| dc.description.abstract | As technology advances, digital tachograph records not only more information than before but also driving behavior in detail, which afterwards is exported in charts, in order to be applied in multiple uses. Due to rising fuel price in recent years there has been an increase in transportation cost. As far as managements are concerned, it is a critical issue to understand the reasons for affecting fuel consumption and how to achieve energy saving.
In this study, we gathered the drivers’ route of company A and tachograph information. After data processing, the factors that affect fuel consumption are divided into four attributes, which are people, cars, route, driving behavior, and which include 15 variables. Through multiple regression analysis, the reasons that affect fuel consumption are found and establish fuel consumption prediction model. Then the model is used to predict route fuel consumption and calculate carbon emission. At last we explore, under various scenarios, explore the effectiveness of carbon reduction as reference for managements.
The results show that the factors affecting fuel consumption are driver’s age, seniority, car type, every kilometer revenue and acceleration. The driver’s age, seniority and car type are the dummy variables. Five variables are associated with the consumption. The route of A company for example, uses the fuel consumption prediction model to estimate the total fuel consumption and calculate CO2 emissions, and the simulated situation adjusts five significantly affected consumption variables. If the driver’s age, seniority, car type, every kilometer revenue and acceleration are in the best situation, the research route can be reduced by 33 tons about 8.5% CO2 emissions a year.
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