: | 10.1155/2020/7057519 |