Smart cities are nowadays equipped with pervasive networks of sensors that monitor traffic in real-time and record huge volumes of traffic data. These datasets constitute a rich source of information that can be used to extract knowledge useful for municipalities and citizens. In this paper we are interested in exploiting such data to estimate future speed in traffic sensor networks, as accurate predictions have the potential to enhance decision making capabilities of traffic management systems. Building effective speed prediction models in large cities poses important challenges that stem from the complexity of traffic patterns, the number of traffic sensors typically deployed, and the evolving nature of sensor networks. Indeed, sensors are frequently added to monitor new road segments or replaced/removed due to different reasons (e.g., maintenance). Exploiting a large number of sensors for effective speed prediction thus requires smart solutions to collect vast volumes of data and train effective prediction models. Furthermore, the dynamic nature of real-world sensor networks calls for solutions that are resilient not only to changes in traffic behavior, but also to changes in the network structure, where the cold start problem represents an important challenge. We study three different approaches in the context of large and dynamic sensor networks: local, global, and cluster-based. The local approach builds a specific prediction model for each sensor of the network. Conversely, the global approach builds a single prediction model for the whole sensor network. Finally, the cluster-based approach groups sensors into homogeneous clusters and generates a model for each cluster. We provide a large dataset, generated from $\sim$1.3 billion records collected by up to 272 sensors deployed in Fortaleza, Brazil, and use it to experimentally assess the effectiveness and resilience of prediction models built according to the three aforementioned approaches. The results show that the global and cluster-based approaches provide very accurate prediction models that prove to be robust to changes in traffic behavior and in the structure of sensor networks.

如今，智慧城市配备了无处不在的传感器网络，可以实时监控交通并记录大量交通数据。这些数据集构成了丰富的信息源，可用于提取对市政当局和市民有用的知识。在本文中，我们有兴趣利用此类数据来估计交通传感器网络的未来速度，因为准确的预测可能会增强交通管理系统的决策能力。在大城市中建立有效的速度预测模型提出了重要挑战，这些挑战源于交通模式的复杂性，通常部署的交通传感器的数量以及传感器网络的不断发展的性质。实际上，经常会添加传感器来监视新的路段，或者由于不同的原因（例如，维护）。因此，利用大量传感器进行有效的速度预测需要智能解决方案来收集大量数据并训练有效的预测模型。此外，现实世界中传感器网络的动态特性要求解决方案不仅具有应对流量行为变化的能力，而且还具有应对网络结构变化的能力，在这种情况下，冷启动问题是一个重要的挑战。在大型动态传感器网络的背景下，我们研究了三种不同的方法：本地，全局和基于群集。本地方法为网络的每个传感器建立一个特定的预测模型。相反，全局方法为整个传感器网络建立一个单独的预测模型。最后，基于聚类的方法将传感器分为同类聚类，并为每个聚类生成一个模型。$〜$通过部署在巴西福塔莱萨的多达272个传感器收集了13亿条记录，并将其用于实验评估根据上述三种方法构建的预测模型的有效性和弹性。结果表明，基于全局和基于群集的方法提供了非常准确的预测模型，这些模型被证明对交通行为和传感器网络结构的变化具有鲁棒性。