通過車輛偵測器資料分析與視覺化探索壅塞擴散模式

Exploring the Propagation Pattern of Traffic Congestion Through Analyzing and Visualizing Vehicle Detector Data

許家維(Chia-Wei Hsu)

指導教授 ： 許聿廷

國立臺灣大學/工學院/土木工程學研究所/碩士(2018年)

https://doi.org/10.6342/NTU201802653

摘要

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。

關鍵字

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

並列摘要

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.

並列關鍵字

congestion propagation pattern ； kernel density estimation ； traffic state detection ； vehicle detectors ； visualization

參考文獻

REFERENCE

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