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Expanding Connected Vehicle Data Framework (CVDF) Data Sources to Increase Applications and Use on Texas Roadways

项目名称：	Expanding Connected Vehicle Data Framework (CVDF) Data Sources to Increase Applications and Use on Texas Roadways
摘要：	Connected vehicle (CV) technology is enabling transportation systems to become safer and smarter. Texas is assembling a robust CV ecosystem, with several CV deployments underway. At the heart is the Connected Vehicle Data Framework (CVDF), a data exchange that enables Texas Department of Transportation (TxDOT) to publish key information, such as work zone locations and travel times, as well as ingest data from other public agencies and third parties regarding traffic characteristics, road weather conditions, and safety events. Constraints in data access and format standardization, however, limit the CVDF from realizing its full potential. This project will leverage the existing CVDF that currently supports the Texas Connected Freight Corridors project to expand its efficacy through applications, data partners, and corridors. By expanding the CVDF, TxDOT will unlock new benefits—improved real-time traveler information; increased CV adoption in passenger and freight markets; and more strategic infrastructure investments. This project will deliver a CVDF Expansion Toolkit that includes: (1) New applications that improve safety and mobility (e.g., truck parking availability, road weather warning, border wait times); (2) Recommended data partners from local and regional agencies as well as third-party data providers to improve traffic operations; and (3) Corridor investment strategies that identify Texas roadways for CV operations and describe infrastructure readiness tactics.
状态：	Active
资金：	350000
资助组织：	Texas Department of Transportation
管理组织：	Texas Department of Transportation
项目负责人：	Dassi, Martin
执行机构：	Center for Transportation Research
主要研究人员：	Chin, Kristie
开始时间：	20220901
预计完成日期：	20240831
主题领域：	Data and Information Technology；Highways；Operations and Traffic Management；Planning and Forecasting；Vehicles and Equipment