Real-Time Data Capture and Management

Real-Time Data Capture and Management is the creation and expansion of access to high-quality, real-time, multi-modal transportation data captured from connected vehicles, mobile devices, and infrastructure.

The vision for the Real-Time Data Capture and Management research program is the active acquisition and systematic provision of integrated, multi-source data to enhance current operational practices and to transform future surface transportation system management.

Research Plan

The objective of the Real-Time Data Capture and Management research program is to enable the development of environments that support the collection, management, integration, and application of real-time transportation data.

Real-time data applications have the potential to increase highway safety and operational efficiency nationwide. The data will allow travelers to make better-informed travel decisions. Public and private sector data on all modes and roads can be used to transform transportation management.

Real-time data sets also have the potential to support a range of multi-modal mobility applications. Real-time information on parking availability and transit schedules can enable smarter mode choice decisions, and yield time and fuel efficiencies for travelers. Updated freight movement data helps commercial freight operators to optimize operations.

The results of the Real-Time Data Capture and Management research program will reveal opportunities for achieving greater efficiencies within our transportation systems. Some of the data types that can be captured and managed include: situational safety; environmental conditions; congestion data; and cost information (derived from both traditional sources — traffic management centers, Automated Vehicle Location systems; and non-traditional sources — mobile devices, IntelliDrive^SM applications). Data also can be collected from toll facilities, parking facilities, and transit stations.

The Real-Time Data research program includes the following tracks:

Track 1: Engage stakeholders for input across all phases, from foundational analysis to pilot
deployment.

Track 2: Develop data environments and address technical, institutional, and standards
issues surrounding the collection and dissemination of data.

Track 3: Conduct proof-of-concept tests, and test standards, procedures, tools, and
protocols to produce implementation guidance for a real-world environment.

Track 4: Conduct pilot deployments and demonstrate the data capture and data
management techniques in an operational setting, while providing stakeholders with opportunities to develop systems beyond the life of the program.

Track 5: Develop evaluation and performance measures.

Track 6: Coordinate outreach and technology transfer. Test data sets, data collection, and analysis methodologies will be shared with stakeholders, with information available to the broader transportation community.

This research program will build on the existing Real-Time Information Market Assessment and the recent Real-Time System Management Information Program.

DOT will engage a wide range of stakeholders to help guide the research program. Related U.S. DOT research programs, such as Dynamic Mobility Applications and the AERIS Program, are expected to define data requirements, identify information gaps, and use the real-time data sets that will be developed under this program.

Data Capture High Level Roadmap | HTML

Research Goals

To systematically capture real-time, multi-modal data from connected vehicles, devices, and infrastructure.
To develop data environments that enable integration of high-quality data from multiple sources for transportation management and performance measurement.

Research Questions

What data are available today from both traditional and non-traditional sources? What is the quality of the data?
How can probe data be integrated with traditional data sources to support traffic/transit/freight applications?

Research Outcomes

The results of this research program will be used to develop data environments and demonstrations that show the value of ubiquitous real-time multi-modal information.