基於道路容量限制之區域容受力估算－以社子島為例

Abstract

都市計畫係透過考量諸多因素，如：土地使用分區、建築物限制、公共設施、交通建設及環境因素等之交互作用，對都市的發展進行預測及事先規劃，其中一項重要的考量點為人口容受力，其定義為：「都市環境在特定的限制及規範下，所能容納的最大人口數。」過往的都市計畫如：內湖科學園區以及淡海新市鎮在都市計畫階段時，因未充分整合所有應考量之因素，特別是交通管理以及土地使用分區層面，而造成現階段諸多問題，包括塞車以及車流於尖離峰之顯著差異。而社子島即將迎來都市更新，為避免其產生如上述或其他不預期的負面效應，本研究提出一個以道路容量限制來估算人口容受力的方法，並以社子島作為案例分析。 本研究欲推算道路容量限制下的起迄對需求係由何種分布所組成，藉以根據運具選擇以及人口結構相關參數推算人口容受力。本研究透過最小平方法起迄對推估及容量限制指派法的雙層模型來求得穩定路網的起迄對需求及分布，並觀察不同情境下的路網樣態差異，而後根據不同的運具選擇及人口結構情境推算相對應的人口容受力。 本研究的結果顯示，用於起迄對推估中的變異數共變異數矩陣之變異數大小對於迭代的次數有顯著影響，然對於起迄對的分布並沒有造成顯著差異，主要的差異來自於矩陣的隨機性。而運具選擇及人口結構皆會造成人口容受力的顯著影響，其中又以運具選擇的影響力更甚於人口結構。公共運具使用的比例提昇會使人口容受力增加，私有運具則相反。而15-59歲的人口增加會使人口容受力減少，0-14歲以及65歲以上則具有相反效果。本研究最後提出與人口容受力相關的建議及結論，並指出未來可行的研究方向，對以交通管理之角度來估算人口容受力的領域有所貢獻，以期避免都市計畫所帶來的交通負面效應。

Urban planning predicts and pre-plans urban development by considering the interaction of various factors, such as land-use zoning, building restrictions, public facilities, transportation construction and environmental factors. Population carrying capacity is also one critical issue, defined as: “the maximum population that an urban can accommodate under certain restrictions and policies.” In the past, some urban development plans such as Neihu Science Park and Danhai New Town did not fully integrate relevant factors that can be influential during the urban planning stage, especially the transportation planning and land-use zoning, which led to several serious problems nowadays, including traffic congestion and significant imbalance in traffic flows between peak hours and off-peak hours. Shezidao is another area to undergo urban renewal, which is of great concern and controversy. In order to avoid the aforementioned or other unexpected negative effects, this study proposes a method for estimating population carrying capacity based on the constraints of road capacities and conducts a case study for Shezidao. This study seeks to construct the distribution of the Origin-Destination (O-D) demand upon the road capacities to estimate the population carrying capacity. In this study, the O-D demand and its distribution over a stable network are obtained through a bi-level programming model, consisting of the Generalized Least Squares (GLS) O-D estimation and the Capacity Restricted Assignment method. Scenario analysis is conducted to observe varying network flow patterns with different estimation settings. Furthermore, based on different mode choices and demographic structures, the population carrying capacity is estimated over different development scenarios. The results indicate that the magnitude of the variance of the variance-covariance matrix used in O-D estimation has significant impact on the number of iterations, but there is no significant difference in the derived distribution of O-D demand. Instead, the main difference comes from the randomness of the variance-covariance matrix. The mode choice and the demographic structure significantly impact the population carrying capacity, while the mode choice is more influential than the demographic structure. A growth in the proportion of public transportation use can increase the population carrying capacity. In addition, the increase in the population of 15-59 years old (the major population of commuters) can also reduce the population carrying capacity when considering roadway network capacity. Thereupon, this study concludes research findings and suggestions related to population carrying capacity estimation from the perspective of traffic management so as to prevent traffic deterioration from inadequate urban planning.