none｜國立中央大學博碩士論文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	陸亦福 Lutfi Fanani
論文名稱：	Bus Arrival Prediction - to Ensure Users Not to Miss the Bus (Preliminary Study based on Bus Line 243 Taipei)
指導教授：	梁德容 Deron Liang Achmad Basuki Achmad Basuki
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 資訊工程學系 Department of Computer Science & Information Engineering
論文出版年：	2014
畢業學年度：	102
語文別：	英文
論文頁數：	70
中文關鍵詞：	預測公車到站時間、等待時間、常態分布
外文關鍵詞：	Bus Arrival Prediction, Waiting Time, Normal Distribution
相關次數：	點閱：8 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

FANANI, LUTFI. 公車到站時間預測 - 目的確保使用者不錯過公車 (基於台北公車路線243的初步研究)
對於搭乘公車的人而言，公車到站時間是很重要的，而且這個時間會被很多因素所影響，例如: 等紅綠燈、交通擁塞以及天氣狀況等。這些因素都會影響到公車到站時間，進而延長乘客的等候時間，所以提供乘客精準的時間有助於乘客下決定以及減少在公車站等待公車的時間。本篇論文中提出一種常態分佈的方法並使用行車資料中的隨機變數針對台北的243公車進行預測，而我們所使用的資料來自於台北公車資料庫。
我們使用常態分佈的方式對公車的到站時間進行預測並確保使用者不會錯過公車，我們也將這個結果與已存在的方法進行比較。使用者使用我們所建議的方法在尖峰時間不會錯過公車的機率是93%，而一般時間是85%。已存在的方法在尖峰時間是65%，而一般時間是70%。經過我們的實驗證實我們所建議的方法比已存在的方法可以更加準確地預測公車到站時間。
關鍵字: 預測公車到站時間、等待時間、常態分布

FANANI, LUTFI. Bus Arrival Prediction – to Ensure Users Not to Miss the Bus (Preliminary Study based on Bus Line 243 Taipei).
The bus arrival time is the primary information for most city transport travelers. It is influenced by stochastic variation in number of factors, (e.g. intersection delay, traffic congestion, and weather condition) resulting in buses to deviate from the predetermined schedule and lengthening of passenger waiting times for buses. Providing passengers with an accurate information system of bus arrival times can reduce passenger waiting times. In this thesis, we used the normal distribution method to the random of travel times data in a bus line number 243 in Taipei area. In developing the models, data were collected from Taipei Bus Company. A normal distribution method used for predicting the bus arrival time in bus stop to ensure users not to miss the bus, and compare the result with the existing application. The result of our experiment showed that our proposed method has a better prediction than existing application, with the probability user not to miss the bus in peak time is 93% and in normal time is 85%, greater than from the existing application with the 65% probability in peak time, and 70% in normal time.

TABLE OF CONTENTS
摘 要 ......................................................................................................................................... i
ABSTRACT ............................................................................................................................... ii
ACKNOWLEDGEMENTS ...................................................................................................... iii
TABLE OF CONTENTS .......................................................................................................... iv
LIST OF FIGURES ................................................................................................................... vi
LIST OF TABLES .................................................................................................................. viii
CHAPTER 1 ............................................................................................................................... 1
INTRODUCTION ...................................................................................................................... 1
1.1. Background .................................................................................................................. 1
1.2. Motivation .................................................................................................................... 1
1.3. Research Object ........................................................................................................... 6
1.4. Thesis Structure ........................................................................................................... 6
CHAPTER 2 ............................................................................................................................... 7
LITERATURE REVIEW ........................................................................................................... 7
2.1. Related Work ............................................................................................................... 7
2.1.1. Historical Data Based Model ................................................................................ 7
2.1.2. Regression Model ................................................................................................. 7
2.1.3. Time Series Model ............................................................................................... 8
2.2. Literature Review ........................................................................................................ 9
2.2.1. Random Variable .................................................................................................. 9
2.2.2. Normal Distribution ............................................................................................ 10
2.2.3. Cumulative Distribution Function ...................................................................... 12
2.3. Preliminary Analysis .................................................................................................. 13
2.3.1. Taipei Bus Company .......................................................................................... 13
2.3.2. Google Application ............................................................................................ 13
2.3.3. Travel Time ........................................................................................................ 13
2.3.4. Waiting Time ...................................................................................................... 14
2.3.5. Come Early and Come Late ............................................................................... 14
2.3.6. Peak Hour, Normal Hour .................................................................................... 14
CHAPTER 3 ............................................................................................................................. 15
METHODOLOGY ................................................................................................................... 15
3.1. Experiment System .................................................................................................... 15
3.2. Experiment Design .................................................................................................... 15
3.3. Data ............................................................................................................................ 15
3.3.1. Data Collection ................................................................................................... 17
3.3.2. Data Processing .................................................................................................. 18
3.4. Testing System ........................................................................................................... 20
CHAPTER 4 ............................................................................................................................. 24
EXPERIMENT AND RESULT ............................................................................................... 24
4.1. Arrival Prediction in Peak Hour ................................................................................ 24
4.1.1. Stop 7 to Stop 14 (S7, S14) .................................................................................. 24
4.1.2. Stop 8 to Stop 14 (S8, S14) .................................................................................. 26
4.1.3. Stop 9 to Stop 14 (S9, S14) .................................................................................. 28
4.1.4. Stop 10 to Stop 14 (S10, S14) ............................................................................... 30
4.1.5. Stop 11 to Stop 14 (S11, S14) ............................................................................... 32
4.1.6. Stop 12 to Stop 14 (S12, S14) ............................................................................... 34
4.2. Arrival Prediction in Normal Hour ............................................................................ 36
4.2.1. Stop 7 to Stop 14 (S7, S14) .................................................................................. 36
4.2.2. Stop 8 to Stop 14 (S8, S14) .................................................................................. 38
4.2.3. Stop 9 to Stop 14 (S9, S14) .................................................................................. 40
4.2.4. Stop 10 to Stop 14 (S10, S14) ............................................................................... 42
4.2.5. Stop 11 to Stop 14 (S11, S14) ............................................................................... 44
4.2.6. Stop 12 to Stop 14 (S12, S14) ............................................................................... 46
4.3. Comparison Result ..................................................................................................... 48
CHAPTER 5 ............................................................................................................................. 51
DISCUSSION ........................................................................................................................... 51
5.1. Weekend Condition ................................................................................................... 51
5.2. Dwell Time ................................................................................................................ 51
CHAPTER 6 ............................................................................................................................. 52
CONCLUSION AND FUTURE WORK ................................................................................. 52
6.1. Conclusion ................................................................................................................. 52
6.2. Future Work ............................................................................................................... 52
REFERENCES ......................................................................................................................... 53
                                

REFERENCES
[1] Corpuz, Grace. ”Public Transport or Private Vehicle: Factors that Impact on Mode Choice”. Australasian Transport Research Forum. Australia. 2006.
[2] Cheng, Shaowu. Liu, Baoyi. “ Bus Arrival Time Prediction Model Based on APC Data”. The sixth advantage forum on Transportation of China. China. 2010.
[3] Jeong, Ranhee. Rillet, Laurence. “Bus Arrival Time Prediction Using Artificial Neural Network Model”. IEEE Intelligent Transportation System Conference. USA. 2004.
[4] Hendra Brata, Adam. “Thesis: Software Development of Automatic Data Collector”. National Central University. Taiwan. 2014.
[5] Kebede Gurmu, Zegeye. “A Dynamic Prediction of Travel Time for Transit Vehicles in Brazil Using GPS Data”. University of Twente Publications. Netherlands. 2010.
[6] Chien, S.I.J., Ding, Y., and Wei, C. “Dynamic Bus Arrival Time Prediction with Artificial Neural Networks”. Journal of Transportation Engineering. 2002.
[7] Zhou, Pengfei., Zheng, Yuanqing. “How Long to Wait?: Predicting Bus Arrival Time with Mobile Phone Based Participatory Sensing”. Nanyang Technological University Publications. Singapore. 2012.
[8] Bishop, Christoper M,. “Patern Recognition and Machine Learning”. Springer Science and Business Media. Singapore. 2006.
[9] Litman, Todd,. “Evaluating Public Transit as an Energy Conservation and Emission Reduction Strategy”. Victoria Transport Policy Institute Publication. 2012.
[10] Neslehova, Johanna. “On Rank Correlation Measures for Non-continuous Random Variables”. Journal of Multivariate Analysis. 2006.
[11] Mishalani, Rabi. G., McCord, Mark. “Passenger Waiting Time Perception at Bus Stop: Empirical Result and Impact on Evaluating Real Time Bus Arrival Information”. Journal of Publict Transportation. 2006.
[12] Davenport, W. ; Root, W. “An Introduction to the Theory of Random Signals and Noise”. Willey-IEEE Press E-book Chapters. 1987.
[13] Taipei Times News. “Taipei Introduces Direction Stickers for Foreign Tourist”. Available at:http://www.taipeitimes.com/News/taiwan/archives/2013/04/19/2003560119
[14] Taipei City Public Transportation Office. “Taipei E-Bus System”. Available at: http://www.e-bus.taipei.gov.tw/new/english/en_index_6_1.aspx
[15] Taipei Public Bus. “What is The Taipei Bus?”. Available at: http://guidetotaipei.com/article/taipei-public-bus
[16] 165284 Taipei Bus Information and Transit System. Available at: http://www.5284.com.tw/Dybus.aspx?Lang=En
[17] Wikipedia. “Normal Distribution”. Available at: http://en.wikipedia.org/wiki/Normal_distribution
[18] Wikipedia. “Random Variable”. Available at: http://en.wikipedia.org/wiki/Random_variable
[19] Wikipedia. “Rush Hour”. Available at: http://en.wikipedia.org/wiki/Rush_hour
[20] Google Maps. “Get Direction Feature”. Available at: http://www.maps.google.com.tw
[21] Bus Route Planner. “The Existing Application”. Unpublished.
[22] Mathisfun.com. “Random Variables”. Available at: http://www.mathsisfun.com/data/random-variables.html
[23] Answers.com. “Normal Distribution”. Available at: http://www.answers.com/topic/normal-distribution
[24] Engineering Statistics Handbook. “Cumulative Distribution Function”. Available at: http://www.itl.nist.gov/div898/handbook/eda/section3/eda362.htm
[25] Meng, Qiang., Qu, Xiaobo. “Bus Dwell Time Estimation at Bus Bays: A Probabilistic Approach”. National University of Singapore. 2001.
[26] Zang, Jian., Yan, Ling., “Study of the Prediction Model of Bus Arrival Time”. IEEE Management and Service Science Conference. 2009.

簡易檢索 / 詳目顯示

相關論文