详情

Best Practices for Data Fusion of Probe and Point Detector Data

项目名称：	Best Practices for Data Fusion of Probe and Point Detector Data
摘要：	Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and comprehensive information than that provided by any individual data source. In a transportation context, transportation agencies are seeking to define the types and characteristics of data for entry into data fusion engines. Research is needed to identify the challenges, issues, and proven or potential practices for performing data fusion to measure or forecast travel time, speed, reliability, and other aspects of operational performance on roadway networks. Traffic datasets of interest include point sensors; Bluetooth; data from GPS devices embedded in smartphones, personal navigation devices, taxis, and fleets; third-party travel time data; and emerging connected vehicle (CV) data sets. Research that enables better knowledge of the network state could help improve traffic management and planning decisions to address impacts of recurrent and non-recurrent (e.g., incident-related) congestion. Improved network state estimates could also enhance safety outcomes by identifying locations with high crash rates and anomalous traffic flow conditions. The objectives of this research are to do as follows: (1) Develop a process to (a) identify specific objectives for data fusion; (b) identify data sources available for fusion; (c) select the most suitable data for fusion; and (d) facilitate the fusion itself. (2) Develop guidelines for transportation agencies to facilitate data fusion, improve data reporting, and ultimately improve traffic management.
状态：	Active
资金：	200000
资助组织：	National Cooperative Highway Research Program；American Association of State Highway and Transportation Officials (AASHTO)；Federal Highway Administration
项目负责人：	Jared, David M
执行机构：	Michael L Pack, LLC
主要研究人员：	Pack, Michael L
开始时间：	20220907
预计完成日期：	20240307
主题领域：	Data and Information Technology；Highways；Operations and Traffic Management；Safety and Human Factors