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.

翻译：在准确、及时的交通预报下,可以提前预测受到影响的交通条件,以指导各机构和居民适当应对交通模式的变化;然而,现有的交通预测工作主要依赖历史交通模式,主要依靠时间序列数据、交通强度和Twitter数据强度之间的关联性,然后将两个时间序列数据输入我们拟议的社会觉悟框架,即将自然语言表现纳入长期交通预测的时间序列记录中的交通-Twitter变异器。大西雅图地区的实验结果表明,我们提议的模型超越了所有评价矩阵中的基线模型。这个NLP-join社会觉醒框架可以成为一种宝贵的网络交通预测和管理系统。