QTIP：根据事件参数对流量模型进行基于模拟的快速调整
QTIP: Quick simulation-based adaptation of Traffic model per Incident Parameters
摘要：
目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。这些模型非常适合普通的道路条件，但往往在最需要的时候失败：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：一个基于模拟的框架，准瞬时适应交通中断后的预测模型。简而言之，QTIP针对多种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根据受影响车辆发出的即时遇险信号所传达的事件属性构建模拟场景。这种实时信号由车载监控系统提供，车载监控系统在世界范围内变得越来越普遍。我们实验QTIP在丹麦高速公路的案例研究，结果表明，QTIP可以提高交通预测的第一个关键分钟的道路事件。
英文摘要：
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, such as 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 immediate distress signals from affected vehicles. Such real-time signals are provided by In-Vehicle Monitor Systems, which are becoming increasingly prevalent world-wide. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
