LAUSR

Social media (e.g., Sina Weibo) have the advantage of reflecting traffic information, including the reasons for jams, illegal behaviors, and emergency recourses on roads. However, there remains a challenging issue regarding how to sufficiently mine traffic information. In this paper, we propose a deep learning-based method that uses social media data for traffic jam management. The core ideas of the proposed method are twofold. First, a multichannel network with a Long Short-Term Memory layer (LSTM-layer) and a Convolution layer (Conv-layer) (termed as MC-LSTM-Conv) is proposed. This model consists of two information channels for extracting abstract features from input text. Each channel includes two Conv-layers, and an LSTM-layer is added to one of the four Conv-layers. The MC-LSTM-Conv model is used to extract check-in microblogs reflecting traffic jams from mass Sina Weibo data. Second, a series of matching rules are constructed based on the keywords that are related to traffic-jam scenes. These rules further classify the microblogs extracted by the first step into four classes, and each of the classes reflects a specific road condition (i.e., traffic accidents or large-scale activities, road construction, traffic lights, and the low efficiency of government agencies). Experiments on Sina Weibo data demonstrate that the proposed multichannel network has superior performance in extracting microblogs about traffic jams. The keyword fuzzy matching method can fetch detailed information about traffic jams efficiently.