Road traffic forecasting is crucial in Intelligent Transportation Systems (ITS). To achieve accurate results, it is necessary to model the dynamic nature and the complex non-linear dependencies governing traffic. The goal is particularly challenging when the prediction involves more than just one traffic variable. This paper proposes a novel multi-task learning model, called AST-MTL, to perform multi-horizon predictions of the traffic flow and speed at the road network scale. The strategy combines a multilayer fully-connected neural network (FNN) and a multi-head attention mechanism to learn related tasks while improving generalization performance. The model also includes the graph convolutional network (GCNs) and the gated recurrent unit network (GRUs) to extract the spatial and temporal features of traffic conditions. Our experiments employ new sets of GPS data, called OBU data, to perform traffic prediction in the freeway and urban contexts. The experimental results prove our model can effectively perform multi-horizon traffic forecasting for different types of roads and outperform state-of-the-art models.INDEX TERMS Deep learning, multi-task learning, graph mining, traffic prediction.

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<p>As climate change prospects point towards the pressing need for local-scale adaptation measures, heat exposure becomes one of the key aspects in determining the health of the urban environment. In addition, many western metropolises are characterized by an ageing population which may lead to an increased community-level sensitivity to heat extremes – that is the case in many European Functional Urban Areas (FUAs), including in the Greater Lisbon (hereinafter Lisbon) area. Lisbon has already a track record of being regularly exposure to severe heatwaves (HW), and regional climate change prospects point to its aggravation in coming decades (frequency, duration, and severity), as with other Southern European cities. Accordingly, there is a pressing need to pinpoint the urban locations where people are relatively more exposed to the excess heat, which can lead to dehydration, cerebrovascular accidents or thrombogenesis.</p><p> </p><p>In this study, air temperature measurements from citizen owned meteorological stations is retrieved from open data platforms, quality controlled and co-located with Earth Observation (EO) data and products to downscale the official air temperature forecasts (from deterministic numerical weather predictions, NWP) from the native regional scale (2.5km) up to a metric spatial resolution (200m). As the NWP model resolves the regional physical processes, the Machine Learning (ML) high-resolution output is able to adjust its bias to the specificities of the urban location, by accurately predicting the local contribution of the urban heat island (UHI) effect, quantifying the heat anomaly at the neighborhood scale. The cooling effect of the urban green infrastructure is also detected, providing mensurable scenarios to support future urban greening initiatives. In addition, with these results, the identification of short-term critical areas during heatwave events becomes possible, supporting the local public health stakeholders in their decision-making – i.e., regarding where and when to act.</p>