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Accurate and reliable traffic forecasting for complicated transportationnetworks is of vital importance to modern transportation management. Thecomplicated spatial dependencies of roadway links and the dynamic temporalpatterns of traffic states make it particularly challenging. To address thesechallenges, we propose a new capsule network (CapsNet) to extract the spatialfeatures of traffic networks and utilize a nested LSTM (NLSTM) structure tocapture the hierarchical temporal dependencies in traffic sequence data. Aframework for network-level traffic forecasting is also proposed bysequentially connecting CapsNet and NLSTM. On the basis of literature review,our study is the first to adopt CapsNet and NLSTM in the field of trafficforecasting. An experiment on a Beijing transportation network with 278 linksshows that the proposed framework with the capability of capturing complicatedspatiotemporal traffic patterns outperforms multiple state-of-the-art trafficforecasting baseline models. The superiority and feasibility of CapsNet andNLSTM are also demonstrated, respectively, by visualizing and quantitativelyevaluating the experimental results.

Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models