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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hwang, K.P. Wei-Hsun Lee Wen-Bin Wu |
| Copyright Year | 2012 |
| Description | Author affiliation: Dept. of Transportation and Communication Management, National Cheng Kung University, Tainan, Taiwan, R.O.C (Hwang, K.P.; Wei-Hsun Lee; Wen-Bin Wu) |
| Abstract | Travel time information plays an important role in ITS, especially in advanced traveler information system (ATIS). Traditionally, travel time is predicted by a single data source, such as vehicle detectors (VD) or probing vehicles (PV). In this paper, we try to predict travel time by integrating these two data sources by a dynamic weighted fusion scheme. The weights of the data sources are dynamically determined by the distance weight scheme to enhance the prediction precision. The proposed TTP model is applied to a small traffic network located in the east and north district of Tainan City, Taiwan. VD data is provided by traffic bureau of Tainan city government and probing vehicles raw data is collected from a Taxi dispatching system. The experiment results show that dynamic weighted combination of these two data sources can enhance the precision of the TTP, and the prediction stability of the proposed model is better than both the single source TTP models (VD or PV). |
| Starting Page | 476 |
| Ending Page | 481 |
| File Size | 767631 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467330718 |
| e-ISBN | 9781467330701 |
| e-ISBN | 9781467330695 |
| DOI | 10.1109/ITST.2012.6425224 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-11-05 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | data fusion Roads Predictive models Cities and towns Data models Stability analysis travel time prediction Delay advanced traveller information system (ATIS) Vehicles |
| Content Type | Text |
| Resource Type | Article |
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