I-Corps: Intelligent Traffic Management System

这个i-Corps项目的更广泛的影响/商业潜力将是显著减少交通拥堵、车辆碰撞风险和燃料消耗。这可能会带来巨大的经济效益，因为交通拥堵会给经济带来巨大的成本。预计智能交通事件管理系统将被州交通部(DOTS)用于减少事件的持续时间和影响，并提高驾车者、撞车受害者和应急人员的安全。DOTS的其他好处将是：减少人员培训需求，改善工作量条件，提高工人保留率。管理交通的州、市和城市机构将使用该解决方案作为智能和可靠的决策辅助系统，以实时监控交通状况，使用咨询控制主动控制风险，快速检测交通事件，确定事件的位置和潜在原因，建议交通控制替代方案，并最大限度地减少交通事件管理操作员的认知瓶颈。该系统使用新的机器学习技术和基于图的趋势过滤方法，对从监控交通网络的传感器获得的海量、空间相关的多维时间序列数据进行异常检测和状态估计。事实证明，这些方法在检测有故障的传感器和快速报告交通事件方面优于最先进的方法。智能交通管理系统还将提供先进的人机界面，以减少交通事件管理人员的视觉、听觉、认知和精神运动(VACP)工作量。该系统数据架构使用最先进的数据管道来获取数据，使用大规模并行方法来进行流和批处理分析，使用分布式数据库来进行可扩展的数据存储，并使用GPU增强的方法来对大量数据进行快速数据可视化。

The broader impact/commercial potential of this I-Corps project will be significant reductions in traffic congestion, vehicle crash risk, and fuel consumption. This will potentially have large economic benefits as traffic congestion causes significant costs to the economy. It is anticipated that intelligent traffic incident management system will be used by state departments of transportation (DOTs) to reduce the duration and impacts of incidents and improve the safety of motorists, crash victims, and emergency responders. Additional benefits to the DOTs will be: reduced personnel training needs, improved workload conditions, and increased worker retention rates. State, municipal and city agencies managing traffic will use this solution as a smart and reliable decision-assist system to monitor traffic conditions in real time, proactively control risk using advisory control, quickly detect traffic incidents, identify the location and potential cause of incidents, suggest traffic control alternatives, and minimize cognitive bottlenecks for traffic incident management operators.This I-Corps project is focused on understanding the product-market fit for intelligent traffic management systems. The proposed system uses novel machine learning techniques and graph-based trend filtering approaches for anomaly detection and state estimation for massive, spatially correlated, multi-dimensional time series data obtained from sensors that monitor the traffic networks. These approaches have been shown to be superior to the state-of-the-art approaches for detecting faulty sensors and quickly reporting traffic incidents. An advanced human-machine interface will also be provided for the Intelligent Traffic Management system with the aim to reduce the Visual, Auditory, Cognitive and Psychomotor (VACP) workload of the Traffic Incident Managers. The system data architecture uses state-of-the-art data pipelines for data ingestion, massively parallel methods for stream and batch analytics, distributed databases for scalable data storage, and GPU-augmented methods for fast data visualization of large volumes of data.