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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/64778

    Title: 市區公車油耗與節能減碳之研究;A Study on City Bus Fuel Consumption and Energy Saving and Carbon Reduction
    Authors: 林治良;LIN,CHIH-LIANG
    Contributors: 土木工程學系
    Keywords: 行車紀錄器;多元迴歸分析;駕駛行為;油耗;節能減碳;Tachograph;Multiple Regression Analysis;Driving Behavior;Fuel Consumption;Energy Saving and Carbon Reduction
    Date: 2014-07-21
    Issue Date: 2014-10-15 14:23:40 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著科技的進步,數位式行車紀錄器除了能夠記錄更多資訊,亦可將行車過程中的駕駛行為作詳細記錄並以圖表方式將資訊輸出,便於日後各種用途來使用。近年來油價上漲,交通運輸業營運成本逐年提高,對於管理者而言,瞭解影響油耗的原因以及如何才能達到省油的目的,是一個重要的課題。而近年來節能減碳之意識興起,如何有效的節省能源消耗以及減少二氧化碳排放量,為當今世界各國共同努力的目標。


    ;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.
    Appears in Collections:[土木工程研究所] 博碩士論文

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