以智慧卡資料探討公共運輸使用者轉乘行為之空間變異

Abstract

隨著都市人口的快速成長，都市交通的負面影響也日趨嚴重，為此，公共運輸應運而生。然而公共運輸並非單一系統，若要有效地滿足都市的運輸需求，則對系統間轉乘型態的了解便至關重要。同時，影響轉乘行為的因素眾多，包括社會經濟、建成環境乃至地理空間等，而這些變數影響關係在非均質的地理空間中也往往存在差異。因此，本研究之目的為對大臺北地區公共運輸使用者的轉乘行為進行分析，藉由了解其特徵以及與其他因子間的影響關係、探索其空間上的變異，來對大臺北地區的旅運型態更加了解，並有助於建構更友善之轉乘環境。 在對於轉乘行為的分析上，以往多採取問卷形式的調查資料，然而此方式雖獲得的資訊較全面，但具有成本高昂、精確度低等問題，也因此不適用於大量或是長期的分析。近年由於資訊科學之演進及自動收費系統的普及，智慧卡的資料能夠被處理與應用，並提供更長期、大量且整合不同系統的分析方式，也讓以智慧卡資料來研究公共運輸議題成為一項具有優勢的選項。本文即以大臺北地區智慧卡（悠遊卡）中的捷運、YouBike資料進行研究，透過兩運具間轉乘行為的識別，依序應用空間分析、熱區分析與迴歸等方式，分析大臺北地區公共運輸使用者轉乘行為之空間變異，並探討背後的因素如人口、社會經濟、建成環境、定期票等，及其影響關係在空間上的異質性。 研究結果發現，各項社會經濟與建成環境變數確實會對使用者轉乘行為造成效果不一之影響，並可與過往文獻做呼應或是呈現出大臺北地區旅運行為的特殊性；而在政策效果上，亦能透過模式分析結果驗證定期票的影響。上述發現不僅可填補過往運用智慧卡資料進行轉乘分析的研究缺口，更能了解其影響因素與影響關係之空間變化，有助於未來政府與營運單位改善各地的轉乘資源，同時更有效地建立富有彈性之公共運輸政策，提升整體公共運輸的質與量。

Having the advantages of releasing traffic congestion and improving environmental quality, public transport systems have gradually become a vital part of urban transportation. However, public transport systems are usually composed of multiple systems, a comprehensive understanding of transfer patterns among systems is an essential issue in order to satisfy user needs of public transport. Because system-transferring is related to users’ travel behaviors, identifying the spatiotemporal variations of transfer patterns helps public transport agencies realizing current situations and further enhancing the service quality. Most previous studies on transfer pattern analysis used survey data. Although the information derived from survey data is detailed, there are still some disadvantages and limits. Thanks to the progress of data science and the automatic fare collection systems, increasing novel methods using smart card data have been developed. This study applied the transit smart card (Easy Card) data of Taipei Metropolitan Area to analyze the spatial variations of public transport users’ transfer patterns. Two study issues were proposed: how has the “1280 monthly pass” scheme affected transfer patterns? And, what are the associations of transfer patterns with build environment and socioeconomic attributes? The empirical findings of this study show that the socio-economic and built environment attributes had different impacts on travelers’ transfer behaviors. Comparing with previous studies, the uniqueness of travelers’ transfer behaviors in the Taipei Metropolitan Area was identified. The empirical findings can not only fill the research gap in analyzing transfer behavior by smart card data, but also help us understanding the relationships among the study variables and their spatial variations. Results gained from this research can be used by governments or public transport agencies as a reference to improve the inter-system transfer quality or formulate more flexible policies, and promote the quality and ridership of public transport.