The rapid advancement of information and communication technology has brought a revolution in the domain of public transport (PT) planning alongside other areas of transport planning and operations. Of particular significance are the passively generated big data sources (e.g., smart cards, detailed vehicle location data, mobile phone traces, social media) which have started replacing the traditional surveys conducted onboard, at the stops/stations and/or at the household level for gathering insights about the behavior of the PT users. This paper presents a systematic review of the contemporary research papers related to the use of novel data sources in PT planning with particular focus on (1) assessing the usability and potential strengths and weaknesses of different emerging big data sources, (2) identifying the challenges and highlighting research gaps. Reviewed articles were categorized based on qualitative pattern matching (similarities/dissimilarities) and multiple sources of evidence analysis under three categories—use of big data in (1) travel pattern analysis, (2) PT modelling, and (3) PT performance assessment. The review revealed research gaps ranging from methodological and applied research on fusing different forms of big data as well as big data and traditional survey data; further work to validate the models and assumptions; lack of progress on developing more dynamic planning models. Findings of this study could inform transport planners and researchers about the opportunities/challenges big data bring for PT planning. Harnessing the full potential of the big data sources for PT planning can be extremely useful for cities in the developing world, where the PT landscape is changing more rapidly, but traditional forms of data are expensive to collect.

中文翻译：

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公共交通规划的新兴大数据源：对当前艺术状态和未来研究方向的系统回顾

信息和通信技术的快速发展带来了公共交通 (PT) 规划领域以及其他交通规划和运营领域的革命。特别重要的是被动生成的大数据源（例如，智能卡、详细的车辆位置数据、手机轨迹、社交媒体），它们已经开始取代在船上、车站/车站和/或家庭层面进行的传统调查用于收集有关 PT 用户行为的见解。本文系统回顾了与在 PT 规划中使用新数据源相关的当代研究论文，特别关注 (1) 评估不同新兴大数据源的可用性和潜在优势和劣势，(2) 识别挑战并突出研究差距。根据定性模式匹配（相似性/差异性）和多来源证据分析将审查的文章分为三类——在 (1) 旅行模式分析、(2) PT 建模和 (3) PT 绩效评估中使用大数据。审查揭示了研究差距，包括融合不同形式大数据的方法论和应用研究以及大数据和传统调查数据；进一步验证模型和假设的工作；在开发更动态的规划模型方面缺乏进展。这项研究的结果可以让交通规划者和研究人员了解大数据为 PT 规划带来的机遇/挑战。