Traffic prediction on road networks is highly challenging due to the complexity of traffic systems and is a crucial task in successful intelligent traffic system applications. Existing approaches mostly capture the static spatial dependency relying on the prior knowledge of the graph structure. However, the spatial dependency can be dynamic, and sometimes the physical structure may not reflect the genuine relationship between roads. To better capture the complex spatial‐temporal dependencies and forecast traffic conditions on road networks, a multi‐step prediction model named Spatial‐Temporal Attention Wavenet (STAWnet) is proposed. Temporal convolution is applied to handle long time sequences, and the dynamic spatial dependencies between different nodes can be captured using the self‐attention network. Different from existing models, STAWnet does not need prior knowledge of the graph by developing a self‐learned node embedding. These components are integrated into an end‐to‐end framework. The experimental results on three public traffic prediction datasets (METR‐LA, PEMS‐BAY, and PEMS07) demonstrate effectiveness. In particular, in the 1 h ahead prediction, STAWnet outperforms state‐of‐the‐art methods with no prior knowledge of the network.

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A critical problem in large neural networks is over parameterization with a large number of weight parameters, which limits their use on edge devices due to prohibitive computational power and memory/storage requirements. To make neural networks more practical on edge devices and real-time industrial applications, they need to be compressed in advance. Since edge devices cannot train or access trained networks when internet resources are scarce, the preloading of smaller networks is essential. Various works in the literature have shown that the redundant branches can be pruned strategically in a fully connected network without sacrificing the performance significantly. However, majority of these methodologies need high computational resources to integrate weight training via the back-propagation algorithm during the process of network compression. In this work, we draw attention to the optimization of the network structure for preserving performance despite compression by pruning aggressively. The structure optimization is performed using the simulated annealing algorithm only, without utilizing back-propagation for branch weight training. Being a heuristic-based, non-convex optimization method, simulated annealing provides a globally near-optimal solution to this NP-hard problem for a given percentage of branch pruning. Our simulation results have shown that simulated annealing can significantly reduce the complexity of a fully connected network while maintaining the performance without the help of back-propagation.

Self-similar growth and fractality are important properties found in many real-world networks, which could guide the modeling of network evolution and the anticipation of new links. However, in technology-convergence networks, such characteristics have not yet received much attention. This study provides empirical evidence for self-similar growth and fractality of the technology-convergence network in the field of intelligent transportation systems. This study further investigates the implications of such fractal properties for link prediction via partial information decomposition. It is discovered that two different scales of the network (i.e., the micro-scale structure measured by local similarity indices and the scaled-down structure measured by community-based indices) have significant synergistic effects on link prediction. Finally, we design a synergistic link prediction (SLP) approach which enhances local similarity indices by considering the probability of link existence conditional on the joint distribution of two scales. Experimental results show that SLP outperforms the benchmark local similarity indices in most cases, which could further validate the existence and usefulness of the synergistic effect between two scales on link prediction.

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