9,255 research outputs found
Structural recurrent neural network for traffic speed prediction
Deep neural networks have recently demonstrated the
traffic prediction capability with the time series data obtained
by sensors mounted on road segments. However, capturing
spatio-temporal features of the traffic data often requires a
significant number of parameters to train, increasing compu-
tational burden. In this work we demonstrate that embedding
topological information of the road network improves the
process of learning traffic features. We use a graph of a ve-
hicular road network with recurrent neural networks (RNNs)
to infer the interaction between adjacent road segments as
well as the temporal dynamics. The topology of the road
network is converted into a spatio-temporal graph to form a
structural RNN (SRNN). The proposed approach is validated
over traffic speed data from the road network of the city of
Santander in Spain. The experiment shows that the graph-
based method outperforms the state-of-the-art methods based
on spatio-temporal images, requiring much fewer parameters
to trai
Scalable learning with a structural recurrent neural network for short-term traffic prediction
This paper presents a scalable deep learning approach for short-term traffic prediction based on historical traffic data in a vehicular road network. Capturing the spatio-temporal relationship of the big data often requires a significant amount of computational burden or an ad-hoc design aiming for a specific type of road network. To tackle the problem, we combine a road network graph with recurrent neural networks (RNNs)
to construct a structural RNN (SRNN). The SRNN employs a spatio-temporal graph to infer the interaction between adjacent road segments as well as the temporal dynamics of the time series data. The model is scalable thanks to two key aspects. First, the proposed SRNN architecture is built by using the semantic similarity of the spatio-temporal dynamic interactions of all segments. Second, we design the architecture to deal with fixed-length tensors regardless of the graph topology. With the real traffic speed data measured in the city of Santander, we demonstrate the proposed SRNN outperforms the image-based approaches using the capsule network (CapsNet) by 14.1% and the convolutional neural network (CNN) by 5.87%, respectively, in terms of root mean squared error (RMSE). Moreover, we show that the proposed model is scalable. The SRNN model trained with data of a road network is able to predict traffic data of different road networks, with the fixed number of parameters to train
Risk Assessment Algorithms Based On Recursive Neural Networks
The assessment of highly-risky situations at road intersections have been
recently revealed as an important research topic within the context of the
automotive industry. In this paper we shall introduce a novel approach to
compute risk functions by using a combination of a highly non-linear processing
model in conjunction with a powerful information encoding procedure.
Specifically, the elements of information either static or dynamic that appear
in a road intersection scene are encoded by using directed positional acyclic
labeled graphs. The risk assessment problem is then reformulated in terms of an
inductive learning task carried out by a recursive neural network. Recursive
neural networks are connectionist models capable of solving supervised and
non-supervised learning problems represented by directed ordered acyclic
graphs. The potential of this novel approach is demonstrated through well
predefined scenarios. The major difference of our approach compared to others
is expressed by the fact of learning the structure of the risk. Furthermore,
the combination of a rich information encoding procedure with a generalized
model of dynamical recurrent networks permit us, as we shall demonstrate, a
sophisticated processing of information that we believe as being a first step
for building future advanced intersection safety system
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