都會通勤是近代都市發展的一項重要議題。當都市逐漸向外圍擴張，除了造成土地利用型態產生變化，也影響都市居民的通勤狀況，民眾需要花費更多通勤成本往返住所與工作地點。
住業均衡（Jobs-Housing Balance）理論認為在理想狀態下，就業人口的工作地點與居住地點位於相同區域，以節省通勤時間、距離，減少汽、機車的使用時間，進而降低能源消耗，以及空氣汙染的排放量。既有文獻多使用政府各類年度交通與人口統計資料，應用於Jobs - Housing 比例，衡量住業均衡現象，一地所提供之就業機會數與居住數是否達到平衡。同時搭配超額通勤指數，量化往返住所與工作地點所耗費之額外通勤成本，並進一步推估都會區潛在的最小及最大通勤成本。過往受限於資料時空解析度不佳，在即時交通資料的大數據分析，或都會通勤區之空間視覺化，皆缺乏深入探討。
臺灣都市區域發展過程中，高速公路建設擔任重要的角色，聯繫著都市、市鎮、郊區、鄉村，增加城鄉與區域間的可及性，帶動高速公路沿線城市與交流道周邊區域之人口以及工商業快速發展。交通部高速公路局將具有不同車輛類型的車行紀錄，去標籤化後，發布自2015年起，不同的電子道路系統（Electronic Toll Collection，簡稱ETC）資料集，而本研究選用的資料集為「各類車種旅次數量（M08A）」。試圖以時空解析度較佳的巨量資料，進行空間視覺化，並區分臺灣不同的通勤圈，以探討各通勤圈內住業分布與通勤現象。
本研究結果將臺灣高速公路跨市鎮通勤，分為五大通勤圈：北北桃通勤圈、新竹通勤圈、中部通勤圈、臺南通勤圈、高雄通勤圈。透過超額通勤指標分析五大通勤圈通勤狀況，大部分之通勤距離為逐年增加的趨勢，而北北桃通勤圈、新竹通勤圈、臺南通勤圈、高雄通勤圈的通勤較為穩定，而中部通勤圈的通勤則逐年趨近飽和。藉由不同視覺化方式，探討住業分布與通勤圈觀測範圍、住業均衡範圍以及潛力通勤範圍，助於瞭解通勤圈主要通勤範圍，其住業的空間分布，並推估潛力發展範圍，期望可作為將來都市規劃的參考依據。

Urban commuting is one of the most important issues in the development of modern cities. As the cities gradually expand to the suburbs, not only does the land use pattern change, but it also affects the commuting of urban residents, who have to spend more costs on commuting between their homes and workplaces.
Jobs-Housing Balance theory indicates that the ideal situation would be the supply of jobs and housing in the same region, which will reduce commuting time and distance, the use of automobiles and motorcycles, the energy consumption and air pollution emissions. Scholars before used a variety of annual government transportation and Census data to measure the Jobs-Housing ratio, which measured the balance between the number of employment opportunities and the number of residences in a region. Also, they quantified the additional cost of commuting between home and work via Excess Commuting, and estimated the potential minimum and maximum commuting costs in metropolitan areas. In the past, due to the poor spatial and temporal resolution of the data, there was a lack of analyzing real-time traffic data and spatial visualization of urban commuting areas.
In the development of Taiwan’s urban areas, highway construction plays an important role. It links cities, towns, and suburbs, increasing accessibility between urban and rural areas, driving the rapid development of population and industry in the cities along the highway and the areas surrounding the interchanges. The Freeway Bureau, Ministry of Transportation and Communications (MOTC) released the Electronic Toll Collection (ETC) dataset on the highway in Taiwan. The dataset records different types of vehicles. The dataset selected for this study is the “number of trips by vehicle type (M08A)”. This study attempts to spatially visualize the big data with better spatial-temporal resolution, and to distinguish different commuting areas in Taiwan. The goal is to explore the distribution of jobs and housing, and commuting phenomena within each commuting area.
The results of this study divides the cross-town commuting of Taiwan’s highways into five major commuting areas: Pei-Pei-Tao commuting area, Hsinchu commuting area, the middle commuting area, Tainan commuting area, and Kaohsiung commuting area. Analyzing the commuting of the five commuting areas through the Excess Commuting Index, most of the commuting distances are increasing year by year. While the commuting of the Pei-Pei-Tao commuting area, Hsinchu commuting area, Tainan commuting area, and Kaohsiung commuting area are more stable, excepting the middle commuting area which is getting saturated year by year. Through different visualization methods, this study explores the distribution of jobs and housing, commuting areas, and the jobs-housing balance area, and the potential commuting areas. This study is helpful of understanding the main commuting scopes of commuting areas, the spatial distribution of jobs and housing, and the potential development areas. The results of this study could be a reference for the future urban planning.

中華民國交通部（2021）。109年民眾日常使用運具狀況調查摘要分析。取自：https://srda.sinica.edu.tw/srda_freedownload.php?recid=3338&fileid=20366。
白仁德（2015）。歷年來高速公路系統沿線地區人口及產業空間分布變遷之研究。Journal of Taiwan Land Research，18（2）， 21－46。
周辰（2017）。臺灣中南部主要都市地區之國道 ETC 各車種行車距離與土地使用及生活圈範圍合理性之探討。成功大學都市計劃學系學位論文，1－119。
林佩萱（2011）。 從成長管理之住業均衡觀點檢視台南市現況-兼論住宅政策及土地使用議題。土地問題研究季刊，10（1），106－122。
林珆如（2005）。全球化下的大都會衍伸區帶之發展與轉變—以 2000 年台灣西部走廊為例。 元智大學資訊社會學碩士論文。
林書汝（2010）。高速公路建設對住業均衡的影響：國道 5 號之實證分析。國立臺北大學都市計畫研究所碩士論文。
邱信智（2000）。台灣地區都會區通勤-就業活動空間分布之研究。逢甲大學建築與都市計畫所碩士論文。
范聖堂（1993）。 工作-居住均衡指標之建立及其應用。國立交通大學交通運輸工程研究所碩士論文。
梁紹芳（2019）。捷運對都會區內人口產業分布之影響 —台北捷運實證研究。國立政治大學。
陳力煒（2015）。台北都會區之住業均衡分析。國立臺灣海洋大學河海工程學系碩士論文。
Batty, M. (2013). Big data, smart cities and city planning. Dialogues in human geography, 3(3), 274-279.
Brueckner, J. K. (2000). Urban sprawl: diagnosis and remedies. International regional science review, 23(2), 160-171.
Bwire, H., & Zengo, E. (2020). Comparison of efficiency between public and private transport modes using excess commuting: An experience in Dar es Salaam. Journal of Transport Geography, 82, 102616. doi:https://doi.org/10.1016/j.jtrangeo.2019.102616
Camagni, R., Gibelli, M. C., & Rigamonti, P. (2002). Urban mobility and urban form: the social and environmental costs of different patterns of urban expansion. Ecological Economics, 40(2), 199-216. doi:https://doi.org/10.1016/S0921-8009(01)00254-3
Cervero, R. (1989). Jobs-housing balancing and regional mobility. Journal of the American Planning Association, 55(2), 136-150.
Cervero, R. (1996). Jobs-housing balance revisited: trends and impacts in the San Francisco Bay Area. Journal of the American Planning Association, 62(4), 492-511.
Chen, H.-P. (2000). Commuting and land use patterns. Geographical and Environmental Modelling, 4(2), 163-173.
Chen, J., Gao, J., & Chen, W. (2016). Urban land expansion and the transitional mechanisms in Nanjing, China. Habitat International, 53, 274-283. doi:https://doi.org/10.1016/j.habitatint.2015.11.040
Chowdhury, T. A., Scott, D. M., & Kanaroglou, P. S. (2013). Urban form and commuting efficiency: A comparative analysis across time and space. Urban Studies, 50(1), 191-207.
Cohen, B. (2006). Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability. Technology in society, 28(1-2), 63-80.
Gao, Q.-L., Li, Q.-Q., Zhuang, Y., Yue, Y., Liu, Z.-Z., Li, S.-Q., & Sui, D. (2019). Urban commuting dynamics in response to public transit upgrades: A big data approach. PloS one, 14(10), e0223650.
Giuliano, G., & Small, K. A. (1993). Is the Journey to Work Explained by Urban Structure? Urban Studies, 30(9), 1485-1500. doi:10.1080/00420989320081461
Hamilton, B. W., & Röell, A. (1982). Wasteful commuting. Journal of Political Economy, 90(5), 1035-1053.
Hanson, S., & Pratt, G. (1988). Reconceptualizing the Links between Home and Work in Urban Geography. Economic Geography, 64(4), 299-321. doi:10.2307/144230
Herbert, D. T., & Thomas, C. J. (1982). Urban Geography.
Horner, M. W. (2002). Extensions to the concept of excess commuting. Environment and Planning A: Economy and Space, 34(3), 543-566.
Huang, Z., Wei, Y. D., He, C., & Li, H. (2015). Urban land expansion under economic transition in China: A multi-level modeling analysis. Habitat International, 47, 69-82. doi:https://doi.org/10.1016/j.habitatint.2015.01.007
Levine, J. (1998). Rethinking accessibility and jobs-housing balance. Journal of the American Planning Association, 64(2), 133-149.
Long, Y., & Thill, J.-C. (2015). Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing. Computers, Environment and Urban Systems, 53, 19-35.
Ma, K.-R., & Banister, D. (2006). Extended Excess Commuting: A Measure of the Jobs-Housing Imbalance in Seoul. Urban Studies, 43(11), 2099-2113. Retrieved from http://www.jstor.org/stable/43197427
Ma, K.-R., & Banister, D. (2007). Urban spatial change and excess commuting. Environment and planning A, 39(3), 630-646.
Margolis, J. (1957). Municipal fiscal structure in a metropolitan region. Journal of Political Economy, 65(3), 225-236.
McGee, T. G. (1991). The Emergence of Desakota Regions in Asia: Expanding a Hypothesis. In N.Ginsburg, B. Koppel, & T.G.McGee (Eds.), The Extended Metropolis: Settlement Transition in Asia (pp. 3-25). Honolulu: Univ. of Hawaii Press.
Merriman, D., Ohkawara, T., & Suzuki, T. (1995). Excess commuting in the Tokyo metropolitan area: measurement and policy simulations. Urban Studies, 32(1), 69-85.
Miller, H. J., & Goodchild, M. F. (2015). Data-driven geography. GeoJournal, 80(4), 449-461.
Murphy, E. (2009). Excess commuting and modal choice. Transportation Research Part A: Policy and Practice, 43(8), 735-743.
Nechyba, T. J., & Walsh, R. P. (2004). Urban sprawl. Journal of economic perspectives, 18(4), 177-200.
Niedzielski, M. A. (2006). A spatially disaggregated approach to commuting efficiency. Urban Studies, 43(13), 2485-2502.
Niedzielski, M. A., Horner, M. W., & Xiao, N. (2013). Analyzing scale independence in jobs-housing and commute efficiency metrics. Transportation Research Part A: Policy and Practice, 58, 129-143.
Peng, Z.-R. (1997). The jobs-housing balance and urban commuting. Urban Studies, 34(8), 1215-1235.
Sagiroglu, S., & Sinanc, D. (2013). Big data: A review. Paper presented at the 2013 international conference on collaboration technologies and systems (CTS).
Small, K. A., & Song, S. (1992). "Wasteful" Commuting: A Resolution. Journal of Political Economy, 100(4), 888-898. Retrieved from http://www.jstor.org/stable/2138692
Wang, D., & Chai, Y. (2009). The jobs–housing relationship and commuting in Beijing, China: the legacy of Danwei. Journal of Transport Geography, 17(1), 30-38.
Weitz, J. (2003). Jobs-housing balance: American Planning Association Chicago, IL.
White, M. J. (1988). Urban Commuting Journeys Are Not "Wasteful". Journal of Political Economy, 96(5), 1097-1110. Retrieved from http://www.jstor.org/stable/1837250
Yang, X., Fang, Z., Yin, L., Li, J., Zhou, Y., & Lu, S. (2018). Understanding the spatial structure of urban commuting using mobile phone location data: a case study of Shenzhen, China. Sustainability, 10(5), 1435.
You, H., & Yang, X. (2017). Urban expansion in 30 megacities of China: categorizing the driving force profiles to inform the urbanization policy. Land Use Policy, 68, 531-551. doi:https://doi.org/10.1016/j.landusepol.2017.06.020
Zhang, P., Zhou, J., & Zhang, T. (2017). Quantifying and visualizing jobs-housing balance with big data: A case study of Shanghai. Cities, 66, 10-22. doi:https://doi.org/10.1016/j.cities.2017.03.004
Zhou, J., & Long, Y. (2014). Jobs-housing balance of bus commuters in Beijing: Exploration with large-scale synthesized smart card data. Transportation Research Record, 2418(1), 1-10.
Zhou, J., & Murphy, E. (2019). Day-to-day variation in excess commuting: An exploratory study of Brisbane, Australia. Journal of Transport Geography, 74, 223-232. doi:https://doi.org/10.1016/j.jtrangeo.2018.11.014
Zhou, J., Murphy, E., & Long, Y. (2014). Commuting efficiency in the Beijing metropolitan area: An exploration combining smartcard and travel survey data. Journal of Transport Geography, 41, 175-183.