L o a d i n g
Overviewintelligent-transportation-systems-itsits-joint-program-office-jpospeed-datatraffic-detectorstraffic-flowtravel-timework-zones
Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland 2019 Average Annual Daily Traffic data
Additional Information
KeyValue
Common-Core_Bureau-Code021:15
Common-Core_Contact-Emailelina.zlotchenko@dot.gov
Common-Core_Contact-NameElina Zlotchenko
Common-Core_Geographic-CoverageMaryland highway network
Common-Core_Licensehttp://www.usa.gov/publicdomain/label/1.0/
Common-Core_Program-Code021:042
Common-Core_Public-Access-Levelpublic
Common-Core_PublisherFederal Highway Administration
categories{}
owner_display_nameITS-JPO
source_created_at2024-09-16T19:26:06.000Z
source_updated_at2024-11-20T17:52:54.000Z
harvest_object_id1619c87e-e642-43d1-ad85-761be142b908
harvest_source_ida7dcdbaf-e4e3-4821-be64-d4ab014fcc67
harvest_source_titleDOT Open Data Catalog
Files
- CSVData for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case Raw Maryland Average Annual Daily Traffic 2019