Skip to main content

XYZT.ai adds time to the mobility equation

Timestamps on critical ITS data allow organisations to drive additional insights
By Andrew Bardin Williams April 29, 2024 Read time: 2 mins
Data from Hamburg (image: XYZT.ai)

Location information has revolutionised the transportation industry, giving organisations pinpoint accuracy of vehicles, hazards and other road users across a variety of ITS applications dealing with traffic monitoring, autonomous driving, collision avoidance, pedestrian safety and others.

Belgian company XYZT.ai has taken this visibility one step further by adding timestamps to critical ITS data, allowing organisations to drive additional insights that take travel time and time of day into account. The company then presents this data in a visually appealing way that makes it easier for users to understand and work with the data in a meaningful way.

The City of Hamburg, for example, uses XYZT.ai for mobility analytics with floating vehicle data from Inrix.

According to Bart Adams, the company’s CTO, most companies look at data in the aggregate to identify trends and patterns - but it’s also important to be able to look at individual events that take place at a certain time. This includes a fleet manager tracking delivery timeframes, a delivery service optimising routes during the morning rush hour or a traffic engineer auditing travel times displayed on variable message signs. Adding time on top of location data and then presenting it in a way that is easy to consume makes this possible.

“There are billions of data points generated across a city every day,” said Lida Joly, XYZT.ai CEO, at last week's ITS America Conference & Expo 2024 in Phoenix, AZ. “It’s important that people are able to visualise this data in an effective way that allows them to make cities safer and better.”

For more information on companies in this article

Related Content

  • V2X: The design challenges
    May 2, 2018
    The connected future throws up a number of enticing possibilities for us all. But, says Houman Zarrinkoub of MathWorks, issues around visualisation, prototyping and model evolution need to be examined carefully. We are all aware of the huge amount of investment going into driverless car technologies. With the likes of Volvo, Tesla and BMW getting in on the act, soon they will be a common sight on our roads. However, for this to occur, the vehicles must be able to connect with each other and ensure driver
  • Fetch.ai launches blockchain AI parking
    November 20, 2020
    Scheme with Datarella will reward Munich's drivers for parking in less popular destinations
  • Redflex deal moves it into AI territory 
    November 27, 2020
    Acquisition of RoadMetric is to add video detection and data analytics to firm's solutions
  • ITS advancement lays beyond benefit-cost analysis
    May 29, 2013
    Shelley Row, former Director of the US Department of Transportation’s ITS Joint Program Office, gives her views on the way forward for the industry. We, as intelligent transportation system (ITS) proponents and engineers, tend to be overly fixated on benefit-cost data. We want decisions to be made on logical grounds for which benefit-cost calculations are optimal. While benefit-cost data is necessary, it is not always sufficient. We can learn from our history where we see three broad groups of ITS deploymen