Speed prediction in large and dynamic traffic sensor networks

Magalhaes Regis Pires; Lettich Francesco; Macedo Jose Antonio; Nardini Franco Maria; Perego Raffaele; Renso Chiara; Trani Roberto

摘要

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 similar to 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. (C) 2019 Elsevier Ltd. All rights reserved.

机译：如今，智能城市现在配备了普遍的传感器网络，可以实时监控流量并记录大量的流量数据。这些数据集构成了丰富的信息来源，可用于提取对市政当局和公民有用的知识。在本文中，我们有兴趣利用这些数据来估计交通传感器网络中的未来速度，因为准确的预测有可能增强交通管理系统的决策能力。大城市建设有效速度预测模型构成了源于交通模式的复杂性的重要挑战，通常部署的交通传感器数量以及传感器网络的不断变化的性质。实际上，经常添加传感器以监测新的道路段或由于不同的原因（例如，维护）而替换/删除。利用大量传感器用于有效速度预测，因此需要智能解决方案来收集大量数据和列车有效预测模型。此外，现实世界传感器网络的动态性质呼吁解决方案，这些解决方案不仅是流量行为的变化，而且还可以改变网络结构，其中冷启动问题代表一个重要的挑战。我们在大型和动态传感器网络的背景下研究三种不同的方法：本地，全局和基于集群。本地方法为网络的每个传感器构建特定预测模型。相反，全局方法为整个传感器网络构建一个预测模型。最后，基于群集的方法将传感器组分为同类群集，并为每个群集生成模型。我们提供一个大型数据集，从福塔莱萨，巴西在福塔莱萨，巴西部署的272名传感器中收集的类似收集的1.3亿条记录，并用它来通过实验评估根据上述三种方法建立的预测模型的有效性和恢复力。结果表明，全球和基于群集的方法提供了非常精确的预测模型，该模型被证明是稳健的，以便在交通行为和传感器网络的结构中变化。（c）2019 Elsevier Ltd.保留所有权利。

Speed prediction in large and dynamic traffic sensor networks

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