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

PFI：BIC-大数据创新支持的交通事故管理智能服务系统（TIMELI）

• 批准号: 1632116
• 负责人: Anuj Sharma
• 金额: $ 100万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1632116&HistoricalAwards=false
• 关键词: PFI; BIC; Smart; Service; System

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).

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于撞车、卡车倾覆或车辆熄火等交通事故造成的。拥堵每年给商业卡车运输业造成92亿美元的损失，事实证明，拥堵每分钟发生一次事故，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，通常由州交通部(DOT)运营的交通事件管理(TIM)中心监控道路上的交通事件，协调事件响应，并提供交通管理和控制，以将交通事件的影响降至最低。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件管理)，该系统在现有产品的基础上大大增强了事件风险评估和响应的能力。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，并且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，并依赖技术人员输入控制行动。为了解决这些限制，这一智能系统的目标是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以及改进的分析和机器学习。各州DOTS使用该系统可以减少事故的持续时间和影响，并提高驾车者、撞车受害者和紧急响应人员的安全。使用还将减少TIM技术人员的疲劳，降低他们的离职率。TIMELI的目标和成果是使用新兴的大规模数据分析，通过主动的交通控制减少道路事件的数量，并通过早期检测、响应和交通管理和控制将确实发生的个别事件的影响降至最低。这将使用端到端机器学习来实现态势感知，使用偏微分方程设计和快速求解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。目前在数据处理和归档、决策支持分析以及输出格式设计方面的技术差距将利用大数据技术加以解决。将接收多个大型数据流，并执行数据分析以确保质量和异常检测。将使用新的算法方法、机器学习和随机框架来检测异常异常值并实施上下文敏感的流量模型。先进的人机界面将以直观的形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。目标是开发TIMELI并将其整合到现有的TIM系统中。这些将通过以下方法完成：(1)定义TIM用户需求，并利用人为因素研究确定技术人员任务中的瓶颈；(2)开发包括大数据支持的后端解决方案、分析引擎和前端界面的原型；以及(3)在爱荷华州DOT现有的TIM环境中进行测试、评估和集成。TIMELI的多项创新将改变当前的TIM系统，创建一个智能且可靠的决策辅助系统，用于实时监控交通状况，使用咨询控制主动控制风险，快速检测交通事件，确定这些事件的位置和潜在原因，建议交通控制替代方案，并将TIM运营商的认知瓶颈降至最低。该试验台将是交通研究和教育中心的全功能交通操作实验室，与爱荷华州交通部的数据流相连。这项研究将通过吸收土木工程、电气和计算机工程、机械工程和人为因素工程的本科生和研究生研究人员来促进教育，并将生成用于开发教育材料的真实世界数据集。该项目的合作伙伴是爱荷华州州立大学(主要学术机构)、TransCore(商业智能交通系统提供商，得梅因，IA)和爱荷华州交通部(政府机构，Ames，IA)。