(3.239.33.139) 您好!臺灣時間:2021/03/02 16:39
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:李文宇
研究生(外文):Wen-Yu Lee
論文名稱:應用深度學習自編碼於臺鐵國定假日之運量分析
論文名稱(外文):Deep Auto-encoder for Analyzing the Ridership of Taiwan Railways Administration on National Holidays
指導教授:許聿廷許聿廷引用關係
指導教授(外文):Yu-Ting Hsu
口試委員:鍾智林孫千山
口試委員(外文):Chih-Lin Chung
口試日期:2019-06-19
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:72
中文關鍵詞:國定假日日運量深度學習自編碼K平均演算法多項邏輯斯迴歸城際旅次區域型旅次
DOI:10.6342/NTU201901063
相關次數:
  • 被引用被引用:0
  • 點閱點閱:109
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
臺灣除擁有周休二日的休假制度外,每年也另有八個國定假日,依序為元旦、春節、二二八和平紀念日、清明節、勞動節、端午節、中秋節、及國慶日。除了春節連假長達六天(以上)以及少數一天的國定假日外,其餘假期長度多為三至四天。過往由於資料取得問題,針對臺鐵國定假日的運量,並無系統性的研究,近期在取得臺鐵2014至2018年間的所有票證資料後,因而獲得了完整的運量資訊。而進一步比較國定假日與一般週末之運量後,發現因國定假日時間長,其運量的波動程度相對較大,且其旅次需求量多,營運調度相對複雜,如能明確掌握運量分佈之情形,將可提供更完善的應對策略。過往關於運量的研究,大多是針對各種交通運具,如公車、軌道系統(地鐵、火車、高鐵)、航空的運量,進行直接預測;或針對影響運量的因子進行分析。相較之下,針對國定假日的運量特性進行分析之研究寥寥可數,因此本研究期臺鐵對於往後的國定假日,以研究成果為依據,能在事前瞭解每一日可能的運量分佈,將之作為營運調度的參考。
本研究的研究方法為依據每一放假日的本質,用其相對應的「起訖縣市(OD)-日運量分佈」表格,逐日分析其運量特性,使用深度學習自編碼(Deep Auto-encoder)降維、擷取特徵,再使用K-平均演算法(K-means Clustering)將相似的放假日分群,並找出各群的代表特徵,最後建立多項邏輯斯迴歸(Multinomial Logistics Regression)模型,提供明確的歸納彙整。研究結果分為四群,分別是「返鄉返回工作群」、「次返鄉返回工作群」、「次區域旅次群」、「區域旅次群」。「返鄉返回工作群」在城際旅次(中長程)之運量占比則近三成(27.89%),居四群之冠。「區域旅次群」則擁有超過80%區域型旅次的運量占比,近似於平常日的運量分佈。而「次返鄉返回工作群」、「次區域旅次群」則介於兩者之間。
Besides two days off a week, there are eight national holidays as New Year ’s Day, Lunar year holidays, 228 Peace Memorial day, Ching Ming Festival, Labor Day, Dragon Boat Festival, Moon Festival and Double Tenth Day in sequence a year in Taiwan. Chinese New Year’s holidays are national six days or above. The rest have, most likely, three or four days off when to be coupled with the weekend. In the past, lacking of the research in ridership of the national holidays due to the difficulty in getting the relative data for analysis. Recently after obtaining all the ticketing data, to analyze and compare the traffic volume between weekend and the national holidays gains a conclusion as follows. The daily ridership for the national holidays is with more fluctuations comparing to the weekend. Obviously, to grab, accurately, the distribution of ridership is great help to find out the proper strategy. Hence, this article focuses on exploring the ridership of the national holidays. The previous research, mostly, aims at directly predicting the ridership for the various carriers of transportation such as bus, railway system including subway, train and high-speed rail and air transport. Or, analyze the factors which will affect the ridership. However, the objective of this research is to let the Taiwan Railway Administration can realize the possible ridership distribution of national holidays in advance and take action based on our outputs.
This article is using the ridership distribution table from Original County to Destination County, and utilizing a deep learning auto-encoder by dimensionality reduction method to extract the feature. Then, re-utilize K-means Clustering to have the similar holiday grouped and find out the feature of each group so as to establish “Multinomial Logistics Regression” model to provide the clear and proper induction. Then About the group one, the ridership which belong to the Regional trip occupies 63.96% of the average daily ridership. The ridership of Intercity Trip takes 27.89% (approximate to 30%), which is the greatest of the 4 groups. The fourth group owns exceeding 80% of ridership of Regional trip. It’s similar to the distribution of travel volume of weekday. The second and third groups are distributed between the first and the fourth groups.
誌謝 I
中文摘要 II
英文摘要 III
目錄 V
圖目錄 VIII
表目錄 IX
第一章 緒論 1
1.1 研究背景 1
1.2 動機及目的 1
1.3 研究流程與方法 1
第二章 文獻回顧 5
2.1 臺鐵營運現況 5
2.2 運量預測之相關研究 5
2.2.1 運用類神經網絡方法於運量預測 5
2.2.2 運用迴歸模式於運量預測 6
2.2.3 小結 7
2.3 分群分析之相關研究 7
2.4 影響軌道系統運量之因子 8
2.5 文獻回顧小結 9
第三章 資料敘述與分析 10
3.1 原始資料 10
3.1.1 運量資料(國定假日) 10
3.1.2 運量資料(一般週末) 11
3.1.3 其他資料(油價、降雨量、溫度) 12
3.2 資料描述 13
3.2.1 資料概況 13
3.2.2 敘述性統計描述 15
3.2.3 逐日運量資料描述(一般週末 & 國定假日) 17
3.3 資料整理 33
第四章 資料分群與迴歸模式 36
4.1 資料分群 36
4.1.1 深度學習自編碼(Deep Auto-encoder) 36
4.1.2 非監督式分群: K-平均演算法(K-means Clustering) 39
4.1.3 分群結果 40
4.1.4 討論 45
4.2 迴歸模式 59
4.2.1 多項邏輯斯迴歸(Multinomial Logistics Regression) 59
4.2.2 結果與討論 61
4.3 小結與資料驗證 66
4.3.1 小結 66
4.3.2 資料驗證 67
第五章 結論 68
5.1 研究彙整 68
5.2 未來建議 68
參考文獻 70
Bishop, C. M. (2006). Pattern recognition and machine learning. springer.
Cardozo, O. D., García-Palomares, J. C., & Gutiérrez, J. (2012). Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Applied Geography, 34, 548-558.
Cervero, R. (1994). Rail-oriented office development in California: how successful?. Transportation Quarterly, 48(1).
Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199-219.
Chu, X. (2004). Ridership Models at the Stop Level. Report BC137-31. National Center for Transit Research, Florida Department of Transportation, Tallahassee.
Currie, G., Ahern, A., & Delbosc, A. (2011). Exploring the drivers of light rail ridership: An empirical route level analysis of selected Australian, North American and European systems. Transportation, 38(3), 545-560.
Ewing, R., & Cervero, R. (2001). Travel and the built environment: a synthesis. Transportation research record, 1780(1), 87-114.
Gutiérrez, J., Cardozo, O. D., & García-Palomares, J. C. (2011). Transit ridership forecasting at station level: an approach based on distance-decay weighted regression. Journal of Transport Geography, 19(6), 1081-1092.
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504-507.
Keijer, M. J. N., & Rietveld, P. (2000). How do people get to the railway station? The Dutch experience. Transportation planning and technology, 23(3), 215-235.
Kuby, M., Barranda, A., & Upchurch, C. (2004). Factors influencing light-rail station boardings in the United States. Transportation Research Part A: Policy and Practice, 38(3), 223-247.
Loo, B. P., Chen, C., & Chan, E. T. (2010). Rail-based transit-oriented development: lessons from New York City and Hong Kong. Landscape and Urban Planning, 97(3), 202-212.
MacQueen, J. (1967, June). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, No. 14, pp. 281-297).
Nam, K., & Schaefer, T. (1995). Forecasting international airline passenger traffic using neural networks. The Logistics and Transportation Review, 31(3), 239-252.
Nam, K., Yi, J., & Prybutok, V. R. (1997). Predicting airline passenger volume. The Journal of Business Forecasting, 16(1), 14.
Steinbach, M., Ertöz, L., & Kumar, V. (2004). The challenges of clustering high dimensional data. In New directions in statistical physics (pp. 273-309). Springer, Berlin, Heidelberg.
Sung, H., & Oh, J. T. (2011). Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea. Cities, 28(1), 70-82.
Untermann, R. K. (1984). Accommodating the pedestrian: Adapting towns and neighbourhoods for walking and bicycling.
Walters, G., & Cervero, R. (2003). Forecasting transit demand in a fast growing corridor: The direct-ridership model approach. Fehrs and Peers Associates.
Xie, J., Girshick, R., & Farhadi, A. (2016, June). Unsupervised deep embedding for clustering analysis. In International conference on machine learning (pp. 478-487).
Yun, S. Y., Namkoong, S., Rho, J. H., Shin, S. W., & Choi, J. U. (1998). A performance evaluation of neural network models in traffic volume forecasting. Mathematical and Computer Modelling, 27(9-11), 293-310.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文
 
無相關期刊
 
無相關點閱論文
 
系統版面圖檔 系統版面圖檔