利用多層次成長模式探討不同建成環境下的都市交通壅塞過程

Abstract

交通壅塞之過程是道路交通領域中的一大議題，它指的不單純是單一時間面下的壅塞，而是包含交通壅塞從形成、傳播、到消散等的過程，了解壅塞的變化機制與影響範圍之後可幫助決策者管理與改善交通壅塞情形。多數探討建成環境對壅塞的影響之研究主要集中於探討一段時間內 (如平日、假日、離峰、尖峰) 的壅塞，缺少討論壅塞在變化過程時的研究，忽略了建成環境的影響效果在不同時間時皆不同；而少數有探討壅塞過程的研究則受限於電腦運算效率，僅侷限於小尺度的理想網絡、缺少大尺度的都市研究。 本研究提出一個實證分析中分析交通壅塞過程的新方法，透過二次成長模型來分析壅塞的擴散速度及不同時間時建成環境對壅塞的影響效果。本研究使用 2017 年 1 月 10 日星期二與 1 月 21 日星期六12:00-19:00由臺北市交通管制處所提供的車輛偵測器 (Vehicle Detector, VD) 資料，並以車道平均速度來衡量交通壅塞程度。由於車速會受到道路上下游之車速影響、具有空間相依性，故使用 Max-P 分群方法將 VD 依照其位置及每小時之平均速度來分群，並將此分群結果作為空間分析單位。依變數為每群的壅塞程度，自變數部分則包括群周圍 500 公尺環域範圍內的建成環境及時間變數，結果以時間函數的方式來描述建成環境對交通壅塞的非線性成長過程，並以圖的方式視覺化呈現各建成環境變數在不同時段對壅塞的影響效果。

Traffic congestion propagation is a big issue in road transportation systems. It contains not only the congestion condition under one certain time but also the formation, transmission, propagation speed, affecting areas and other factors of congestion. Understanding these characteristics can help people manage and reduce traffic congestion. Numerous researches have studied how built environment affect traffic congestion, but most research focused on the congestion in a specific time period, like weekday, weekend, off peak and peak. Ho¬¬wever, it is not clear that how built environment at different times may affect congestion propagation. Limited by computer computing efficiency, the few research on congestion propagation has been mainly based on microsimulations of link-level dynamics, lack of research in large urban networks. This study proposed a new empirical method to analysis traffic congestion propagation. Using quadratic growth model, the relationship between congestion propagation and built environment was analyzed. A data set of vehicle detector (VD) data of 2017/1/10 (Tue.) and 1/21 (Sat.) 12:00-19:00 from Bureau of Traffic Engineering, Taipei City Government is used to access traffic conditions. A linear growth model is utilized to analyze the spatial-temporal traffic data. In order to deal with the dependency among the data, each VD is firstly divided into different clusters by Max-P clustering. Then the ratio of the congested VDs within each cluster is used as the dependent variable, which represents the level of congestion. Independent variables include the built environment factors in a 500-meter radius of each cluster, which can be divided into traffic-related factors and land use, and time variable. The results use a function of time and figures to show the over-time relationship among congestion and the build environment factors.