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Introducing the O/D CO2 Matrix Using Connected Vehicle Data to Reduce Greenhouse Gas Emissions

By Bob Lee|2019-06-11T22:45:25+00:00June 10th, 2019|

On June 5th at ITS America in Washington DC, Moonshadow CEO, Eimar Boesjes, delivered a presentation titled “Introducing the O/D CO2 Matrix Using Connected Vehicle Data to Reduce Greenhouse Gas Emissions” at a technical session and panel discussion.

Millions of connected vehicles generate detailed movement data. After anonymizing the data it can be used to gain insight into the movement patterns of vehicles as well as which movements generate the most greenhouse gas (GHG) emissions.

Anonymized connected vehicle data includes the origin, destination and waypoints of trips. Timestamps give us the day […]

INRIX Trip Reports vs. INRIX Trip Paths

By Eimar Boesjes|2019-05-30T07:23:57+00:00May 30th, 2019|

Three years ago, INRIX introduced INRIX Trips, also known as INRIX Trip Reports. INRIX Trip Reports provides accurate insight into the trips people take, including where they begin and end their journeys and all the waypoints in between. There are three types of points in the INRIX Trip Reports 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. The INRIX Trip Reports data can be used to analyze movement patterns, generate origin/destination matrices […]

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

By Eimar Boesjes|2019-05-17T20:05:38+00:00May 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 […]

NYC Motor Vehicle Collisions Data in DB4IoT

By Bob Lee|2019-04-10T21:26:30+00:00April 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 […]

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

By Bob Lee|2019-03-25T21:09:46+00:00December 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 […]

Deriving Greenhouse Gas Emissions Estimates from Connected Vehicle Data

By Bob Lee|2018-10-31T19:51:38+00:00October 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 […]

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