臺灣博碩士論文加值系統

臺北轉運站位居臺北市之交通樞紐點，高鐵、臺鐵、捷運與國道客運等乘客轉運於此，亦是臺灣西部地區與東部地區之交通轉運樞紐之一。為服務龐大客運班次，且受限於土地資源，臺北轉運站為臺灣第一座多樓層客運轉運站。由於立體站體設計，有別於單一月台層轉運站，服務績效不只在於人(乘客)之乘運效率，車輛於站內之營運績效對於整體系統服務效率影響更為顯著。

營運至今，尖峰時段常因車流交織問題出現壅塞，其中以位於3樓之承德出口較為嚴重，Wei et al. (2011) 對此提出號誌設計模式，根據車流特性(紓解車速、車間距、路口寬度等)以及不同時段之流量特性，進行號誌時制設計之初步探討。然，臺北轉運站之車輛到離站，並非完全依循既定時刻表，車輛到站時間具有不確定性，預設之號誌時制績效仍有改善空間。

因此，本研究嘗試利用站內現有之RFID車輛監測/監控系統與類神經網路之預測功能，預測承德出口於下一個時間區間之交通需求特性(包含到達車流量與轉向比例)，並以轉向比例修正既有號誌設計模式之容量參數，構建臺北轉運站內部動態號誌控制模式。本研究亦考量未來路線班次調整或其他可能影響交通特性之外部因素，設計類神經網路持續自我訓練與學習機制，使具有調適場站運轉狀況之功能，根據本研究前測結果，服務績效良好。

Transit systems are solutions for the heavy travel demand problems of high-density areas, which play important roles in congestion mitigation, energy conservation and pollutant reduction. Improving bus arrival and departure on-time performance is a critical concern in reducing users’ wait and transfer time. Unlike a single-platform terminal, bus flow interruption in a multi-level-platform terminal could significantly reduce the service quality and deteriorate environmental condition.

Significant congestion has been experienced during peak periods at a T-junction in the multi-platform bus terminal, Taipei Bus Station, recently launched in the heart of Taipei metropolitan area. Based on the findings for this issue of an analytical signal control model developed by Wei et al. (2011), a traffic demand forecasting model is needed to upgrade the existing pre-timed control strategy to dynamic control level. The artificial neural network approach is employed for constructing the demand forecasting model taking into account the relevant traffic flow information provided by the RFID readers embedded in the terminal monitoring systems in this research.

Considering time-varying demand, this study developed an adaptive signal control model for managing bus traffic in Taipei Bus Station. An off-line operational performance is examined with historical data, followed by on-line preliminary testing by new data. In the case study, the proposed model has demonstrated itself very efficient in reducing congestion within the terminal.

CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Brief Introduction of TBS 3
1.3 Problem Statement and Research Objectives 5
1.4 Research Procedures 7
CHAPTER 2 LITERATURE REVIEW 10
2.1 Recent Studies on Taipei Bus Station (TBS) 10
2.1.1 Capacity and Level of Service (LOS) 10
2.1.2 Signal Control for Inner Junction in TBS – Time-Dependent Fix Control Plan 11
2.2 Adaptive Signal Control Methods 12
2.2.1 SCATS 13
2.2.2 SCOOT 14
2.2.3 RHODES 14
2.2.4 OPAC 16
2.2.5 ARTC 18
2.3 Artificial Neural Networks Applications in Transportation 20
2.3.1 Artificial Neural Networks 20
2.3.2 Brief Introduction of Relevant Applications 22
2.4 Comments 27
CHAPTER 3 MODEL CONSTRUCTION ＆ EVALUATION CRITERIA 30
3.1 Signal Control Model – Analytical Modeling Approach 31
3.1.1 Discharge Time Functions 32
3.1.2 Delay Functions 36
3.2 Traffic Forecasting Model – Artificial Neural Network (ANN) Approach 37
3.2.1 Forecasting Model Construction 38
3.2.2 Back Propagation Network (BPN) Algorithm of ANNs 39
3.3 Model Evaluation 44
CHAPTER 4 DATA COLLECTION ＆ ANALYSIS 46
4.1 Flow Rate 46
4.2 Turning Factor 51
CHAPTER 5 MODLE CALIBRATION ＆ OFF-LINE EVALUATION 54
5.1 Pre-set Timing Plans 54
5.2 Input Variable Selection for ANN Model 58
5.3 ANN Model Structure 58
5.3.1 Basic Model Calibration 60
5.3.2 Advanced Model Calibration 65
5.4 ANN Model Assessments 71
CHAPTER 6 PRELIMILARY TESTING FOR ON-LINE APPLICATION 72
6.1 Procedure of ON-LINE Evaluation 72
6.2 The Programs of Preliminary Testing 75
6.2.1 Testing Period 75
6.2.2 Sample Size 75
6.2.3 Program and Software 75
6.2.4 Retraining tolerance setting 75
6.3 Results of Preliminary Testing 76
6.4 Sensitivity Analysis 80
6.4.1 Relaxing Retraining Tolerance during Non-Peak Hours 80
6.4.2 Extreme Volume Testing 81
CHAPTER 7 CONCLUSIONS ＆ RECOMMENDATIONS 82
7.1 Conclusions 82
7.2 Recommendations 84

REFERENCES R-1
Appendix 1 Flow Rates in a Typical Day A-1
Appendix 2 Pre-set Timing Plans A-7

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