  Short-term traffic speed prediction has been an important research topic in
the past decade, and many approaches have been introduced. However, providing
fine-grained, accurate, and efficient traffic-speed prediction for large-scale
transportation networks where numerous traffic detectors are deployed has not
been well studied. In this paper, we propose DistPre, which is a distributed
fine-grained traffic speed prediction scheme for large-scale transportation
networks. To achieve fine-grained and accurate traffic-speed prediction,
DistPre customizes a Long Short-Term Memory (LSTM) model with an appropriate
hyperparameter configuration for a detector. To make such customization process
efficient and applicable for large-scale transportation networks, DistPre
conducts LSTM customization on a cluster of computation nodes and allows any
trained LSTM model to be shared between different detectors. If a detector
observes a similar traffic pattern to another one, DistPre directly shares the
existing LSTM model between the two detectors rather than customizing an LSTM
model per detector. Experiments based on traffic data collected from freeway
I5-N in California are conducted to evaluate the performance of DistPre. The
results demonstrate that DistPre provides time-efficient LSTM customization and
accurate fine-grained traffic-speed prediction for large-scale transportation
networks.
