ABSTRACT

Current data-driven traffic prediction models are usually trained with large datasets, e.g., several months of speeds and flows. Such models provide very good fit for ordinary road conditions, but often fail just when they are most needed: when traffic suffers a sudden and significant disruption, e.g., a road incident. In this work, we describe QTIP: a simulation-based framework for quasi-instantaneous adaptation of prediction models upon traffic disruption. In a nutshell, QTIP performs real-time simulations of the affected road for multiple scenarios, analyzes the results, and suggests a change to an ordinary prediction model accordingly. QTIP constructs the simulated scenarios per properties of the incident, as conveyed by real-time distress signals from In-Vehicle Monitor Systems, which are becoming increasingly prevalent worldwide. We experiment QTIP in a case study of a Danish motorway, and the results show that QTIP can improve traffic prediction in the first critical minutes of road incidents.

摘要

当前的数据驱动交通预测模型通常使用大型数据集进行训练，例如几个月的速度和流量。这样的模型非常适合普通的道路条件，但往往在最需要它们的时候失败：当交通遭受突然和重大的中断时，例如道路事故。在这项工作中，我们描述了 QTIP：一种基于模拟的框架，用于在交通中断时准瞬时适应预测模型。简而言之，QTIP 针对多个场景对受影响的道路进行实时模拟，分析结果，并相应地建议对普通预测模型进行更改。QTIP 根据事件的属性构建模拟场景，由车载监控系统的实时遇险信号传达，这些信号在世界范围内变得越来越普遍。