(18.207.253.100) 您好!臺灣時間:2021/05/06 08:11
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:蔡政諺
研究生(外文):TSAI, CHENG-YEN
論文名稱:運用ETC開放資料進行雪山隧道交通管制成效之評估與分析
論文名稱(外文):Evaluation of Traffic Management Strategies for Hsuehshan Tunnel Based on ETC Open Data Analysis
指導教授:鄭王駿鄭王駿引用關係
指導教授(外文):CHENG, WANG-JIUNN
口試委員:吳宗禮劉龍龍鄭王駿
口試委員(外文):WU, TSUNG-LILIU, LUNG-LUNGCHENG, WANG-JIUNN
口試日期:2017-06-19
學位類別:碩士
校院名稱:實踐大學
系所名稱:資訊科技與管理學系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:57
中文關鍵詞:ETC開放資料高乘載管制匝道儀控管制差速管制
外文關鍵詞:ETC open datahigh-occupancy vehicle lane controlramp metering controldifferential lane speed control
相關次數:
  • 被引用被引用:2
  • 點閱點閱:356
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:95
  • 收藏至我的研究室書目清單書目收藏:0
當車流超出道路設計的運量時就會引起塞車的現象,再加上長隧道經常是兩地區間的交通要道與瓶頸,每逢尖峰時段無法避免將成為可預期性重複發生塞車的路段。雪山隧道每逢連續假期必定塞車即為一例。行政單位曾實施差別費率、大客車行駛路肩等管制措施,但皆未能有效改善隧道內車速過慢的問題。本論文將應用雪山隧道的ETC開放資料來分析塞車時段與駕駛行為之間的相關性。
因為隧道內禁止變換車道,一旦出現慢速車時,會嚴重降低後方跟車的速度與間距,形成隧道內走走停停的主因。也由於行車速度和間距與駕駛的開車習性直接相關聯,因此如何克服駕駛在隧道內維持正常的行車速度和合理的間距,方能緩解隧道內車速過慢的問題。再從ETC資料分析得知高乘載管制時段之車速遠高於匝道儀控管制時段的速度,平均是時速80比35,但其行經門架的車輛數幾近相同。因此,可推論出在高乘載時段共乘人數較多,駕駛為較有經驗的機率相對高於匝道儀控時段。
本論文將以此觀點提出以一線預約制高乘載一線匝道儀控之差速管制措施來提升隧道內之總運量與節省行經雪山隧道所花費的時間,使其能將維持較高速度與較短間距之駕駛集中行駛預約車道,並在不影響用路人的權益下,讓不習慣高速行駛之駕駛集中使用傳統匝道儀控之車道。在總運量不變的條件下,推估出新管制措施的效益為預約車道全日可17.4分鐘過雪山隧道,整體而言每人次平均可縮短約22分鐘的旅程,用以解決塞車的問題。

Traffic is congested if the flow of vehicles is over the road capacity. Since long tunnels usually become bottlenecks between two regions, expected recurring congestions are inevitable during traffic peak time. Taiwan's Hsueh Shan tunnel is a typical recurring congestion case during long weekend. In order to mitigate the traffic jam, many traffic management controls, such as "Discrimination of Pricing on Peak and Off-peak Periods" and "Bus on Shoulder," were tried without improving the tunnel congestion.
In this paper, the ETC open data of the tunnel will be used to analyze the correlation between traffic congestion and driving behavior. Because lane changes are prohibited in the tunnel, once a driver slows down, will seriously reduce the speed and spacing of the rear followers, this causes the stop-and-go traffic flows in the tunnel. But also due to the driving speed and following distance directly related to driving habits, how to overcome the driving in the tunnel to maintain normal driving speed and reasonable following distance become the key problem to mitigate the tunnel traffic congestion.
Further analysis revealed that the speed of the high-occupancy vehicle lane control period was much higher than the speed of ramp metering control period, with an average speed of 80 to 35, but both traffic throughputs were almost the same. Therefore, it can be deduced that the probability of skilled drivers selected for each high-occupancy vehicles is higher than that of drivers during the ramp metering control period.
In this paper, in order to improve the total traffic volume , flow rate and the cost time in the tunnel, we propose a new traffic control scheme by using the differential lane speed control, that is, one higher speed reservation lane for high-occupancy vehicle and the other much lower speed lane for ramp metering control. With the approach, not only we can collect the skilled drivers to use the reservation lane to maintain a higher speed with a shorter following distance, but also without affecting the unskilled drivers to use the other traditional ramp-controlled lane. Under the same traffic volume condition, the reserved vehicles and buses can pass the tunnel within 17.4 minutes and the overall average per person can shorten the journey about 22 minutes, to solve the congestion problem.

致謝 i
摘要 ii
Abstract iii
目錄 iv
表次 vi
圖次 vii
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 4
第二章 文獻探討 6
第一節 塞車時段的管制措施 6
第二節 駕駛行為對交通流量的影響 6
第三章 研究方法 8
第一節 研究方法與步驟 8
第二節 單線高乘載管制措施說明 9
第四章 現行與預約管制措施之比較與分析 14
第一節 雪山隧道北向南的車流資料分析 14
第二節 評估公式 17
第三節 各項措施效益評比 19
第五章 總結與未來方向 24
參考文獻 28
附錄 31
附錄A 爬蟲程式 32
附錄A-1 32
附錄A-2 33
附錄B scalc程式 34
附錄B-1 34
附錄B-2 36
附錄B-3 38
附錄B-4 40
附錄B-5 42
附錄B-6 44
附錄B-7 46
附錄B-8 48
附錄B-9 50
附錄C 資料圖形化程式 52
附錄C-1 52
附錄C-2 53
附錄D Apache Spark 安裝說明 54
附錄E Apache Zeppelin 安裝說明 56

一、中文
1.王銘亨 (2014),國內外 ETC 道路交通執法策略比較分析,1 0 3年道路交通安全與執法研討會論文集,頁 205-218 。
2.白舜豪、邱裕鈞 (2015) 道路交通事故衍生車輛延滯、能耗及污染排放之推估式,國立交通大學運輸與物流管理學研究所碩士論文。取自http://www.iot.gov.tw/file.ashx?id=e28fbfcb-4037-48a5-92f5-1f8ac004d54b
3.交通部臺灣區國道高速公路局 (2014),國道5號試辦「例假日差別收費」說明資料(Q&A),2017/1/25取自http//www.freeway.gov.tw/Upload/Html/20148796/%E5%9C%8B5%E8%A9%A6%E8%BE%A6%E5%B7%AE%E5%88%A5%E6%94%B6%E8%B2%BB%E8%AA%AA%E5%B8%96.pdf
4.交通部臺灣區國道高速公路局 (2016),國道5號「宜蘭-頭城北上路肩通行大客車(Bus On Shoulder,BOS)及主線儀控」範圍再延伸之說明資料(Q&A),2017/1/25取自http://www.freeway.gov.tw/Upload/Html/2016617138/1050503-BOS%E5%8F%8A%E4%B8%BB%E7%B7%9A%E5%84%80%E6%8E%A7%E7%AC%AC%E4%BA%8C%E9%9A%8E%E6%AE%B5%E4%B9%8B%E8%AA%AA%E6%98%8E%E8%B3%87%E6%96%99.pdf
5.交通部臺灣區國道高速公路局 (2016),疏導措施一覽表,2017/1/25取自http://www.freeway.gov.tw/Upload/Html/2016113127/page1.htm
6.交通部臺灣區國道高速公路局 (2017),交通部臺灣區國道高速公路局即時路況資訊,2017/1/25取自http://1968.freeway.gov.tw/
7.交通部臺灣區國道高速公路局全球資訊網 (2015),國道高速公路計程電子收費階段交通資料蒐集支援系統(Traffic Data Collection System, TDCS)使用手冊(V3.0),2017/1/25取自https//www.freeway.gov.tw/UserFiles/File/TIMCCC/TDCS%E4%BD%BF%E7%94%A8%E6%89%8B%E5%86%8A(tanfb) v3.0.pdf
8.郭逸、甘芝萁 (2015),「北捷10年前取消靠邊站 民眾多半不知道」,自由時報,2015/07/22,2017/1/25取自http://news.ltn.com.tw/news/life/paper/899830
9.行政院環境保護署國家溫室氣體登陸平台 (2013),溫室氣體係數管理表 6.0.1版,2017/1/25取自http://ghgregistry.epa.gov.tw/Information/Information_Pub.aspx?r_id=147
10.吳俊良、劉瑞賢、游子揚、許敦淵 (2012),精神疲勞駕駛事故跡證態樣-以高速公路交通事故為例,內政部警政署國道公路警察局自行研究報告。
11.陳建中、王羿涵、黃若玟、楊宗璟 (2010),高速公路通行費改採差別費率制度時對交通的影響-以名間服務區為例,九十九年道路交通安全與執法研討會論文集,351-376頁。
12.蘇弈、邱裕鈞 (2015) 差別費率策略下高速公路假日旅次之旅運選擇行為,國立交通大學運輸與物流管理學系研究所碩士論文。
13.羅建旺 (2017),「雪隧科技執法 6分鐘抓一件」,聯合新聞網,2017/6/16,2017/6/19取自 https://udn.com/news/story/7266/2527338
二、英文
1.Apache Spark「The Apache Software Foundation」Retrieved January 25, 2017 from http://spark.apache.org/
2.Apache Zeppelin 「Apache Software Foundation」Retrieved January 25, 2017 from https://zeppelin.apache.org/
3.Forster, M., Frank, R., Geria, M., & Engel, T. (2012), Improving highway traffic through partial velocity synchronization, 2012 IEEE Global Communications Conference (GLOBECOM), pp. 5573-5578.
4.Horn, B.K.P. (2013), Suppressing traffic flow instabilities, 16th International IEEE Conference on Intelligent Transportation Systems, ITSC 2013, Hague, pp. 13-20.
5.Kinsey, M.J., Galea, E.R., & Lawrence, P.J. (2011), Modelling evacuation using escalators: a London underground dataset, Springer-Verlag Berlin Heidelberg, pp. 385-399.
6.Lizee, G., & Fapojuwo, A.O. (2001), Highway traffic models for wireless networks, IEEE VTS 53rd Vehicular Technology Conference, Spring 2001, 4, pp. 2766-2770.
7.Ma, Y., Chowdhury, M., Sadek, A., & Jeihani, M. (2009), Real-time highway traffic condition assessment framework using vehicle–infrastructure integration (VII) with artificial intelligence (AI), IEEE Transactions on Intelligent Transportation Systems, 10(4), pp. 615-627.
8.Magtoto, J., & Roque, A. (2012), Real-time traffic data collection and dissemination from an Android Smartphone using proportional computation and freesim as a practical transportation system in Metro Manila, TENCON 2012 IEEE Region 10 Conference, pp. 1-5.
9.Messina, D. (2010), Why traffic flow slows when drivers hit tunnels. Retrieved January 25, 2017 from http://pilotonline.com/news/local/transportation/why-traffic-flow-slows-when-drivers-hit-tunnels/article_83519484-e462-5955-bc85-afb20bb653a5.html
10.Mian, R., Ghanbari, H., Zareian, S., Shtern, M., & Litoiu, M. (2014), A data platform for the highway traffic data, 2014 IEEE 8th International Symposium on the Maintenance and Evolution of Service-Oriented and Cloud-Based Systems, pp. 47-52.
11.Prathombutr, P., Pattara-atikom, W., & Chaovalit, P. (2012), Traffy social eyes: the traffic CCTV service platform, SRII Global Conference (SRII), 2012 Annual, pp. 869-874.
12.Scala. École Polytechnique Fédérale de Lausanne (EPFL). Retrieved January 25, 2017 from https://www.scala-lang.org/
13.Tan, Z., Xia, Y., Yang, Q., & Zhou, G. (2015), Adaptive fine pollutant discharge control for motor vehicles tunnels under traffic state transition, IET Intelligent Transport Systems, 9(8), pp. 783-791.
14.Work, D.B. Tossavainen, O.P., Blandin, S., Bayen, A.M., Iwuchukwu, T., & Tracton, K., (2008), An ensemble kalman filtering approach to highway traffic estimation using GPS enabled mobile devices, 47th IEEE Conference on Decision and Control, CDC 2008, Cancun, pp. 5062-5068.
15.Yishui, S., Wei, C., & Fang, L. (2015), Research on bottleneck effect of tunnel in steep uphill of city highway, 2015 International Conference on Transportation Information and Safety (ICTIS), pp. 133-137.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔