An exploration of the interaction between urban human activities and daily traffic conditions: A case study of Toronto, Canada
An exploration of the interaction between urban human activities and daily traffic conditions: A case study of Toronto, Canada
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DOI：
10.1016/j.cities.2018.07.001
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发表时间：
2019-01-01
期刊：
影响因子：
6.7
通讯作者：
Zipf, Alexander
中科院分区：
文献类型：
--
作者：
Huang, Wei;Xu, Shishuo;Zipf, Alexander
了解市民如何与交通系统互动是解决各种城市问题的关键，尤其是交通拥堵。最近，学者们致力于相关工作，从开发交通预测器到理解人类的流动性和活动模式。使用了多种类型的数据，其中众包数据(如社交媒体数据)起到了至关重要的作用。针对已有工作提出的从社交媒体数据中提取交通信息的局限性，本文旨在通过将地理标记推文中的人类活动与日常交通状况联系起来，来探索人类活动对日常交通拥堵的潜在影响。加拿大多伦多的一项案例研究结果显示，与娱乐相关的活动更有可能出现在晚上的高峰时间，而似乎早高峰时间对人类活动的敏感度较低。此外，据悉，参与国际活动的活动往往会对城市交通产生长期影响。这项工作为城市规划者和政策制定者提供了一种新的工具，可以有效地利用低成本的社交媒体数据来处理复杂的城市问题，并为基于众包数据的城市交通和城市动态分析研究提供了启示。
Understanding how citizens interact with transportation system is a key to solving a variety of urban issues in general and traffic congestion in particular. Recently, scholars have put efforts on the pertinent work ranging from developing traffic predictors to understanding human mobility and activity patterns. Multiple types of data have been used, of which crowdsourced data (e.g. social media data) plays an essential role. Due to the limitation of traffic information extraction from social media data raised in the existing work, this paper aims to develop an approach which allows us to explore the potential influence of human activities on daily traffic congestions through linking human activities derived from geotagged tweets to the daily traffic conditions. The result of a case study of Toronto, Canada exhibits that entertainment related activities are more likely to appear during evening peak hours, while it seems that morning rush hours are less sensitive to human activities. In addition, it is learned that the activities involved in international events tend to have a long-term impact on urban traffic. This work provides a new tool for urban planners and policy makers to deal with complex urban issues effectively using low-cost social media data and sheds light on the research on analyzing urban traffic and urban dynamics based on crowdsourced data.