59 research outputs found
Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction
Tracking congestion throughout the network road is a critical component of
Intelligent transportation network management systems. Understanding how the
traffic flows and short-term prediction of congestion occurrence due to
rush-hour or incidents can be beneficial to such systems to effectively manage
and direct the traffic to the most appropriate detours. Many of the current
traffic flow prediction systems are designed by utilizing a central processing
component where the prediction is carried out through aggregation of the
information gathered from all measuring stations. However, centralized systems
are not scalable and fail provide real-time feedback to the system whereas in a
decentralized scheme, each node is responsible to predict its own short-term
congestion based on the local current measurements in neighboring nodes.
We propose a decentralized deep learning-based method where each node
accurately predicts its own congestion state in real-time based on the
congestion state of the neighboring stations. Moreover, historical data from
the deployment site is not required, which makes the proposed method more
suitable for newly installed stations. In order to achieve higher performance,
we introduce a regularized Euclidean loss function that favors high congestion
samples over low congestion samples to avoid the impact of the unbalanced
training dataset. A novel dataset for this purpose is designed based on the
traffic data obtained from traffic control stations in northern California.
Extensive experiments conducted on the designed benchmark reflect a successful
congestion prediction
GMB: An Efficient Query Processor for Biological Data
Bioinformatics applications manage complex biological data stored into distributed and often heterogeneous databases and require large computing power. These databases are too big and complicated to be rapidly queried every time a user submits a query, due to the overhead involved in decomposing the queries, sending the decomposed queries to remote databases, and composing the results. There is also considerable communication costs involved. This study addresses the mentioned problems in Grid-based environment for bioinformatics. We propose a Grid middleware called GMB that alleviates these problems by caching the results of Frequently Used Queries (FUQ). Queries are classified based on their types and frequencies. FUQ are answered from the middleware, which improves their response time. GMB acts as a gateway to TeraGrid Grid: it resides between users’ applications and TeraGrid Grid. We evaluate GMB experimentally
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