PFI:BIC- A Smart Service System for Traffic Incident Management Enabled by Large-data Innovations (TIMELI)

美国联邦公路管理局估计，美国道路上四分之一的拥堵是由交通事故造成的，比如撞车、翻车或车辆熄火。交通拥堵每年给商业卡车运输业造成92亿美元的损失，而交通拥堵每持续一分钟，二次撞车的风险就会增加2.8%。为了解决这些经济和安全问题，交通事故管理中心（TIM）通常由州交通部门（DOT）运营，监测道路交通事故，协调事故响应，并提供交通管理和控制，以尽量减少交通事故的影响。这项研究将开发一种新的TIM系统，称为TIMELI（由大数据创新实现的交通事故管理），与现有产品相比，该系统大大增强了事故风险评估和响应能力。目前可用的基于软件的智能交通系统（ITS）仅限于非常基本的控制，不能提供全面或动态的决策支持。这些系统在地图上显示交通数据流，并依靠技术人员输入控制动作。为了解决这些限制，这个智能系统旨在成为一个更有效的数据驱动的TIM，提供以用户为中心的信息可视化和改进的分析和机器学习。各州dot使用该系统可以减少事故的持续时间和影响，并提高驾驶者、碰撞受害者和紧急救援人员的安全性。使用还将减少TIM技术人员的疲劳和降低他们的流动率。TIMELI的目标和成果是利用新兴的大规模数据分析，通过主动交通控制来减少道路事故的数量，并通过早期发现、响应和交通管理和控制来最大限度地减少个别事故的影响。这将通过端到端机器学习来实现态势感知，利用偏微分方程设计和快速解决地理时间感知交通模型，随机模型预测控制，以及以用户为中心的高级可视化技术来辅助决策。目前在数据处理和存档、决策支持分析和输出格式设计方面的技术差距将通过大数据技术得到解决。多个大数据流将被摄取，数据分析将执行质量保证和异常检测。新的算法方法、机器学习和随机框架将用于检测异常异常值和实现上下文敏感的流量模型。先进的人机界面将以直观的格式提供信息可视化和决策建议，以最大限度地减少任何认知瓶颈。目标是发展timi并将其纳入现有的timi系统。这些将通过以下方法完成：(1)定义TIM用户需求并通过人为因素研究确定技术人员任务中的瓶颈；(2)开发一个原型，包括支持大数据的后端解决方案、分析引擎和前端接口；(3)在爱荷华州交通部现有的TIM环境中进行测试、评估和集成。TIMELI的多项创新将通过创建一个智能可靠的决策辅助系统来改变当前的TIM系统，该系统用于实时监控交通状况，通过咨询控制主动控制风险，快速检测交通事故，识别这些事件的位置和潜在原因，提出交通控制替代方案，并最大限度地减少TIM运营商的认知瓶颈。测试平台将是交通研究和教育中心的全功能交通操作实验室，该实验室与爱荷华州交通部的数据流相连。这项研究将涉及土木工程、电气与计算机工程、机械工程和人为因素工程的本科生和研究生，并将产生用于开发教材的真实数据集，从而为教育做出贡献。该项目的合作伙伴是爱荷华州立大学（主要学术机构）、TransCore（一家智能交通系统的商业供应商，爱荷华州得梅因）和爱荷华州交通部dot（政府机构，爱荷华州艾姆斯）。

The Federal Highway Administration estimates that a quarter of the congestion on U.S. roads is due to traffic incidents such as a crash, an overturned truck, or stalled vehicles. Congestion costs the commercial trucking industry $9.2 billion annually, and incidents have been shown to increase the risk of secondary crashes by 2.8 percent with every minute of congestion. To address these economic and safety issues, traffic incident management (TIM) centers, typically operated by state departments of transportation (DOT), monitor roadways for traffic incidents, coordinate incident response, and provide traffic management and control to minimize the impacts of traffic incidents. This research will develop a new TIM system, called TIMELI (Traffic Incident Management Enabled by Large-data Innovations), that has greatly enhanced capabilities for incident risk assessment and response over current products. Software-based intelligent transportation systems (ITS) that are currently available are limited to very basic controls and do not provide comprehensive or dynamic decision support. These systems display streams of traffic data on a map and rely on technicians to input a control action. To address these limitations, this smart system aims to be a more effective data-driven TIM that provides user-centric information visualization and improved analytics and machine learning. Use of the system by state DOTs can reduce the duration and impacts of incidents and improve the safety of motorists, crash victims, and emergency responders. Use will also reduce the TIM technician fatigue and reduce their turnover rates. The goal and outcome of TIMELI is to use emerging large-scale data analytics to reduce the number of road incidents through proactive traffic control and to minimize the impact of individual incidents that do occur through early detection, response, and traffic management and control. This will be achieved using end-to-end machine learning for situational awareness, the design and rapid solution of geo-temporally aware traffic models using partial differential equations, stochastic model predictive control, and user-centric advanced visualization techniques for decision assistance. Current technology gaps in data handling and archiving, analysis for decision support, and the design of output formats will be addressed using big data technologies. Multiple large data streams will be ingested and data analytics will be performed for quality assurance and anomaly detection. New algorithmic approaches, machine learning, and a stochastic framework will be used to detect anomalous outliers and implement context-sensitive traffic models. An advanced human machine interface will provide information visualization and decisions recommendations in an intuitive format to minimize any cognitive bottlenecks. The objectives are to develop TIMELI and to integrate it into an existing TIM system. These will be accomplished by the following methods: (1) defining TIM user requirements and identifying bottlenecks in technician tasks using human factors research; (2) developing a prototype that includes a big-data-enabled back-end solution, an analytics engine, and a front-end interface; and (3) conducting testing, evaluation, and integration within Iowa DOT's existing TIM environment. TIMELI's multiple innovations will transform current TIM systems by creating a smart and reliable decision assist system used to monitor traffic conditions in real time, proactively control risk using advisory control, quickly detect traffic incidents, identify the location and potential cause of these incidents, suggest traffic control alternatives, and minimize cognitive bottlenecks for TIM operators. The test bed will be the Center for Transportation Research and Education's fully functional traffic operations lab that is connected to the Iowa DOT's data streams.This research will contribute to education by involving undergraduate and graduate researchers in Civil Engineering, Electrical and Computer Engineering, Mechanical Engineering and Human Factors Engineering, and will generate real-world data sets that will be used in developing educational material.The partners in this project are Iowa State University (lead academic institutions), TransCore (a commercial provider of intelligent transportation systems, Des Moines, IA), and Iowa Department of Transportation-DOT (government agency, Ames, IA).