Department of Computer Science, Rutgers University, Piscataway, NJ, 08854-8019, USA.
Department of Computer Science, Florida State University, Tallahassee, FL, 32306, USA.
Sci Rep. 2022 Sep 26;12(1):15988. doi: 10.1038/s41598-022-19394-z.
Understanding human mobility is of great significance for sustainable transportation planning. Long-term travel delay change is a key metric to measure human mobility evolution in cities. However, it is challenging to quantify the long-term travel delay because it happens in different modalities, e.g., subway, taxi, bus, and personal cars, with implicated coupling. More importantly, the data for long-term multi-modal delay modeling is challenging to obtain in practice. As a result, the existing travel delay measurements mainly focus on either single-modal system or short-term mobility patterns, which cannot reveal the long-term travel dynamics and the impact among multi-modal systems. In this paper, we perform a travel delay measurement study to quantify and understand long-term multi-modal travel delay. Our measurement study utilizes a 5-year dataset of 8 million residents from 2013 to 2017 including a subway system with 3 million daily passengers, a 15 thousand taxi system, a 10 thousand personal car system, and a 13 thousand bus system in the Chinese city Shenzhen. We share new observations as follows: (1) the aboveground system has a higher delay increase overall than that of the underground system but the increase of it is slow down; (2) the underground system infrastructure upgrades decreases the aboveground system travel delay increase in contrast to the increase the underground system travel delay caused by the aboveground system infrastructure upgrades; (3) the travel delays of the underground system decreases in the higher population region and during the peak hours.
理解人类的移动性对于可持续交通规划具有重要意义。长期旅行延迟变化是衡量城市中人类移动性演变的关键指标。然而，由于它发生在不同的模式中，例如地铁、出租车、公共汽车和私家车，并涉及到相互关联，因此量化长期旅行延迟是具有挑战性的。更重要的是，长期多模式延迟建模的数据在实践中很难获得。因此，现有的旅行延迟测量主要集中在单一模式系统或短期移动模式上，无法揭示长期旅行动态和多模式系统之间的影响。在本文中，我们进行了一项旅行延迟测量研究，以量化和理解长期多模式旅行延迟。我们的测量研究利用了 2013 年至 2017 年为期 5 年的 800 万居民数据集，其中包括一个拥有 300 万每日乘客的地铁系统、一个 1.5 万辆出租车系统、一个 1 万辆私家车系统和一个 1.3 万辆公共汽车系统的深圳市。我们分享了以下新的观察结果：（1）地面系统的延迟总体增加高于地下系统，但增加速度较慢；（2）地下系统基础设施升级降低了地面系统旅行延迟的增加，而地面系统基础设施升级却增加了地下系统旅行延迟；（3）在人口密度较高的地区和高峰时段，地下系统的旅行延迟会降低。
