原文传递
Long-term travel time prediction using gradient boosting
| 题名： | Long-term travel time prediction using gradient boosting |
| 正文语种： | 英文 |
| 作者： | Che-Ming Chen;Chia-Ching Liang;Chih-Peng Chu |
| 作者单位： | National Dong Hwa University |
| 关键词： | Decision tree; ensemble method; Fourier series; gradient boosting; longterm prediction; travel time prediction |
| 摘要： | Reliable long-term travel time prediction would be effective support to traffic management, for example, traffic flow control or the pricing of tolls. Gradient boosting (GB) has been suggested as an excellent tool for short-term travel time prediction problems. This paper shows that GB with modifications can also work for Iong-term prediction. We introduce key variables, regarded as multiple factors, from various sources into a GB model and apply the Fourier filteri ng process to reduce no ise. Those key variables in elude time of the day, day of the week, holidays, big events or activities, promotion of tolls, and narrowing of roadways. This paper takes the first step in applying key variables (from various sources) to predict long-term travel time. The electronic toll collection (ETC) data of Taiwan Freeway No.1 are used to train and to test in our process. Results demonstrate that the prediction ability of the GB model with Fourier filtering is the best. This paper shows a research direction of Ion g-term travel time predict! on, and the relatively im porta nt key variables. |
| 出版日期： | 2020 |
| 出版年： | 2020 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | Vol24 |
| 期： | No01-06 |
| 页码： | 109-124 |
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