RESEARCH INITIATION AWARD GRANT: STATISTICAL MODELS FOR REAL-TIME PARAMETER PREDICTION FROM PROBE VEHICLE DATA AT TRAFFIC INTERSECTIONS

研究启动奖：根据交通路口探测车辆数据进行实时参数预测的统计模型

• 批准号: 1238705
• 负责人: Gurcan Comert
• 金额: $ 19.83万
• 依托单位: Benedict College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1238705&HistoricalAwards=false
• 关键词: RESEARCH; INITIATION; AWARD; GRANT; STATISTICAL

The objectives of the project entitled: Statistical Models for Real-Time Parameter Prediction from Probe Vehicle Data at Traffic Intersections, are to develop new queue length estimate models at traffic intersections, to teach undergraduate students mathematical and computational modeling skills, and to develop project based undergraduate courses.The project will investigate the estimation of system parameters such as queue length, delay, and flow rate at traffic intersections based on data from vehicles instrumented with wireless communication and location tracking technologies, so called probe vehicles. Estimating system states in real-time can enable optimal control through efficiently allocating the available capacity such that a defined performance metric is optimized, for example the queue length is minimized. To estimate these performance measures in real time, various surveillance technologies are being employed to measure traffic flow parameters which are subsequently utilized in models for delay estimation and prediction. However, these detection technologies are not effective in estimating queue lengths or delays. Research is needed to develop models using probe information such as count, location, and time, and to understand how probe vehicle technology could potentially improve the estimation of desired parameters. During this project,first queue length estimators for two-lane isolated intersections with left and right turn movements for Poisson arrivals, then platoon arrivals will be developed. Fixed and random service time distributions will be investigated. Finally, developed models will be tested with VISSIM microscopic simulation platform in order to show if the models are accurately estimating the desired parameters and under what conditions the models are adequate.

该项目名为：交通交叉口探测车实时参数预测的统计模型，旨在开发新的交通交叉口排队长度估计模型，教授本科生数学和计算建模技能，并开发基于项目的本科课程。该项目将研究基于安装了无线通信和位置跟踪技术的车辆(即所谓的探测车)的数据估计交通交叉口的排队长度、延误和流量等系统参数。实时估计系统状态可以通过有效地分配可用容量来实现最优控制，从而优化所定义的性能度量，例如最小化队列长度。为了实时估计这些性能指标，各种监控技术被用来测量交通流参数，这些参数随后被用于延误估计和预测的模型中。然而，这些检测技术在估计队列长度或延迟方面并不有效。需要进行研究，以利用探测信息(如计数、位置和时间)开发模型，并了解探测车技术如何潜在地改进所需参数的估计。在本项目中，首先对泊松到达的双车道左右转单车道交叉口的排队长度估计器进行了研究，然后开发了排队到达的估计方法。固定和随机的服务时间分布将被调查。最后，在VISSIM微观仿真平台上对所开发的模型进行测试，以显示模型是否准确地估计了期望的参数，以及在什么条件下模型是合适的。