  Reliable traffic flow prediction is crucial to creating intelligent
transportation systems. Many big-data-based prediction approaches have been
developed but they do not reflect complicated dynamic interactions between
roads considering time and location. In this study, we propose a dynamically
localised long short-term memory (LSTM) model that involves both spatial and
temporal dependence between roads. To do so, we use a localised dynamic spatial
weight matrix along with its dynamic variation. Moreover, the LSTM model can
deal with sequential data with long dependency as well as complex non-linear
features. Empirical results indicated superior prediction performances of the
proposed model compared to two different baseline methods.
