Deep Auto-encoder for Analyzing the Ridership of Taiwan Railways Administration on National Holidays

• Main Authors: Wen-Yu Lee, 李文宇
• Other Authors: Yu-Ting Hsu
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/74q9x7

碩士 === 國立臺灣大學 === 土木工程學研究所 === 107 === 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.