Network conflation is a continuing necessity and difficult task for agencies using geo-referenced maps and databases. Conflation is the process of identifying common points and references to reconcile two or more geo-datasets across overlapping areas. Because of differences in scales, resolutions, and accuracy or conventions, data referring to the same location often do not have the same geographic reference and cannot be combined easily. This leads to defining “near enough” criteria to expect two references to represent the same feature.
Guidelines for Mobility Data Sharing Governance and Contracting is a short set of recommended guidelines for data sharing that consider the goals of both public agencies and mobility service providers, as well as the need to protect consumer privacy.
The HPMS is a national level highway information system that includes data on the extent, condition, performance, use, and operating characteristics of the Nation's highways. In general, the HPMS contains administrative and extent of system information on all public roads, while information on other characteristics is represented in HPMS as a mix of universe and sample data for arterial and collector functional systems.
The University of California, Berkeley Partners for Advanced Transportation Technology (PATH) and Caltrans have developed an open source architecture for an ICM Data Hub for the I-210 Pilot project.
The ICM Knowledgebase is intended to be a highly-useable, reliable 'one-stop' searchable online reference that provides transportation professionals the tools, strategies, sample documents and knowledge they need to successfully implement ICM in their corridors. https://www.its.dot.gov/research_archives/icms/knowledgebase.htm
The IRCO was developed as part of NCHRP 17-75 and is presented in NCHRP Research Report 904.
This report provides an executive level synopsis of the United States Department of Transportation (USDOT) ICM demonstration project (specifically) and program in general, including an explanation of the ICM concept and program structure, key accomplishments and findings, future needs, and the outlook for national deployment.
Describes requirements, technical issues, technologies, and practices that will be necessary to collect, use and share large volumes of messages from roadside devices to the TMC. Current intelligent transportation systems (ITS) devices on the roadside typically have a one-to-one relationship with the traffic management system or center, where each device sends the data collected locally to the system.
This report describes requirements, technical issues, technologies, and practices that will be necessary to collect, process, store, use and share large volumes of messages from road side equipment (RSE) to the traffic management centers (TMCs). These recommendations could find their way into data sharing standards for connected and automated vehicles (CAV) that could better define practices.
The purpose of this report is to 1) Identify how big data tools and technologies can be used in traffic management systems or TMCs; 2) Develop potential use cases for integrating big data technology and tools into traffic management systems or TMCs; 3) Assess how connected vehicle and traveler related data could be used to enhance the operation of traffic management systems or TMCs; 4) Analyze how the sharing of data with other TMCs, systems, connected vehicles and travelers; and agency business proce