论文标题
RADNET：使用流量预测，时空路线网络中的事件预测
RadNet: Incident Prediction in Spatio-Temporal Road Graph Networks Using Traffic Forecasting
论文作者
论文摘要
时空系统中有效且准确的事件预测对于最大程度地减少服务停机时间和优化性能至关重要。这项工作旨在利用历史数据来使用时空预测来预测和诊断事件。我们考虑道路交通系统的特定用例，事件采取异常事件的形式，例如事故或破碎的车辆。为了解决这个问题，我们开发了一种称为RADNET的神经模型，该模型预测系统参数，例如未来时间段的平均车辆速度。由于这样的系统在很大程度上遵循每日或每周的周期性，因此我们将Radnet的预测与历史平均值进行比较，以标记事件。与先前的工作不同，radnet在两个排列中渗透了空间和时间趋势，最后在预测之前结合了密集的表示。这促进了知情推理和更准确的事件检测。具有两个公开可用和一个新的道路交通数据集的实验表明，与最先进的方法相比，所提出的模型的预测F1得分高达8％。
Efficient and accurate incident prediction in spatio-temporal systems is critical to minimize service downtime and optimize performance. This work aims to utilize historic data to predict and diagnose incidents using spatio-temporal forecasting. We consider the specific use case of road traffic systems where incidents take the form of anomalous events, such as accidents or broken-down vehicles. To tackle this, we develop a neural model, called RadNet, which forecasts system parameters such as average vehicle speeds for a future timestep. As such systems largely follow daily or weekly periodicity, we compare RadNet's predictions against historical averages to label incidents. Unlike prior work, RadNet infers spatial and temporal trends in both permutations, finally combining the dense representations before forecasting. This facilitates informed inference and more accurate incident detection. Experiments with two publicly available and a new road traffic dataset demonstrate that the proposed model gives up to 8% higher prediction F1 scores compared to the state-of-the-art methods.
