Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Computing | en_US |
| dc.contributor.advisor | Chan, C. C. Keith (COMP) | - |
| dc.creator | Zhang, Pingcheng | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/10148 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Traffic state prediction using deep learning methods | en_US |
| dcterms.abstract | Traffic state prediction is a very important part of smart city related research. In the era of big data, we can get access to unprecedented massive amount of data, which allow us to better perceive and observe the operation of the city and explore its the operation mechanism. In recent years, the vigorous development of deep learning has enabled these data to play a more valuable role, making it possible to obtain knowledge about cities from the data. Firstly, this dissertation discussed the necessity and significance of traffic state prediction in smart cities, and holds that accurate traffic state prediction is the basic prerequisite and necessary condition for the realization of smart cities. Then, through the survey of the related works, we summarized the related research on traffic forecasting using deep learning methods. Through literature review, it can be noticed that due to the nature of data, most of works focused on scalar value prediction while few work has been done to study the shifting of traffic states. To fill this gap, this dissertation attempts to study the state shift rule of traffic states using deep learning methods. By strictly defining the problem, the traffic state prediction problem is tranlated into the time series prediction problem of adjacency matrices, and an innovative data structure is proposed to describe the traffic state. The experiment results show that this method of describing traffic state can capture traffic characteristics, and the prediction ability of the model can also provide some reference at the macro level. | en_US |
| dcterms.extent | viii, 42 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2019 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.LCSH | Traffic estimation | en_US |
| dcterms.LCSH | Travel -- Forecasting | en_US |
| dcterms.LCSH | Machine learning | en_US |
| dcterms.LCSH | Traffic engineering -- Data processing | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022270058203411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.79 MB | Adobe PDF | View/Open |
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