Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting

Understand

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.