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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 許聿廷 | |
dc.contributor.author | Chia-Wei Hsu | en |
dc.contributor.author | 許家維 | zh_TW |
dc.date.accessioned | 2021-05-12T09:32:58Z | - |
dc.date.available | 2018-08-09 | |
dc.date.available | 2021-05-12T09:32:58Z | - |
dc.date.copyright | 2018-08-09 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-07 | |
dc.identifier.citation | REFERENCE
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[26] 林豐博、曾平毅、林國顯、蘇振維、張瓊文、鄭嘉盈、呂怡青、劉國慶、陳昭堯、王怡方。(2011)。”2011年臺灣公路容量手冊”,交通部運輸研究所。 [27] 胡英浩。(2012)。”使用GPS軌跡資料推估遊客空間分布–以野柳地質公園為例”,碩士論文,國立台灣大學地理環境資源研究所。 [28] 張正鼎。(2012)。”整合資料探勘與核密度估計技術於人體多重疾病共同生理指標之評估”,博士論文,元智大學工業工程管理研究所。 [29] 葉志宏。(2016)。”臺北市政府交通局三橫三縱自行車路網施工影響說明”,台北市交通工程管制處。 [30] 余孟冠。(2009)。”關於核密度函數估計之研究”,碩士論文,國立清華大學數學研究所。 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/handle/123456789/1128 | - |
dc.description.abstract | 對於交通管理而言,道路交通狀況的監測是很重要的課題,而如何監控和避免道路壅塞是交通管理最主要關心的面向之一。壅塞發生的原因包括自用車使用率提升使得車流量增加、道路系統容量不足或設計不良,以及事故或施工導致車道容量縮減等。而壅塞的影響層面則包括通勤時間的增長、駕駛人情緒上的負面衝擊、生活品質的降低以及在緊急應變上的潛在威脅。因此,深入了解這些壅塞所影響的範圍與層面,並找出道路系統中可能的瓶頸處,提供可靠資訊予用路人與交通管理單位作為參考,將可協助對於預防壅塞更積極的作為,並在交通管理策略上做出改善。
過去文獻中,針對不同資料來源所進行的交通狀態與事件偵測、壅塞擴散模式與資料視覺化皆有相關研究與討論,本研究將基於高解析度之車輛偵測器資料分析,將資料處理、模式辨識與視覺化三個區塊一併納入,建立一個完整的壅塞分析架構。本研究首先進行原始車輛偵測器資料的清理,處理資料中缺失與錯誤的問題,並篩選出後續分析所需要的特定資料,並根據與不同的壅塞界定門檻值,定義出壅塞發生的時空位置。本研究以車輛偵測器的實際位置,地圖圖資與鄰接矩陣的觀念建立路網。接著使用調整的核密度推估方法進行壅塞擴散模式的分析,在不同時間段進行案例分析,探討一階、二階鄰接以及不同轉向的上游路段受下游壅塞源頭影響的情形,歸納出可供參考的壅塞擴散模式和推估原則,並透過視覺化呈現交通車流資料的變化特性。 藉由案例分析的不同情境設定,與不同尺度的視覺化結果,將可以從圖面上觀察到整體路網當中有較高機率發生壅塞的位置,以及各源頭路段發生壅塞之後,傳遞的方向與影響程度。在路網中大部分的路段上觀察到的現象符合一些一般性的原則,上游路段受到壅塞的影響,一階鄰接路段大於二階鄰接路段;另外在相同鄰接度的情形下,直行進入下游路段大於左轉進入下游路段,左轉進入下游路段又大於右轉進入下游路段。 | zh_TW |
dc.description.abstract | The monitoring of roadway traffic conditions is critical for traffic management, where the detection of traffic congestion is one of major concerns. Traffic congestion may have various causes, including the increase of traffic volume due to higher private vehicle usage, inappropriate design or lack of capacity of road network and layout changes on the road segment owing to non-recurrent incidents such as traffic accidents or construction work. Traffic congestion may lead to the rise of commuting time, negative impact of driver physiology, lower quality of life and potential hazard on emergency response could be the impact of traffic congestion. Hence, further understanding of how traffic congestion was formed, propagated and dissipates, and identifying possible bottlenecks are critical for overall traffic management. Based on the relevant knowledge, it is possible to provide drivers and traffic management agencies reliable information to more actively prevent traffic congestion and thereby improve the quality of traffic management strategies.
In the current literature, traffic state detection, congestion propagation pattern and traffic data visualization have been studied and discussed, respectively. Based on high-resolution VD data, this study integrates the consideration of data processing, pattern recognition and visualization to develop a data analysis framework for better understanding of traffic congestion in an urban network. Data cleaning is first performed to deal with the missing and erroneous data, and then a specific data set needed for further analysis is extracted. Based on different thresholds of congestion detection, the spatio-temporal locations of congestion occurrences are recorded. The network structure is constructed based on the actual coordinate of VDs, map information and the concept of the adjacent matrix. An adjusted kernel density estimation approach is proposed and applied to case studies, in order to investigate the effects of congestion propagation on road segments with different characteristics in terms of connection type and adjacency. Finally, a general principle describing the propagation pattern of traffic congestion is concluded and presented through data visualization. Based on different scenarios for the case study and visualization result under different scales, locations with higher probability to be congested in the whole network and the propagation direction and impact after congestion occurred can be observed. Most of the road segments within the network follows some general principles. In terms of the impact on upstream road segments, road segments of the 1st order adjacency receive larger impact than road segments of the 2nd order adjacency. In addition, for road segments of the same order adjacency, which goes straight to the congested road segment is affected most by the source. The segment with a left turn comes second and the segment with a right turn receives the least influence. | en |
dc.description.provenance | Made available in DSpace on 2021-05-12T09:32:58Z (GMT). No. of bitstreams: 1 ntu-107-R05521510-1.pdf: 6070238 bytes, checksum: 8dc2a7723417b684efca799d530b6421 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | CONTENTS
口試委員審定書 i 誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS vi LIST OF FIGURES viii LIST OF TABLES x Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Objectives 6 1.3 Thesis Organization 7 Chapter 2 Literature Review 9 2.1 Traffic State and Event Detection 9 2.2 Propagation Patterns 11 2.3 Kernel Density Estimation and Applications 12 2.3.1 Standard Kernel Density Estimation 14 2.3.2 Planar Kernel Density Estimation 16 2.3.3 Network Kernel Density Estimation 17 2.4 Summary of Literature Review 18 Chapter 3 Methodology 20 3.1 Adjusted Network Kernel Density Estimation 20 3.2 Data Description 21 3.2.1 Network Structure 21 3.2.2 VD data processing 25 3.3 Analysis Procedure 29 Chapter 4 Case Study 33 4.1 Descriptions of the Case Study 33 4.2 Result Analysis 36 4.2.1 Scenario 1: 2015/12/28~2015/12/31 38 4.2.2 Scenario 2: 2016/4/18~2016/4/22 46 4.3 Summary of Insights from Case Study 54 Chapter 5 Conclusions and Future Work 56 5.1 Conclusions 56 5.2 Future Work 58 REFERENCE 60 | |
dc.language.iso | en | |
dc.title | 通過車輛偵測器資料分析與視覺化探索壅塞擴散模式 | zh_TW |
dc.title | Exploring the Propagation Pattern of Traffic Congestion
Through Analyzing and Visualizing Vehicle Detector Data | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳柏華,郭佩棻 | |
dc.subject.keyword | 車輛偵測器,核密度估計,交通狀態偵測,壅塞擴散模式,視覺化, | zh_TW |
dc.subject.keyword | congestion propagation pattern,kernel density estimation,traffic state detection,vehicle detectors,visualization, | en |
dc.relation.page | 64 | |
dc.identifier.doi | 10.6342/NTU201802653 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2018-08-07 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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