METRIS, short for Metropolitan Transportation Information System, is a vision of modern transportation management, centered on streams of dense GPS data from large numbers of vehicles. The data are analyzed and modeled, often in real time, to monitor and to optimize operations and security, and to guide planning policies such as congestion mitigation and air quality.
METRIS is currently applied in the study of drayage trucking around the ports of Los Angeles and Long Beach. METRIS deployment was initially funded by the USDOT Research and Innovative Technology Administration. Some components of the technology suite were developed by the University of California, Santa Barbara and the University of Washington.
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Much of METRIS falls under the general area of GPS Forensics: analyzing GPS data after the fact, to reconstruct detailed vehicle trajectories (e.g. in the aftermath of an accident, or determining what happened when a vehicle was claimed to be lost), or to study synoptic patterns of mass movements. We have developed advanced GPS hardware and software tools for both purposes.
We conduct leading-edge research in advanced geospatial methodologies, much of this in collaboration with UC Santa Barbara. Some projects are funded by the US Department of Transportation, NASA, and the California Department of Transportation. The technical scope of the work includes GPS tracking, map error remediation, remote sensing, data modeling, traffic microsimulation and travel demand modeling.
We have experience with public and private sector applications in Canada, the United States and Australia. We're actively involved in a number of professional organizations and standards development efforts.
A host of applications—LBS, ITS, EMS, retail site selection—demand precise detail on the transportation system: centerline geometry, number of lanes, turn prohibitions at intersections, and real time congestion data. There are questions about fitness for use in location referencing, geocoding and route finding. There's a need for interoperability among data bases of varying quality. We assess application needs. We use GPS and sensing technologies to update data; we develop algorithms for error detection and conflation, and communications protocols to ensure error-free transactions in location exchange.
Large organizations provide parking to thousands of employees, at great expense. We argue that they are potentially the most cost-effective market for transit operators, because of the spatial concentration of travel demand. We design optimal transit routes to serve communities of employees.
Engineers target roads for repair based on visual inspection, or using van-based sensors to measure pavement health parameters. Analyzing sub-metre hyperspectral imagery, we're able to replicate (at least in part) the findings of experts and ARAN-type sensors.
Now more popularly known as drones, from hand-held bumble bees to 30-metre wing-span behemoths, they're poised to revolutionize data gathering for transportation. We are exploring UAV sensing applications in urban management and security.
Determining where to run a new highway is a complex process, requiring detailed physical surveys and a variety of human considerations. We explore technologies such as Lidar and Ifsar for preliminary physical surveys, and develop decision support systems to facilitate the public consultation process for rights of way and alignments.
We were part of the technical team that created the ESRI® UNETRANS template for transportation (2001), and the Standard Labelled Road Network (SLRN) in Ontario, Canada (1991).
Which facilities are most critical in the event of a disaster, and how to prioritize infrastructure for maintenance and protection? How long does it take to evacuate a neighbourhood? We use optimization algorithms and microsimulation to address these issues.