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Investigating long‐term vehicle speed prediction based on BP‐LSTM algorithms - Yufang - 2019 - IET Intelligent Transport Systems - Wiley Online Library
Source: ietresearch.onlinelibrary.wiley.com
Article summary:
1. Vehicle speed prediction plays an important role in modern intelligent vehicles, such as energy management systems and navigation systems.
2. Traditional vehicle speed prediction algorithms are based on theoretical models and lack better algorithm structures for regression prediction calculation.
3. A novel data-driven back propagation-long short-term memory (BP-LSTM) algorithm is proposed for on-road individual long-term average vehicle speed prediction along a driving route.
Article analysis:
The article Investigating long‐term vehicle speed prediction based on BP‐LSTM algorithms by Yufang (2019) is generally reliable and trustworthy, providing a comprehensive overview of the current state of research into long-term vehicle speed prediction. The article provides a detailed description of the challenges associated with traditional vehicle speed prediction algorithms, as well as the potential benefits of using machine learning algorithms to improve accuracy and efficiency. The author also presents a novel data-driven back propagation-long short-term memory (BP-LSTM) algorithm for on-road individual long-term average vehicle speed prediction along a driving route, which could be used to improve travel time predictions and energy consumption predictions for new energy vehicles.
The article does not appear to have any major biases or one sided reporting, nor does it contain any unsupported claims or missing points of consideration. All claims made are supported by evidence from relevant sources, and all counterarguments are explored in detail. There is no promotional content or partiality present in the article either, and possible risks are noted where appropriate. The article presents both sides of the argument equally, making it an overall reliable source of information on this topic.