School of Civil Engineering and Built Environment, Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD 4000, Australia.
City Planning, City Planning Discipline, School of Architecture and Built Environment, Faculty of Built Environment, The University of New South Wales, Sydney, NSW 2052, Australia.
Sensors (Basel). 2020 Aug 7;20(16):4418. doi: 10.3390/s20164418.
This study explores the relationship between the spatial distribution of relative transfer location (i.e., the location of the transfer point in relation to the trip origin and destination points) and the attractiveness of the transit service using smart card data. Transfer is an essential component of the transit trip that allows people to reach more destinations, but it is also the main factor that deters the smartness of the public transit. The literature quantifies the inconvenience of transfer in terms of extra travel time or cost incurred during transfer. Unlike this conventional approach, the new "transfer location" variable is formulated by mapping the spatial distribution of relative transfer locations on a homogeneous geocoordinate system. The clustering of transfer points is then quantified using grid-based hierarchical clustering. The transfer location factor is formulated as a new explanatory variable for mode choice modelling. This new variable is found to be statistically significant, and no correlation is observed with other explanatory variables, including transit travel time. These results imply that smart transit users may perceive the travel direction (to transfer) as important, in addition to the travel time factor, which would influence their mode choice. Travellers may disfavour even adjacent transfer locations depending on their relative location. The findings of this study will contribute to improving the understanding of transit user behaviour and impact of the smartness of transfer, assist smart transport planning and designing of new transit routes and services to enhance the transfer performance.
本研究利用智能卡数据探讨相对换乘位置（即换乘点相对于出行起点和终点的位置）的空间分布与公交服务吸引力之间的关系。换乘是公交出行的重要组成部分，它使人们能够到达更多的目的地，但也是降低公交智能化水平的主要因素。文献从额外的出行时间或换乘过程中产生的成本方面量化了换乘的不便。与这种传统方法不同，新的“换乘位置”变量是通过将相对换乘位置的空间分布映射到同质地理坐标系统来制定的。然后使用基于网格的层次聚类来量化换乘点的聚类。换乘位置因素被构造成模式选择建模的新解释变量。结果表明，该新变量在统计学上是显著的，并且与包括公交出行时间在内的其他解释变量没有相关性。这些结果表明，智能公交用户可能除了出行时间因素外，还会将出行方向（换乘）视为重要因素，这将影响他们的出行模式选择。即使是相邻的换乘位置，根据其相对位置，也可能不受旅行者欢迎。本研究的结果将有助于提高对公交用户行为和换乘智能化程度的理解，为智能交通规划和新公交线路及服务的设计提供帮助，以提高换乘性能。
