Abstract Accessibility metrics are gaining momentum in public transportation planning and policy-making. However, critical user experience issues such as crowding discomfort and travel time unreliability are still not considered in those accessibility indicators. This paper aims to apply a methodology to build spatiotemporal crowding data and estimate travel time variability in a congested public transport network to improve accessibility calculations. It relies on using multiple big data sources available in most transit systems such as smart card and automatic vehicle location (AVL) data. Sao Paulo, Brazil, is used as a case study to show the impact of crowding and travel time variability on accessibility to jobs. Our results evidence a population-weighted average reduction of 56.8% in accessibility to jobs in a regular workday morning peak due to crowding discomfort, as well as reductions of 6.2% due to travel time unreliability and 59.2% when both are combined. The findings of this study can be of invaluable help to public transport planners and policymakers, as they show the importance of including both aspects in accessibility indicators for better decision making. Despite some limitations due to data quality and consistency throughout the study period, the proposed approach offers a new way to leverage big data in public transport to enhance policy decisions.

中文翻译：

---

使用智能卡大数据估计拥挤和旅行时间变化对大型公共交通网络中工作可及性的影响

摘要 可达性指标在公共交通规划和政策制定中获得了动力。然而，在这些可访问性指标中仍未考虑关键的用户体验问题，例如拥挤不适和旅行时间不可靠。本文旨在应用一种方法来构建时空拥挤数据并估计拥挤的公共交通网络中的旅行时间可变性，以改进可达性计算。它依赖于使用大多数交通系统中可用的多个大数据源，例如智能卡和自动车辆定位 (AVL) 数据。巴西圣保罗被用作案例研究，以展示拥挤和旅行时间可变性对就业机会的影响。我们的结果证明人口加权平均减少了 56。由于拥挤的不适，在正常工作日早上高峰期工作的可访问性降低了 8%，由于旅行时间不可靠而减少了 6.2%，两者结合时减少了 59.2%。这项研究的结果对公共交通规划者和政策制定者来说可能是无价的帮助，因为它们表明将这两个方面都包括在可达性指标中以做出更好的决策的重要性。尽管由于整个研究期间的数据质量和一致性存在一些限制，所提出的方法提供了一种利用公共交通中的大数据来增强政策决策的新方法。因为它们显示了将这两个方面都包括在可访问性指标中以更好地做出决策的重要性。尽管由于整个研究期间的数据质量和一致性存在一些限制，所提出的方法提供了一种利用公共交通中的大数据来增强政策决策的新方法。因为它们显示了将这两个方面都包括在可访问性指标中以更好地决策的重要性。尽管由于整个研究期间的数据质量和一致性存在一些限制，所提出的方法提供了一种利用公共交通中的大数据来增强政策决策的新方法。