Autor: |
Linzhi Zou, Jiawen Wang, Minqian Cheng, Jiayu Hang |
Jazyk: |
angličtina |
Rok vydání: |
2024 |
Předmět: |
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Zdroj: |
Journal of Advanced Transportation, Vol 2024 (2024) |
Druh dokumentu: |
article |
ISSN: |
2042-3195 |
DOI: |
10.1155/2024/4912642 |
Popis: |
The travel time reliability (TTR) is crucial for evaluating the reliability of road networks, but real traffic data is often incomplete and sparse. This study validates that road network TTR conforms to a normal distribution and devises a quantification approach for road network TTR. Two reliability estimation methods are tailored for two data sources: section detectors and mobile detectors. Simulation experiments have confirmed the effectiveness of these methods. The study emphasizes that the TTR estimation method using traffic section data (S-TTR), which is based on the verified normal distribution assumption, maintains average absolute errors below 10%. On the other hand, the TTR estimation method that utilizes sparse trajectory data (T-TTR), which relies on tensor decomposition, proficiently fills in all missing data with an average error of 0.0059. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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