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Cooperative Bayesian Estimation of Vehicular Traffic in Large-Scale Networks

机译：大型网络中车辆交通的协同贝叶斯估计

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摘要

Intelligent transportation systems have enormous potential for improving the quality of our lives. They rely on traffic monitoring and control infrastructures to enable an efficient management of mobility. A crucial task is the estimation or prediction of traffic flows by large-scale sensor networks, which is a topic that has been attracting increasing attention in recent years because of its relevance in traffic control over urban areas or freeways. In this paper, we propose an innovative stochastic method for vehicular traffic estimation based on a distributed reconstruction of the density field through the cooperation of smaller monitoring subnetworks. The method guarantees high accuracy (because of information sharing) and, at the same time, moderate computational cost (due to distributed processing). Moreover, subnetworks do not need to exchange sensitive information (e.g., raw data) but simply traffic beliefs. We evaluate the performance of the method on simulated single-lane road scenarios, highlighting the potential benefits of the cooperative approach. As an example of application, we consider a fragmented monitoring scenario characterized by several sensor failures and we show how the proposed approach can overcome the problem related to the sensor malfunctions leveraging on information shared with neighboring subnetworks.
机译：智能交通系统具有改善我们生活质量的巨大潜力。他们依靠流量监视和控制基础结构来实现对移动性的有效管理。一项关键任务是通过大型传感器网络估算或预测交通流量，由于其与市区或高速公路交通控制的相关性，近年来这一话题已引起越来越多的关注。在本文中，我们提出了一种创新的用于车辆交通量估计的随机方法，该方法是通过较小的监控子网的协作，基于密度场的分布式重构而实现的。该方法保证了高精度（由于信息共享），并且同时保证了适度的计算成本（由于分布式处理）。此外，子网不需要交换敏感信息（例如，原始数据），而仅需要交换流量信念。我们评估了该方法在模拟单车道道路情景下的性能，突出了这种合作方法的潜在优势。作为一个应用示例，我们考虑了一个具有多个传感器故障特征的分散监视场景，并展示了该方法如何利用与相邻子网共享的信息来克服与传感器故障相关的问题。

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