Presentation | 2018-07-02 An investigation of prediction accuracy of bus delay time influenced by the amount of training data and the variation of outlier detection Tsubasa Yamaguchi, Mansur AS, Tsunenori Mine, |
---|---|
PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Prediction of bus delay is one of crucial research tasks in the intelligent transport systems (ITS) field, and is important not only for a bus operation company, but also passengers, who want to know how many minutes a bus will be concretely delayed to get at a bus stop. In this paper, we propose methods to predict bus delay using bus probe data collected from Nov. 21st to Dec. 20th, 2013 and provided by Nishitetsu Bus company. We used several machine learning methods to build the prediction models. Wediscuss experimental results from the points that how much extents the difference of the amount of training data and the variation of outlier detection methods affect prediction accuracy of predicting bus delay. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | scheduled bus / delay time prediction / probe data / machine learning |
Paper # | AI2018-4 |
Date of Issue | 2018-06-25 (AI) |
Conference Information | |
Committee | AI |
---|---|
Conference Date | 2018/7/2(1days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | |
Chair | Tsunenori Mine(Kyushu Univ.) |
Vice Chair | Daisuke Katagami(Tokyo Polytechnic Univ.) / Naoki Fukuta(Shizuoka Univ.) |
Secretary | Daisuke Katagami(Ritsumeikan Univ.) / Naoki Fukuta(Univ. of Electro-Comm.) |
Assistant | Yuko Sakurai(AIST) |
Paper Information | |
Registration To | Technical Committee on Artificial Intelligence and Knowledge-Based Processing |
---|---|
Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | An investigation of prediction accuracy of bus delay time influenced by the amount of training data and the variation of outlier detection |
Sub Title (in English) | |
Keyword(1) | scheduled bus |
Keyword(2) | delay time prediction |
Keyword(3) | probe data |
Keyword(4) | machine learning |
1st Author's Name | Tsubasa Yamaguchi |
1st Author's Affiliation | Kyushu University(Kyushu Univ.) |
2nd Author's Name | Mansur AS |
2nd Author's Affiliation | Kyushu University(Kyushu Univ.) |
3rd Author's Name | Tsunenori Mine |
3rd Author's Affiliation | Kyushu University(Kyushu Univ.) |
Date | 2018-07-02 |
Paper # | AI2018-4 |
Volume (vol) | vol.118 |
Number (no) | AI-116 |
Page | pp.pp.15-21(AI), |
#Pages | 7 |
Date of Issue | 2018-06-25 (AI) |