This paper proposes a deep learning approach for traffic flow prediction in complex road networks. Traffic flow data from induction loop sensors are essentially a time series, which is also spatially related to traffic in different road segments. The spatio-temporal traffic data can be converted into an image where the traffic data are expressed in a 3D space with respect to space and time axes. Although convolutional neural networks (CNNs) have been showing surprising performance in understanding images, they have a major drawback. In the max pooling operation, CNNs are losing important information by locally taking the highest activation values. The inter-relationship in traffic data measured by sparsely located sensors in different time intervals should not be neglected in order to obtain accurate predictions. Thus, we propose a neural network with capsules that replaces max pooling by dynamic routing. This is the first approach that employs the capsule network on a time series forecasting problem, to our best knowledge. Moreover, an experiment on real traffic speed data measured in the Santander city of Spain demonstrates the proposed method outperforms the state-of-the-art method based on a CNN by 13.1% in terms of root mean squared error.Index Terms-traffic speed prediction, capsule network (Cap-sNet), convolutional neural network (CNN)
It is now recognized that geometric structures of scaffolds at several size levels have profound influences on cell adhesion, viability, proliferation and differentiation. This study aims to develop an integrated process to fabricate scaffolds with controllable geometric structures at nano-, micro- and macro-scales. A phase separation method is used to prepare interconnected poly(l-lactide) (PLLA) nanofibrous (NF) scaffolds. The pore size of the NF scaffold at the scale of several hundred micrometers is controlled by the size of porogen, paraffin spheres. At millimeter scale and above, the overall shape of the scaffold is defined by a wax mold produced using a three-dimensional printer. The printer utilizes a stereo lithographic file generated from computed tomographic files retrieved from the National Library of Medicine's Visual Human Project. NF PLLA scaffolds with a human digit shape are successfully prepared using this process. Osteoblast cell line MC3T3-E1 cells are then seeded and cultured in the prepared scaffolds. Cell proliferation, differentiation and biomineralization are characterized to demonstrate the suitability of the scaffolds for the digit bone tissue engineering application.
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 computational 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 vehicular 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 graphbased method outperforms the state-of-the-art methods based on spatio-temporal images, requiring much fewer parameters to train.
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