  Over the past decade, many approaches have been introduced for traffic speed
prediction. However, providing fine-grained, accurate, time-efficient, and
adaptive traffic speed prediction for a growing transportation network where
the size of the network keeps increasing and new traffic detectors are
constantly deployed has not been well studied. To address this issue, this
paper presents DistTune based on Long Short-Term Memory (LSTM) and the
Nelder-Mead method. Whenever encountering an unprocessed detector, DistTune
decides if it should customize an LSTM model for this detector by comparing the
detector with other processed detectors in terms of the normalized traffic
speed patterns they have observed. If similarity is found, DistTune directly
shares an existing LSTM model with this detector to achieve time-efficient
processing. Otherwise, DistTune customizes an LSTM model for the detector to
achieve fine-grained prediction. To make DistTune even more time-efficient,
DistTune performs on a cluster of computing nodes in parallel. To achieve
adaptive traffic speed prediction, DistTune also provides LSTM re-customization
for detectors that suffer from unsatisfactory prediction accuracy due to for
instance traffic speed pattern change. Extensive experiments based on traffic
data collected from freeway I5-N in California are conducted to evaluate the
performance of DistTune. The results demonstrate that DistTune provides
fine-grained, accurate, time-efficient, and adaptive traffic speed prediction
for a growing transportation network.
