A Mesoscopic Fuel Consumption Prediction Method Based on rbfnn

Abstract

The invention relates to a RBFNN-based mesoscopic fuel consumption prediction method, which determines the influencing factors of road energy consumption; divides the vehicle driving trajectory into driving segments; calculates the average energy consumption of vehicles in a form segment; and analyzes the average energy consumption distribution of the road section Calculate the average energy consumption of the road section; determine the parameter settings such as road energy consumption factors; use the obtained data set as the training set of the neural network for model learning; input the test data set, and calculate the prediction result of road fuel consumption. With the support of a large amount of energy consumption trajectory data set, the present invention accumulates a large number of observation samples on input parameters such as road section type, vehicle average speed and output parameters of road energy consumption, and grasps the influencing factors of road energy consumption through the training of observation samples The law of the correlation relationship with the average energy consumption of the road, so that the energy consumption of other road sections in the road network with insufficient energy consumption trajectory samples can be predicted, and the generalization of the energy consumption law has been realized. There is a high degree of monitoring granularity. precision.

Description

technical field [0001] The invention relates to a mesoscopic fuel consumption prediction method based on RBFNN (RBF neural network, also called Radial Basis Function) for application in energy-saving and emission-reducing intelligent transportation systems. Background technique [0002] The mesoscopic fuel consumption prediction model is to measure and calculate the average energy consumption of vehicles driving through designated road sections. With the rapid development of urban traffic today, people pay more and more attention to the intelligent application of energy saving and emission reduction. The accuracy of measurement is guaranteed on the basis of a finer measurement granularity. [0003] The traditional mesoscopic fuel consumption prediction method initially uses the driving cycle to describe the characteristics of traffic flow. Many researchers no longer use the driving cycle but VSP (Vehicle Specific Power, motor vehicle specific power) to achieve more fine-gra...

AI Technical Summary

Problems solved by technology

[0005] However, the currently implemented models based on VSP distribution (including other similar models) can only measure energy consumption and emissions according to road grades, and have not yet realized fine-grained monitoring of road microstructure.

At the same time, the existing mesoscopic model uses intermediate quantities such as driving mode and VSP distribution as a bridge to communicate mesoscopic parameters (road grade, average speed, etc.) Multiple parameter conversions in the middle have resulted in a large loss of model measurement accuracy

[0006] To sum up, the existing road energy consumption models still have a lot of room for improvement in terms of accuracy and resolution of energy consumption calculations, and further improvements are needed in order to conduct refined energy consumption monitoring of road networks for environmental protection navigation and other needs. better support for intelligent transportation systems

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