標題: | 都市貨運貨物分類之研究 A Relationship Study of Urban Freight and Cargo Classification |
作者: | 賴惠君 Hui-Chun Lai 黃台生 Tai-Sheng Hwang 運輸與物流管理學系 |
關鍵字: | 都市貨物運輸;貨物分類;群落分析;因子分析;urban freight transportation;cargo classification;cluster analysis |
公開日期: | 1998 |
摘要: | 近年來由於台灣經濟快速成長、國民所得普遍提高及消費需求的加大,導致流通貨物大幅增加,尤其都市內貨物流通更要求迅速確實,但在長期忽視及缺乏完善的管理政策下,使得貨物運輸與都市交通間的衝突更加明顯。本研究希望能藉由對都市貨物流通的瞭解並以交通管理的觀點進行貨物分類的研究,企圖找出不同運送特性之貨物分類,提供運輸管理者另一種交通規劃與政策研擬之參考方向。
由於貨物分類的方式眾多,且各分類之目的均不相同,為瞭解各種貨物在都市流通時之交通特性,故本研究先解構整個都市貨物需求之類別,再就各類別貨物本身特性及在都市中運輸流通的方式作概略性的說明,並由上述特性及運輸流通方式歸納其對都市交通的影響作為本研究分類之依據,而後針對主題設計問卷,並對回收的問卷進行資料統計,以界定貨車所載送貨物類別及運送特性。
本研究之貨物分類,分別就自用車與營業車之資料分別進行變數之因子篩選及貨物群組之建立。在建立群組的分析過程中,首先定義群組的組成變數,利用因子分析,從眾多的變數中,萃取出主要因子,用以解釋複雜的旅次行為,接著利用每一因子中之主要變數,透過群落分析,將所有樣本分成若干個群組,並根據所分出群組之特性,重新賦予各群組新的貨物特性名稱。 Due to the fast growing Taiwan economy and GNP of recent years and high expectation of consumer needs, fright service becomes a very important commercial activity. It is mostly true in case of urban cargo-freight, because efficiency and effectiveness are highly demanded. However, resulting from lacking proper regulation by the government, efficient and effective urban cargo freight appears to be incompatible with heavy traffic of metropolis area. We intend to point out an alternative of transportation planning by studying the relationship between cargo classification and urban freight. For different study purposes, there are many ways of cargo classification. To understand the relationship between cargo classification and urban freight, we started with classification urban cargo services. To analysis the transportation tool for each urban cargo service class and how that tool affects the metropolis traffic, we designed a questionnaire and completed the statistic research for the data collected. In the study, data collected from private and commercial vehicles was further applied to establish variable factor and cargo groupings. By analysis of variable factors, we defined major factors for every grouping. Major factors were then applied to explain the complex trip behavior for each group. By doing cluster analysis, sampled data were classified and grouped. Every grouping was defined and named according to its cargo characteristics. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#NT870118023 http://hdl.handle.net/11536/63880 |
Appears in Collections: | Thesis |