Panasonic and UDoT to develop transportation data network

Panasonic North America has entered into a $50 million partnership with the Utah Department of Transportation (UDoT) to develop an advanced transportation data network. Panasonic says the network will improve safety and mobility on the road by sharing data between vehicles, infrastructure, roadways and traffic operators in real-time. The network is expected to provide the UDoT traffic operations centre with insights into crashes, severe weather or stalled vehicles. Personnel working at the centre wi
June 25, 2019 Read time: 2 mins
598 Panasonic North America has entered into a $50 million partnership with the 1904 Utah Department of Transportation (UDoT) to develop an advanced transportation data network.


Panasonic says the network will improve safety and mobility on the road by sharing data between vehicles, infrastructure, roadways and traffic operators in real-time.

The network is expected to provide the UDoT traffic operations centre with insights into crashes, severe weather or stalled vehicles. Personnel working at the centre will also be able to alert connected vehicle drivers in real-time with alternative routes and delay times.

Panasonic is hoping the agreement will support UDoT in the development of a statewide system for collecting, monitoring and shares connected and autonomous vehicle data.

Both parties will install intelligent sensors and vehicle-mounted software and equipment along selected sections of Utah highways to collect and transmit data speeds up to 10 times per second, Panasonic adds.

A central cloud-based system monitors the information from the sensor/vehicle network and automatically generates alerts that are shared with vehicles, infrastructure components and UDoT personnel.

The first phase of the system will include 40 installation sites, along with a fleet of 30 state-owned vehicles. The partners will identify locations for the system and then develop new software applications, install sensor networks along selected roads.

Panasonic has confirmed that future phases of the system will expand to include 220 installation sites and up to 2,000 vehicles.

UTC

Related Content

  • May 18, 2021
    Velodyne applies AI to traffic monitoring 
    Lidar-based AI traffic solution installed at multiple intersections in New Brunswick, New Jersey
    Read More
  • September 15, 2015
    USDOT announces next generation CV funding
    US Transportation Secretary Anthony Foxx has revealed that New York City, Wyoming, and Tampa will receive up to US$42 million to pilot next-generation technology in infrastructure and in vehicles to share and communicate anonymous information with each other and their surroundings in real time, reducing congestion and greenhouse gas emissions and cutting the unimpaired vehicle crash rate by 80 per cent. As part of the Department of Transportation (USDOT) national connected vehicle pilot deployment progra
    Read More
  • August 5, 2013
    Travel times halve for tolling converts
    The Port Mann Bridge in Vancouver is a prime example of how the latest ITS systems enable new infrastructures to be built and paid for while still providing additional user benefits. Vancouver has 2.2 million inhabitants and, like so many major cities, is divided into two by a river, the Frazer river. This combination makes Vancouver the second most congested city in North America and the most congested in Canada. Through the middle of the city runs the Trans-Canadian Highway 1 which crosses the Frazer Riv
    Read More
  • April 10, 2014
    Cellint measures speed and travel time without roadside infrastructure
    Collecting speed and travel time data without using roadside infrastructure could offer new possibilities to cash-strapped road authorities. Streaming video may be useful for traffic controllers to monitor incidents and automatic number plate recognition may be required for enforcement, but neither are necessary for many ITS functions. For instance travel times, tailbacks, percentage of vehicles turning, origin and destination analysis can all be done using Bluetooth and/or WI-Fi sensors and without video o
    Read More

Highlighted Content