50,102 research outputs found
A vision transformer approach for traffic congestion prediction in urban areas
Traffic problems continue to deteriorate because of the increasing population in urban areas that rely on many modes of
transportation, the transportation infrastructure has achieved considerable strides in the last several decades. This has led to an
increase in congestion control difficulties, which directly affect citizens through air pollution, fuel consumption, traffic law breaches,
noise pollution, accidents, and loss of time. Traffic prediction is an essential aspect of an intelligent transportation system in smart cities
because it helps reduce traffic congestion. This article aims to design and enforce a traffic prediction scheme that is efficient and
accurate in forecasting traffic flow. Available traffic flow prediction methods are still unsuitable for real-world applications. This fact
motivated us to work on a traffic flow forecasting issue using Vision Transformers (VTs). In this work, VTs were used in conjunction with
Convolutional neural networks (CNNs) to predict traffic congestion in urban spaces on a city-wide scale. In our proposed architecture, a
traffic image is fed to the CNN, which generates feature maps. These feature maps are then fed to the VT, which employs the dual
techniques of tokenization and projection. Tokenization is used to convert features into tokens containing Vision information, which are
then sent to projection, where they are transformed into feature maps and ultimately delivered to LSTM. The experimental results
demonstrate that the vision transformer prediction method based on Spatio-temporal characteristics is an excellent way of predicting
traffic flow, particularly during anomalous traffic situations. The proposed technology surpasses traditional methods in terms of
precision, accuracy and recall and aids in energy conservation. Through rerouting, the proposed work will benefit travellers and reduce
fuel use
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
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