  Over the past decade, several approaches have been introduced for short-term
traffic prediction. However, providing fine-grained traffic prediction for
large-scale transportation networks where numerous detectors are geographically
deployed to collect traffic data is still an open issue. To address this issue,
in this paper, we formulate the problem of customizing an LSTM model for a
single detector into a finite Markov decision process and then introduce an
Automatic LSTM Customization (ALC) algorithm to automatically customize an LSTM
model for a single detector such that the corresponding prediction accuracy can
be as satisfactory as possible and the time consumption can be as low as
possible. Based on the ALC algorithm, we introduce a distributed approach
called Distributed Automatic LSTM Customization (DALC) to customize an LSTM
model for every detector in large-scale transportation networks. Our experiment
demonstrates that the DALC provides higher prediction accuracy than several
approaches provided by Apache Spark MLlib.
