Forecasting short-term traffic speed based on multiple attributes of adjacent roads
| Autor: | Liao Sai, Dongjin Yu, Tarique Anwar, Chengfei Liu, Yiyu Wu, Chengbiao Zhou, Wanqing Li |
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| Rok vydání: | 2019 |
| Předmět: |
Information Systems and Management
Relation (database) Computer science Real-time computing 02 engineering and technology Correlation function (quantum field theory) Traffic flow Stability (probability) Management Information Systems Term (time) Artificial Intelligence ComputerSystemsOrganization_MISCELLANEOUS 020204 information systems 0202 electrical engineering electronic engineering information engineering Piecewise 020201 artificial intelligence & image processing Cluster analysis Software |
| Zdroj: | Knowledge-Based Systems. 163:472-484 |
| ISSN: | 0950-7051 |
| DOI: | 10.1016/j.knosys.2018.09.003 |
| Popis: | Forecasting the short-term speed of moving vehicles on roads plays a vital role on traffic control and trip planning, which however still remains a challenging task when the high accuracy is required. In this paper, we propose a novel approach to the short-term traffic speed forecasting, which takes into account the influence of different traffic attributes, such as traffic flow, traffic speed, road occupancy and traffic density, of adjacent roads on the traffic speed. In addition, in order to obtain the more accurate relation between traffic speed and traffic attributes, we employ the idea of piecewise correlation function and adopt the Jenks clustering method with dynamic programming to determine the segment intervals of relation. We validate our approach based on the real data collected from Wenzhou and Hangzhou, two large cities located in eastern China. The extensive experimental results show that, compared with the state-of-the-art approaches, our approach has the higher stability and accuracy, especially for 5-minute and 10-minute speed prediction. |
| Databáze: | OpenAIRE |
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