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  • DB4IoT with INRIX Trips for DKS Clark County WA - On-ramp and Off-ramp are selectors in the side panel

Generate Freeway Origin-Destination Matrices Interactively In DB4IoT with INRIX Trips

By |May 17th, 2019|

There are three types of points in the INRIX Trips data; Origin points, Waypoints and Destination points. Trips start at an origin point, generate data at a number of waypoints and end at a destination point. In DB4IoT with INRIX Trips you can select all waypoints on a specific road and generate an Origin-Destination Matrix for all trips passing through that road. In addition, the platform provides routing information (via waypoints) that visualize the route that was used by a trip.

If you manage a freeway system you are interested in trip routing details, rather than simply the origins and destinations of the trips. For instance, you may want to know when and where vehicles […]

  • The histogram shows the distribution by time of day.

NYC Motor Vehicle Collisions Data in DB4IoT

By |April 10th, 2019|

The NYC Open Data portal provides an interesting data set from the NYPD that is a breakdown of every motor vehicle collision in NYC by location and injury. Each record represents a collision in NYC by city, borough, precinct and cross street. This data can be used by the public to see how dangerous/safe intersections are in NYC.

We imported the data into the DB4IoT analytics platform to visualize the six-year period from July 2012 through June 2018. The following series of images detail the results. Public data sets such as this can be combined in DB4IoT with transportation, traffic, smart-city and ITS data from other soutces such as INRIX, GTFS feeds, CAD/AVL feeds etc. to give planners and traffic engineers […]

  • ITS-WA-Presentation

ITS Washington Annual Meeting Presentation – Using Connected Vehicle Data to Estimate Greenhouse Gas Emissions

By |December 19th, 2018|

Eimar Boesjes, Moonshadow CEO, delivered a technical presentation at the ITS Washington annual meeting on December 11th, 2018 in Seattle.

Estimating vehicle emissions from connected vehicle data does not replace existing environmental models, roadside emissions measurements or environmental models. Rather, it augments these. We think it would be extremely interesting to compare the emissions estimates from DB4IoT with INRIX Trips with actual measurements from roadside air quality measurements, with emissions calculations from gasoline sales and with the existing environmental models. These different approaches each give a different insight and they can be used to ‘ground truth’ each other.

For additional background information please also read our October blog post on this topic.

View the full presentation here.

  • DB4IoT with INRIX Trips - Step 1, Speed in Copenhagen

Deriving Greenhouse Gas Emissions Estimates from Connected Vehicle Data

By |October 31st, 2018|

Eimar M. Boesjes, CEO, Moonshadow Mobile
Joshua Skov, Industry Mentor, Lundquist College of Business, University of Oregon
October 26, 2018

What does the carbon footprint from transportation look like? In particular, how could you map it?

Typically, carbon footprinting exercises for local and regional jurisdictions deliver highly aggregated results. Even when the unit of analysis is geographic, the data is all about pie charts and tables. The results for New York City are…for all of New York City. Meaningful and important, but it leaves us missing something truly geographic in nature. Moonshadow Mobile recently completed a proof of concept (PoC) to derive greenhouse gas emissions estimates using measured vehicle movement data for Copenhagen, Denmark. This shows the spatial presentation of greenhouse gas emissions from […]

  • Generate Origin-Destination Matrices in Seconds

DB4IoT with INRIX Trips: Generate Origin-Destination Matrices in Seconds

By |August 22nd, 2018|

DB4IoT with INRIX Trips delivers the capability to generate Origin-Destination matrices from databases with hundreds of millions of waypoint records within seconds. This makes generating O/D matrices an interactive process. Users can change the time periods, date range or vehicle types and DB4IoT generates the O/D matrix automatically. This video shows how quick and easy it is to generate an O/D matrix in DB4IoT with INRIX Trips.

Please accept marketing-cookies to watch this video.

DB4IoT, Moonshadow’s database engine for the Internet of Moving Things, was purpose-built for time-series geospatial data about moving objects (IoMT data). DB4IoT is faster and more efficient than traditional database solutions in delivering cutting-edge analytics about the movement of millions of vehicles. INRIX, the […]

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Moonshadow Mobile Partners with INRIX to Deliver Powerful Travel Pattern Analytics Platform

By |June 4th, 2018|

Novi, MI – June 4, 2018 – Today, Moonshadow Mobile, Inc. announced a partnership with INRIX, the world leader in transportation analytics and connected car services, to incorporate INRIX Trips Reports into Moonshadow’s DB4IoT platform. The solution offers transportation agencies an intuitive, cloud- based origin-destination analytics platform for understanding the trips consumers and fleets make over time on maps, charts, graphs and animations.

“DB4IoT with INRIX Trips transforms and optimizes operations for departments of transport and public transit agencies, informs short- and long-term transportation planning and engineering efforts, and unleashes the potential of connected vehicle data,” said Eimar Boesjes, CEO at Moonshadow Mobile. “The addition of INRIX Trips data to DB4IoT will shed light on existing infrastructure usage, pinpoint challenges and […]