I-Corps: Intelligent Traffic Management System

I-Corps项目的广泛影响和商业潜力将显著减少交通拥堵、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。预计智能交通事故管理系统将被国家交通部门（DOTs）使用，以减少事故的持续时间和影响，并提高驾驶者、碰撞受害者和应急响应人员的安全性。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可靠的决策辅助系统，实时监控交通状况，通过咨询控制主动控制风险，快速检测交通事故，确定事故的位置和潜在原因，提出交通控制替代方案，并最大限度地减少交通事故管理运营商的认知瓶颈。这个I-Corps项目的重点是了解适合智能交通管理系统的产品市场。该系统使用新颖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大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。这些方法已被证明优于检测故障传感器和快速报告交通事故的最先进方法。智能交通管理系统亦会提供先进的人机界面，以减轻交通事故管理人员在视觉、听觉、认知和精神运动方面的工作量。系统数据架构使用最先进的数据管道进行数据摄取，大规模并行方法用于流和批处理分析，分布式数据库用于可扩展的数据存储，以及gpu增强方法用于大量数据的快速数据可视化。

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.