| Autor: |
Weixin Zhao, Guijuan Wang, Zhong Wang, Liang Liu, Xu Wei, Yadong Wu |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Visual Informatics, Vol 6, Iss 4, Pp 1-11 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2468-502X |
| DOI: |
10.1016/j.visinf.2022.06.002 |
| Popis: |
Bus travel time is uncertain due to the dynamic change in the environment. Passenger analyzing bus travel time uncertainty has significant implications for understanding bus running errors and reducing travel risks. To quantify the uncertainty of the bus travel time prediction model, a visual analysis method about the bus travel time uncertainty is proposed in this paper, which can intuitively obtain uncertain information of bus travel time through visual graphs. Firstly, a Bayesian encoder–decoder deep neural network (BEDDNN) model is proposed to predict the bus travel time. The BEDDNN model outputs results with distributional properties to calculate the prediction model uncertainty degree and provide the estimation of the bus travel time uncertainty. Second, an interactive uncertainty visualization system is developed to analyze the time uncertainty associated with bus stations and lines. The prediction model and the visualization model are organically combined to better demonstrate the prediction results and uncertainties. Finally, the model evaluation results based on actual bus data illustrate the effectiveness of the model. The results of the case study and user evaluation show that the visualization system in this paper has a positive impact on the effectiveness of conveying uncertain information and on user perception and decision making. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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