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

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题名:	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
作者:	林治良;LIN,CHIH-LIANG
贡献者:	土木工程學系
关键词:	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
日期:	2014-07-21
上传时间:	2014-10-15 14:23:40 (UTC+8)
出版者:	國立中央大學
摘要:	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。本研究主要透過蒐集A客運公司駕駛、路線以及行車紀錄器所記錄的各項資訊，經過資料處理將可能影響油耗的因素分為人、車、路、駕駛行為四大屬性共15個變數，透過多元迴歸分析方法找出影響油耗的原因，並建立油耗預測模型。接著利用模型預測路線油耗以及計算碳排放量，最後探討各種情境下節能減碳之成效，作為管理者參考之用。研究結果顯示，影響油耗之因素為駕駛年齡、駕駛年資、車系、每公里營收以及加速度，其中駕駛年齡、駕駛年資和車系為虛擬變數，五個變數皆與油耗呈正相關。最後以A客運公司一條營運路線為例，利用油耗預測模型估計路線總油耗以及計算二氧化碳排放量，並模擬情境調整五個顯著影響油耗的變數，發現若所有變數都是在最佳的情況之下，該研究路線一年可減少33公噸，約8.5%的CO2排放量。 ;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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