車隊運動型態與時空圖理論應用於號誌化幹道即時旅行時間估計－以固定式偵測器資料為基礎

Travel Time Estimation on Signalized Arterials Based on Time-Space Diagram and Platoon Movement Pattern Using Detector Data

申瑋琦(Wei-Chi Shen)

指導教授 ： 劉士仙

淡江大學/管理學院/運輸管理學系碩士班/碩士(2005年)

https://doi.org/10.6846/TKU.2005.00199

摘要

號誌化幹道旅行時間估計因受號誌干擾估計較困難，現行處理方式大多視路段與路口為獨立變數分開處理，最後加總分析；若以時空圖軌跡來看，可將車輛推進分為正常行駛區、減速區、等候區三種狀態來處理，對於車輛到達率部份，本研究提出二元決策模式來進行計算。根據此三階段界面定義相互影響關係，構建即時旅行時間估計模式。
本研究以大度路為實驗路網，利用微波雷達偵測器蒐集交通參數資料，嘗試以巨觀車隊運動型態為基礎，將所計算的旅行時間與錄影所觀測旅行時間進行比較，準確率可達90%內，所估計之即時旅行時間可反映現實狀況車隊運動型態，提高推估交通資訊的正確性。

關鍵字

幹道即時旅行時間估計 ； 時空圖理論 ； 微波雷達式偵測器

並列摘要

Travel time estimation on signalized arterials is complicated because of hard to isolate the erroneous noise from outside of the system. To simplify the problem, current research regards the link travel time and intersection delay as separated independent variables, and summation of them representing the total travel time on link. Based on the trajectory of time-space diagram, the vehicle movement on link could be explicitly expressed as three different discrete transition states, named as free flow travel time, approaching delay, and intersection delay. Applying the detector data, vehicle flow rate on intersection with a binary choice can be defined. Therefore, three transition states can be explained the interfaces and further estimate the real time on a general signaled arterials.
This study estimates the real travel time on arterials, Dai-Du road, from macroscopic platoon movement pattern using microwave detector data. Results are compared with true values from video. The accuracy of the proposed model is higher than 90% and it adequately reflects the real-time travel time within tolerant error in applications.

並列關鍵字

Travel Time Estimation ； Time-Space Diagram ； SmartSensor

參考文獻

28. Dailey, D. J. “Travel Time Estimates Using a Series of Single Loop Volume and Occupancy Measurements”, presentation at the 76th Annual Meeting of Transportation Research Board, Washington, D.C., U.S.A., 1997。

32. Lin, W. H., Kulkarni A., and Mirchandani P., “Short-Term Arterial Travel Time Prediction for Advanced Traveler Information Systems”, Presented at the 11th World Congress on Intelligent Transportation Systems, 8:143-145, 2004。

33. Lawrence A.Klein, “ Sensor Technologies and Data Requirements for ITS”, 2001。

40. Suzuki, H. et al., ”A Neural-Kalman Filter for Dynamic Estimation of Origin-Destination(O-D) Travel Time and Flow on a Long Freeway Corridor,”Prepared for Presentation at the 79th Transportation Research Board Annual Meeting, Washington, D. C., 2000。

參考文獻