Understanding intra-urban travel patterns is beneficial for urban planning and transportation management, among other fields. As an emerging travel mode, online car-hailing platforms provide massive and high-precision trajectory data, thus offering new opportunities for gaining insights into human mobility. This paper aims to explore temporal intra-urban travel patterns by fitting the distributions of mobility metrics and leveraging the boxplot. The statistical characteristics of daily and hourly travel distance are relatively stable, while those of travel time and speed have some fluctuations. More specifically, most residents travel between 2 and 10 km, with travel times ranging from 6.6 to 30 min, which is fairly consistent with our daily experience. Mainly attributed to travel cost, individuals seldom use online car-hailing for too short or long trips. It is worth mentioning that a weekly pattern can be found in all mobility metrics, in which the patterns of travel time and speed are more obvious than that of travel distance. In addition, since October has more rainy days than November, travel distances and travel times in October are higher than that in November, while the opposite is true for travel speed. This paper can provide a beneficial reference for understanding temporal human mobility patterns, and lays a solid foundation for future research.

中文翻译：

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探索时间上的城市内出行方式：在线网上叫车轨迹数据的观点

了解城市内部的出行方式对城市规划和交通管理以及其他领域都是有益的。作为一种新兴的出行方式，在线乘车平台可提供海量，高精度的轨迹数据，从而为深入了解人类出行提供了新的机会。本文旨在通过拟合流动性指标的分布并利用箱线图来探索时间上的城市内出行方式。每日和每小时出行距离的统计特征相对稳定，而出行时间和速度的统计特征则有所波动。更具体地说，大多数居民的行进距离为2至10公里，行进时间为6.6至30分钟，这与我们的日常经验相当一致。主要归因于差旅费用，个人很少因太短或长途旅行而使用在线汽车叫车服务。值得一提的是，在所有移动性指标中都可以找到每周模式，其中旅行时间和速度的模式比旅行距离的模式更明显。此外，由于十月的雨天比十一月多，所以十月的旅行距离和旅行时间都比十一月高，而旅行速度则相反。本文可以为理解人类时空活动方式提供有益的参考，并为以后的研究打下坚实的基础。10月的旅行距离和旅行时间高于11月，而旅行速度则相反。本文可以为理解人类时空活动方式提供有益的参考，并为以后的研究打下坚实的基础。10月的旅行距离和旅行时间高于11月，而旅行速度则相反。本文可以为理解人类时空活动方式提供有益的参考，并为以后的研究打下坚实的基础。