  Traffic prediction is necessary not only for management departments to
dispatch vehicles but also for drivers to avoid congested roads. Many traffic
forecasting methods based on deep learning have been proposed in recent years,
and their main aim is to solve the problem of spatial dependencies and temporal
dynamics. In this paper, we propose a useful dynamic model to predict the urban
traffic volume by combining fully bidirectional LSTM, the more complex
attention mechanism, and the external features, including weather conditions
and events. First, we adopt the bidirectional LSTM to obtain temporal
dependencies of traffic volume dynamically in each layer, which is different
from the hybrid methods combining bidirectional and unidirectional ones;
second, we use a more elaborate attention mechanism to learn short-term and
long-term periodic temporal dependencies; and finally, we collect the weather
conditions and events as the external features to further improve the
prediction precision. The experimental results show that the proposed model
improves the prediction precision by approximately 3-7 percent on the NYC-Taxi
and NYC-Bike datasets compared to the most recently developed method, being a
useful tool for the urban traffic prediction.
