The Study of Using Driving Data to Develop Mechanism for Predicting Fuel Consumption

碩士 === 元智大學 === 資訊工程學系 === 100 === In this study, digital recorders capture vehicle and traffic information, through the study of methods to isolate these basic information associated with the fuel consumption factors, and to explore the fuel factors in characteristics of vehicle and driving habits...

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Bibliographic Details
Main Authors: Yu-Cheng Lin, 林育丞
Other Authors: Hsiu-HsenYao
Format: Others
Language:zh-TW
Published: 2012
Online Access:http://ndltd.ncl.edu.tw/handle/54879408891231518186
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Summary:碩士 === 元智大學 === 資訊工程學系 === 100 === In this study, digital recorders capture vehicle and traffic information, through the study of methods to isolate these basic information associated with the fuel consumption factors, and to explore the fuel factors in characteristics of vehicle and driving habits under their influence and to classify them into the vehicle fuel consumption factor and driving fuel consumption factor. In this study, fuel consumption estimate Methods I, using each index and which index is high correlation with fuel consumption through ANOVA analysis to identify the vehicle factor and the driving factor, these variable factors and types of vehicles in the multiple regression analysis model to predict the fuel consumption (km/l). Another fuel consumption prediction method II for the detection speed range under different number of traffic interregional, useing regression analysis correlation with index and fuel consumption to solve the fuel consumption is discrete problem. Through the number of speed range multiply by the corresponding fuel consumption database to predict the fuel consumption. The results showed that fuel consumption estimates mechanism is divided into a training phase and fuel consumption to estimate the test phase. Different methods of fuel consumption estimated through the training phase of the formula.Estimate methods for different comparison of the road, the use of Index of the vehicle and driving factors for the mountain road and highway is excellent. For generally road and contains a little narrow routes, the advantage of the speed interregionnal recognition is relatively stable.