With accurate and timely traffic forecasting, the impacted traffic conditions
can be predicted in advance to guide agencies and residents to respond to
changes in traffic patterns appropriately. However, existing works on traffic
forecasting mainly relied on historical traffic patterns confining to
short-term prediction, under 1 hour, for instance. To better manage future
roadway capacity and accommodate social and human impacts, it is crucial to
propose a flexible and comprehensive framework to predict physical-aware
long-term traffic conditions for public users and transportation agencies. In
this paper, the gap of robust long-term traffic forecasting was bridged by
taking social media features into consideration. A correlation study and a
linear regression model were first implemented to evaluate the significance of
the correlation between two time-series data, traffic intensity and Twitter
data intensity. Two time-series data were then fed into our proposed
social-aware framework, Traffic-Twitter Transformer, which integrated Nature
Language representations into time-series records for long-term traffic
prediction. Experimental results in the Great Seattle Area showed that our
proposed model outperformed baseline models in all evaluation matrices. This
NLP-joined social-aware framework can become a valuable implement of
network-wide traffic prediction and management for traffic agencies.

本文旨在通过将社交媒体功能纳入考虑范围，以提供一个灵活全面的框架，在交通强度和 Twitter 数据强度之间建立相关性，并将两个时间序列数据馈入我们的 Traffic-Twitter Transformer，为长期交通预测提供自然语言表示。实验结果表明，这种纳入 NLP 的社交感知框架能够成为交通机构网络广泛预测和管理的有价值的实现。