智能公交卡作为越来越普遍的城市公共交通付费方式产生了大量详细的出行数据.这种大数据的出现为出行行为研究特别在时空间方面带来新的机会和挑战.以布里斯班为例,展示了一种地理可视化方法——条件流量图及其在分析智能公交卡数据方面的应用.对该数据进行可视化后,揭示了基于巴士公交的出行在一个城市尺度下相对精细的时空间分布和特征,以及在不同因素(包括快速巴士公交道、乘客组群和天气)作用下的变化.这些发现对建立城市公交系统更有针对性地规划和运营以更好满足乘客出行需求有一定的启示作用.同时,未来研究需要进一步完善和补充智能公交卡数据,并发展更成熟的分析方法.%The increasing prevalence of transit smart card as a transit fare payment method has helped generate travel behaviour data of huge quantity and rich details. The emergence of such big data has brought new opportunities as well as challenges for travel behavior research, particularly in the arena of spatial and temporal analysis. Drawing on Brisbane as a case study, this paper demonstrates the development of a geo-visualisation technique, namely the lfow-comap, and its application in analyzing transit smart card data. Visualizing this data has offered insights into the detailed spatial and temporal patterns and characteristics of trip-making by bus transit, and its variations under the inlfuence of other factors including the presence of exclusive busway, different passenger groups and weather. These ifndings herald a series of implications that have the potential to help devise more targeted planning and operation measures for an urban public transit system with a view to better meeting the travel demands of transit users. Meanwhile, future research may continue to improve the information quality of transit smart card data, and from there, develop more sophisticated analytical techniques.
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