Presentation | 2012-11-07 Parameter Estimation and Active Learning in the ZRP Based on Traffic Spatiotemporal Data Koichi KOBAYASHI, Keisuke YAMAZAKI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | There are many analyses on traffic flow models in order to elucidate the principals of traffic jams and to ease them. However, in spite of significance in application, there are few studies on estimation of the model parameter from observed traffic flow data. In the present paper, we focus on the zero range process (ZRP), the behavior of which is mathematically analysed, and we formulate its parameter estimation based on traffic spatiotemporal data. Moreover, an active learning method to minimize the estimation error by optimization of vehicle density on a road is proposed. We confirm the efficiency of the proposed algorithm by numerical simulations. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | regression analysis / traffic flow models / active learning |
Paper # | IBISML2012-47 |
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Committee | IBISML |
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Conference Date | 2012/10/31(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Parameter Estimation and Active Learning in the ZRP Based on Traffic Spatiotemporal Data |
Sub Title (in English) | |
Keyword(1) | regression analysis |
Keyword(2) | traffic flow models |
Keyword(3) | active learning |
1st Author's Name | Koichi KOBAYASHI |
1st Author's Affiliation | Interdisciplinary Graduate School of Science and Engineering, Tokyo Tech.() |
2nd Author's Name | Keisuke YAMAZAKI |
2nd Author's Affiliation | Interdisciplinary Graduate School of Science and Engineering, Tokyo Tech. |
Date | 2012-11-07 |
Paper # | IBISML2012-47 |
Volume (vol) | vol.112 |
Number (no) | 279 |
Page | pp.pp.- |
#Pages | 5 |
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