DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction

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 traf fic 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 A utomatic L STM C ustomization (ALC) algorithm to a utomatically 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 approac h called D istributed A utomatic L STM C ustomization (DALC) to customize an LSTM model for every detector in large - scale transportation networks. Our experiment demonstrate s that the DALC provides higher prediction accuracy than several approaches provided by Apache Spark MLlib.