Identification of Freeway Traffic Congestion From Social Media Using a Hybrid Deep Learning Method: A Case Study

ABSTRACT The massive real‐time data shared by Internet users provides a potentially rich resource for detecting traffic congestion. Targeting China's predominant social network platform, ‘Sina Weibo’, this paper proposes a hybrid deep learning method to mine valuable freeway traffic congestion information. Specifically, original microblog data is extracted and filtered via a customised web crawler coupled with geographical anchors. Afterwards, the selected microblogs undergo rigorous preprocessing, wherein a domain‐specific Word2Vec model is trained to represent textual information as high‐dimensional word embeddings. To effectively identify congestion‐related microblogs, this study develops a ConvBILSTM model that integrates a TextCNN layer for capturing local textual features and a BILSTM layer for modelling global context dependencies. Extensive experimental evaluations demonstrate the superiority of the proposed method compared to benchmark approaches, achieving a recall of 0.8519 and an F1‐score of 0.8415. Furthermore, the congestion‐prone locations extracted from congestion‐related microblogs based on Document Frequency scores are highly consistent with ground‐truth data. Overall, this research facilitates timely and accurate reporting of traffic congestion, providing a valuable supplement or alternative to conventional freeway traffic surveillance methods.