以電信信令資料探討臺中都會區的住業失衡現象

Abstract

隨著都市空間結構逐漸轉向多核心發展，其分散通勤壓力與促進區域均衡之潛力日益受到重視。臺中市自20世紀末以來即朝多核心都市型態推進，並劃設三大都市核心範圍。然而，臺中市至今仍潛藏交通壅塞與通勤效率不彰等問題，顯示其住宅與就業之間的空間結構與實際通勤行為之間可能存在落差。過往研究多以超額通勤指標量化住業失衡的程度，並描述通勤效率，進一步檢視都會區內的通勤行為。本研究以臺中市為案例，運用具有起訖（Origin and Destination）交通流量記錄之電信信令資料，結合網路圖（Graph）之社區發現（Community Detection）演算法Infomap，依據圖節點之間的連結性劃分出九個具相對較高內部互動的通勤社區，並經地表狀態、都市計畫與通勤流視覺化結果比對後，界定為九大都市核心。進一步計算各核心之超額通勤指標，並結合通勤的特性，歸納為主要都市核心、次要都市核心、地方型核心、衛星市鎮與特殊類型核心等五種類型。結果顯示，主要都市核心雖住業失衡程度較高，卻展現相對較佳之通勤效率；次要都市核心與地方型核心之通勤效率與住業失衡程度居中，而衛星市鎮則呈現高度跨核心通勤依賴，使得通勤效率較不理想。此外，本研究亦透過多元迴歸模型分析通勤距離的影響因素，發現「與都市核心的距離」對多數核心類型具顯著正向影響，唯獨對於衛星市鎮呈現負向效果；另如「公車站點密度」、「土地混合使用」與「住宅機會密度」等變數，則在不同類型核心中展現出空間差異性影響。本研究顯示多核心結構下通勤效率與住業失衡的空間差異，並結合交通大數據資料特性進行通勤的視覺化，提供都市空間治理與交通資源配置之實證參考。As urban spatial structures continue evolving toward polycentric forms, increasing attention has been paid to their potential in alleviating commuting pressure and promoting regional balance. Taichung City has pursued such a polycentric urban model since the late 20th century, designating three major urban centers; however, persistent issues such as traffic congestion and low commuting efficiency suggest a potential mismatch between the spatial configuration of residential and employment locations and actual commuting behaviors.This study takes Taichung City as a case and utilizes telecom signaling data containing origin-destination commuting flows. By applying the Infomap community detection algorithm on the commuting network graph, nine commuting communities with relatively strong internal interactions are identified. These communities are further verified and delineated into nine urban centers through cross-validation with land surface data, urban planning boundaries, and commuting flow visualizations. Subsequently, Excess Commuting are computed for each center. Based on commuting characteristics, these centers are categorized into five types: major urban centers, secondary centers, local centers, satellite towns, and special-function cores.Results reveal that while major urban centers exhibit higher levels of job-housing imbalance, they achieve relatively higher commuting efficiency. Secondary and local centers fall in the middle range, whereas satellite towns show high dependence on cross-core commuting, leading to lower efficiency. Furthermore, multiple regression models are employed to examine factors influencing commuting distance. “The distance to urban centers” shows a significant positive correlation with commuting distance for most core types, but a negative effect for satellite towns. Variables such as “bus stop density”, “land-use mix”, and “housing opportunity density” also display spatially differentiated effects across core types. This research demonstrates spatial disparities in commuting efficiency and job-housing imbalance within a polycentric structure. By integrating big data from telecom sources and visualizing commuting patterns, the study provides empirical evidence to inform urban spatial governance and transport resource allocation.