NDLI: Investigating long-term vehicle speed prediction based on BP-LSTM algorithms

Content Provider	IET Digital Library
Author	Yufang, Li Mingnuo, Chen Wanzhong, Zhao
Abstract	Vehicle speed prediction is quite essential for many intelligent vehicular and transportation applications. Accurate on-road vehicle speed prediction is challenging because individual vehicle speed is affected by many factors related to driver–vehicle–road–traffic system, e.g. the traffic conditions, vehicle type, and driver's behavior, in either a deterministic or stochastic way. Also machine learning makes vehicle speed predictions more accessible by exploring the potential relationship between the vehicle speed and its main factors based on the historical driving data in the context of vehicular networks. This study proposes a novel data-driven vehicle speed prediction method based on back propagation-long short-term memory (BP-LSTM) algorithms for long-term individual vehicle speed prediction along the planned route. Also Pearson correlation coefficient is adopted to analyse the correlation of driver–vehicle–road–traffic historical characteristic parameters for the enhancement of the model's computing efficiency. Finally, a real natural driving data in Nanjing is used to evaluate the prediction performance with a result that the proposed vehicle speed prediction method outperforms other ones in terms of prediction accuracy. Moreover, based on the predicted vehicle speed, this work studies and analyses its effectiveness in two scenarios of energy consumption prediction and travel time prediction.
Starting Page	1281
Ending Page	1290
Page Count	10
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 8, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5593
Journal	IET Intelligent Transport Systems
Publisher Date	2019-04-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Accurate On-road Vehicle Speed Prediction Back Propagation-long Short-term Memory BP-LSTM Algorithm Driver–vehicle–road–traffic Historical Characteristic Parameters Driver–vehicle–road–traffic System Energy Consumption Prediction Historical Driver–vehicle–road–traffic Data Knowledge Engineering Technique Learning in AI Long-term Individual Vehicle Speed Prediction Long-term Prediction Long-term Vehicle Speed Prediction Neural Computing Technique Neural Nets Novel Data-driven Vehicle Speed Prediction Method Predicted Vehicle Speed Prediction Accuracy Prediction Performance Road Traffic Road Vehicle Short-term Prediction Statistics Traffic Engineering Computing Transportation Travel Time Prediction Vehicle Type
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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