Translated Title
Prediction Model of Driving Energy Consumption Based on PCA and BP Network
Translated Abstract
Nowadays, society pays much attention to the problems of fuel consumption. This paper concerns about prediction of microcosmic energy consumption, and its purpose is to realize fuel consumptions of Beijing basic freeway section. Based on OBD/GPS terminal installation on taxis, we extract driving behavior’s data of taxi drivers, select main relevant indexes, set up the prediction model of fuel consumption, and realize accurate prediction of fuel consumption in Beijing basic freeway section. Results show that average speed, standard deviation of speed, max speed, rate of operating condition, average acceleration and deceleration, distance and energy have greater influence on fuel consumption; PCA and neural network combination model can realize energy consumption prediction effectively, and the accuracy of prediction can reach 92.46%. This research can provide strong supports on monitor and regulation of traffic energy consumption.
Translated Keyword
driving behavior
urban traffic
prediction model
neural network
energy consumption
PCA
Access Number
WF:perioarticaljtysxtgcyxx201605028
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