OPEN TRAFFIC DATA OF ENGLAND AND SHORT-TERM TRAFFIC CONGESTION PREDICTION WITH AUTOML
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- MIYAZAKI Toshiyuki
- 八千代エンジニヤリング株式会社 技術創発研究所
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- OOSAWA Akaru
- 八千代エンジニヤリング株式会社 技術管理本部 情報技術部
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- KIKUCHI Yoshikazu
- 八千代エンジニヤリング株式会社 技術創発研究所
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- SUGAWARA Hiroaki
- 八千代エンジニヤリング株式会社 技術創発研究所
Bibliographic Information
- Other Title
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- イングランドの交通オープンデータとAutoMLによる短時間渋滞予測
Abstract
<p>In order to study how to utilize open data on traffic, we downloaded traffic data of England, where the data are available to the public. We focused on short-time traffic congestion prediction as one method of utilization, selected a relatively congested area in southern England and used PyCaret, a type of AutoML, to predict traffic congestion. Our model performed slightly better than models that assumed that the current situation would continue as is or that only the day of the week and time of day were used as input variables, indicating that machine learning can be used to improve traffic congestion prediction. On the other hand, the prediction performance of the as-is model varied greatly depending on the direction of travel at the same location, and the performance of the machine learning model also varied significantly accordingly. In order to compare the performance of machine learning traffic congestion predicts, it is necessary to establish a baseline forecast and show the improvement in performance against that baseline forecast.</p>
Journal
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- Intelligence, Informatics and Infrastructure
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Intelligence, Informatics and Infrastructure 3 (J2), 268-276, 2022
Japan Society of Civil Engineers
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Details 詳細情報について
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- CRID
- 1390012638715479936
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- ISSN
- 24359262
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed