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k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition
| 题名： | k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition |
| 正文语种： | 英文 |
| 作者： | Dalian Maritime Univ; |
| 作者单位： | Bin Yu;Xiaolin Song;Feng Guan;Zhiming Yang;Baozhen Yao |
| 关键词： | Short-term traffic condition; Multi-time-step prediction model; ^-nearest neighbor; Spatial-temporal parameters |
| 摘要： | One of the most critical functions of an intelligent transportation system (ITS) is to provide accurate and real-time prediction of traffic condition. This paper develops a short-term traffic condition prediction model based on the ^-nearest neighbor algorithm. In the prediction model, the time-varying and continuous characteristic of traffic flow is considered, and the multi-time-step prediction model is proposed based on the single-time-step model. To test the accuracy of the proposed multi-time-step prediction model, GPS data of taxis in Foshan city, China, are used. The results show that the multi-time-step prediction model with spatial-temporal parameters provides a good performance compared with the support vector machine (SVM) model, artificial neural network (ANN) model, real-time-data model, and history-data model. The results also appear to indicate that the proposed k-nearest neighbor model is an effective approach in predicting the short-term traffic condition. |
| 出版年： | 2016 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | Vol.142 |
| 期： | No.6 |
| 页码： | 04016018 |
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