Traffic flow forecasting is crucial to Intelligent Transportation Systems (ITS), particularly for route planning and traffic management. Spatiotemporal graph neural networks have been widely used for this purpose, where a global graph structure is mainly used, but local graph features are ignored. Multi-scale spatiotemporal features—the combined application of global and local graph features—can help effectively discover the underlying spatiotemporal patterns. This study proposed a multi-scale adaptive spatiotemporal forecasting model called Adaptive SpatioTemporal InceptionNet (AST-InceptionNet). To enrich the captured features and extract multi-scale spatiotemporal ones, the inception part is applied to combine local spatiotemporal features with several global ones. Additionally, we developed a fully adaptive graph convolution method, including topologically adaptive graph convolution and an adaptive adjacency matrix. It can autonomously and dynamically learn spatial heterogeneity and enable the proposed model to work with unknown adjacency relations. The proficiency of AST-InceptionNet was validated using four publicly available real-world traffic flow datasets. The experiments demonstrate a satisfactory performance from the proposed model, outperforming existing state-of-the-art methods by up to approximately 12%.

We have developed a genetic algorithm (GA) approach to the problem of task scheduling for multiprocessor systems. Our approach requires minimal problem specific information and no problem specific operators or repair mechanisms. Key features of our system include a flexible, adaptive problem representation and an incremental fitness function. Comparison with traditional scheduling methods indicates that the GA is competitive in terms of solution quality if it has sufficient resources to perform its search. Studies in a nonstationary environment show the GA is able to automatically adapt to changing targets.