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.
Investigating long-term vehicle speed prediction based on BP-LSTM algorithms
IET Intelligent Transport Systems ; 13 , 8 ; 1281-1290
2019-05-07
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
BP-LSTM algorithms , accurate on-road vehicle speed prediction , historical driver–vehicle–road–traffic data , transportation , road vehicles , vehicle type , prediction accuracy , long-term vehicle speed prediction , learning (artificial intelligence) , driver–vehicle–road–traffic system , long-term individual vehicle speed prediction , energy consumption prediction , back propagation-long short-term memory , long-term prediction , short-term prediction , predicted vehicle speed , driver–vehicle–road–traffic historical characteristic parameters , neural nets , road traffic , prediction performance , traffic engineering computing , novel data-driven vehicle speed prediction method , travel time prediction
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