Exploring Smartcard and Data Mining to Improve Bus Operation Strategies

碩士 === 中華大學 === 運輸科技與物流管理學系碩士班 === 94 === In the past, the researches about bus operation have had the same shortage, which was always without complete and reliable OD Table. Fortunately, after the popularity of Taipei Smartcard, the data of rider pay can be recorded by Smartcard at the same time. G...

Full description

Bibliographic Details
Main Authors: Shih-Chun Chiu, 邱詩淳
Other Authors: Hsiang-Sheng Lin
Format: Others
Language:zh-TW
Published: 2006
Online Access:http://ndltd.ncl.edu.tw/handle/03279307704703251563
id ndltd-TW-094CHPI0425004
record_format oai_dc
spelling ndltd-TW-094CHPI04250042015-10-13T10:38:07Z http://ndltd.ncl.edu.tw/handle/03279307704703251563 Exploring Smartcard and Data Mining to Improve Bus Operation Strategies 運用悠遊卡及資料探勘求解公車營運改善方案 Shih-Chun Chiu 邱詩淳 碩士 中華大學 運輸科技與物流管理學系碩士班 94 In the past, the researches about bus operation have had the same shortage, which was always without complete and reliable OD Table. Fortunately, after the popularity of Taipei Smartcard, the data of rider pay can be recorded by Smartcard at the same time. Gradually, the public transportation has become a large database industry, which has many useful concealed information. In addition, data mining is also a good technique to analyze the stored data in large databases to discover potential information and knowledge. This research constructs real passenger OD Table by Taipei Smartcard System’s data and Taipei County e-bus System’s data. As well as explore real OD Table to generate bus operation strategies. This research discusses the short-turn service route and express service route. It applied Data Mining technology about clustering and association rules to figure out optimal short-turn service route and optimal express service route, with the objective to save maximum the sum of operator’s cost(including traveling time cost and distance cost)and passengers’ travel time cost(including in-vehicle time cost and waiting time cost). A case study by 802 route shows an optimal condition. By this research, the result shows that the best improving effect of short-turn service is the afternoon peak time period, and the best improving effect of express service is the morning peak time period. It also reflects that operators should base on passengers’ demand to adjust their operational strategies. Overall, the effect of short-turn service is better than express service. However, the performance of optimal short-turn service route solution surpasses the present short-turn service. By the sensitive analysis, excluding from the cost of passenger’s waiting time, we could save more total cost if other factors’ costs arise. It implies that providing customized service not only the passengers may get more suitable bus service but also the operator may save more cost. Hsiang-Sheng Lin 林祥生 2006 學位論文 ; thesis 111 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 碩士 === 中華大學 === 運輸科技與物流管理學系碩士班 === 94 === In the past, the researches about bus operation have had the same shortage, which was always without complete and reliable OD Table. Fortunately, after the popularity of Taipei Smartcard, the data of rider pay can be recorded by Smartcard at the same time. Gradually, the public transportation has become a large database industry, which has many useful concealed information. In addition, data mining is also a good technique to analyze the stored data in large databases to discover potential information and knowledge. This research constructs real passenger OD Table by Taipei Smartcard System’s data and Taipei County e-bus System’s data. As well as explore real OD Table to generate bus operation strategies. This research discusses the short-turn service route and express service route. It applied Data Mining technology about clustering and association rules to figure out optimal short-turn service route and optimal express service route, with the objective to save maximum the sum of operator’s cost(including traveling time cost and distance cost)and passengers’ travel time cost(including in-vehicle time cost and waiting time cost). A case study by 802 route shows an optimal condition. By this research, the result shows that the best improving effect of short-turn service is the afternoon peak time period, and the best improving effect of express service is the morning peak time period. It also reflects that operators should base on passengers’ demand to adjust their operational strategies. Overall, the effect of short-turn service is better than express service. However, the performance of optimal short-turn service route solution surpasses the present short-turn service. By the sensitive analysis, excluding from the cost of passenger’s waiting time, we could save more total cost if other factors’ costs arise. It implies that providing customized service not only the passengers may get more suitable bus service but also the operator may save more cost.
author2 Hsiang-Sheng Lin
author_facet Hsiang-Sheng Lin
Shih-Chun Chiu
邱詩淳
author Shih-Chun Chiu
邱詩淳
spellingShingle Shih-Chun Chiu
邱詩淳
Exploring Smartcard and Data Mining to Improve Bus Operation Strategies
author_sort Shih-Chun Chiu
title Exploring Smartcard and Data Mining to Improve Bus Operation Strategies
title_short Exploring Smartcard and Data Mining to Improve Bus Operation Strategies
title_full Exploring Smartcard and Data Mining to Improve Bus Operation Strategies
title_fullStr Exploring Smartcard and Data Mining to Improve Bus Operation Strategies
title_full_unstemmed Exploring Smartcard and Data Mining to Improve Bus Operation Strategies
title_sort exploring smartcard and data mining to improve bus operation strategies
publishDate 2006
url http://ndltd.ncl.edu.tw/handle/03279307704703251563
work_keys_str_mv AT shihchunchiu exploringsmartcardanddataminingtoimprovebusoperationstrategies
AT qiūshīchún exploringsmartcardanddataminingtoimprovebusoperationstrategies
AT shihchunchiu yùnyòngyōuyóukǎjízīliàotànkānqiújiěgōngchēyíngyùngǎishànfāngàn
AT qiūshīchún yùnyòngyōuyóukǎjízīliàotànkānqiújiěgōngchēyíngyùngǎishànfāngàn
_version_ 1716832123255521280