考量停等車隊型態之混合車流疏解特性分析

Queue discharging characteristics in mixed traffic considering the stopping queue patterns

黃維皓(Wei-Hao Huang)

指導教授 ： 黃家耀

國立交通大學/管理學院/運輸與物流管理學系/碩士(2018年)

摘要

在開發中國家常可觀察到汽機車交叉混合的情景，在臺灣，大量的機車導致路況更為壅擠同時增加了運具間互動的複雜性。為了估計號誌化路口的容量，混合車流的疏解分析相當關鍵。疏解時間之估計誤差的降低可以改善疏解率的估計。本研究旨在了解混合車流下的疏解行為並建立混合車流疏解時間模型加以分析。

綜觀過去研究與文獻探討疏解時間與其影響因子的關係，以車輛數、車隊長度、路口和道路幾何型態以及車輛間的相互影響作用為影響疏解時間之重要因素。然而，較少研究指出車隊排列上之失序混雜程度對車隊疏解上的影響；臺灣公路容量手冊在計算飽和流量時認為混合車隊會因停等區的設置而沒有更多延誤產生，與實際混合停等車隊交錯排列之情形相左。本研究因而以停等車隊型態熵 (Queue Pattern Entropy, QPE) 的概念，欲闡述車輛於車流疏解下相對位置之關係，其功能在於同時考量車隊之停等型態以及不同車輛組成下所描述的停等車隊混合程度。

本研究的資料來源是採用無人機進行影片錄製，此方法提供了本研究能一窺混合車流互動的機會。透過收集到的微觀車流軌跡資料發現，車輛之橫向位移程度以及變動的停等車隊疏解順序等特性揭示混合車流不同於一般汽車車流的疏解行為。本研究採用迴歸分析來建立混合車流的疏解模型。並透過線性與非線性的模式結構校估模式對比並無考量車隊停等型態的模式，結果顯示兩模式皆能有效地敘述混合車流的疏解。與過去研究所提出的模式相比，本研究變數使用之考量QPE程度的模式可以有效地描述車隊停等型態，並減少疏解時間的估計誤差。

關鍵字

疏解時間預測 ； 車隊停等型態 ； 混合車流 ； 疏解特性

並列摘要

The mixed-traffic condition is common in most of the developing countries. In Taiwan, the large ratio of scooters in the mixed traffic increases the complexity of interactions between vehicles. To understand the capacity of signalised intersections, the discharge characteristics under the mixed-traffic condition is critical. The less bias on the estimation of discharge time can improve the estimation of discharge flow rate of intersections. The aim of this study is to understand the discharge behaviour of vehicles under the mixed-flow traffic condition and formulate a model to estimate the discharge time.

Past research discovered the relationship between the discharge time and the affecting factors, such as numbers of the vehicles, length of the queue, geometry design and layout, and the interactions between vehicles. However, there were limited studies that investigated the effect of order and arrangement of vehicles in a queue to the discharge process. In Taiwan’s Highway Capacity Manual, it is assumed that scooters concentrate at the scooter-waiting zone and their discharge do not affect the vehicles behind. However, in observations, scooters and other vehicles may mix up in the queue. This study proposed a new factor, Queue Pattern Entropy (QPE), which can describe the vehicle stopping sequence and queue pattern formed by different vehicle compositions.

Microscopic vehicle trajectory data collected from Unmanned Aerial Vehicle (UAV) is used in this study to gain insights into the interactions between vehicles. The dataset allowed us to observe traffic characteristics such as lateral movement, discharge order, and discharge times of the mixed traffic flow. Furthermore, a regression analysis is proposed to construct a discharge time estimation model under mixed-traffic condition. Linear and non-linear structure has been calibrated and compared to the model without QPE. Both forms of the model shows that the QPE is beneficial and superior to the base model. Compared with previous models in the literature, our model with QPE can effectively describe the queue pattern and attain less bias on the discharge time estimation.

並列關鍵字

discharge time estimation ； queue pattern ； mixed traffic flow ； discharge characteristics

參考文獻

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