Bob Lee

About Bob Lee

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So far has created 17 blog entries.
  • DB4IoT - Create Freeway On-Ramp to Off-Ramp Origin-Destination Matrices

DB4IoT – Create Freeway On-Ramp to Off-Ramp Origin-Destination Matrices

By |October 8th, 2019|

In this DB4IoT Mobility Analytics Platform use-case video we’ll demonstrate how to create freeway, on-ramp to off-ramp, origin-destination matrices. You’ll also learn how to define freeway filter networks and how to analyze traffic that is avoiding a freeway because of congestion.

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  • DB4IoT - Introducing A Network of Pass-Through Gates

DB4IoT – Introducing a Network of Pass Through Gates

By |July 18th, 2019|

We’ve added some exciting new features to empower engineers, planners and agencies to easily create networks of pass-through gates in the DB4IoT interactive mobility analytics platform. These networks can be as simple or complex as necessary to answer the specific questions at hand for your detailed analysis. Watch the following video for an overview.

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  • INRIX Webinar - DB4IoT with INRIX Trip Paths & Trip Reports 2018.0618

DB4IoT and INRIX Trip Paths – Webinar Presentation

By |June 19th, 2019|

INRIX  just announced the availability of a powerful new solution for understanding the movement of people through the trips they take – INRIX Trip Paths. The DB4IoT mobility analytics platform is terrific for deploying the INRIX Trips Paths data for transportation analysis.

INRIX Trip Paths is billions of GPS data points transmitted by moving vehicles or devices expertly map-matched by INRIX to road segment data from sources like OpenStreetMap. It is actual observed individual trips, including associated entry and exit times for each segment of road.

INRIX teamed with Moonshadow and the Center for Advanced Transportation Technology Laboratory at the University of Maryland on a webinar to discuss use cases and trial results in working with this exciting new dataset from INRIX.

Watch […]

  • Planet of Cars 06-11-19

Moonshadow Launches DB4IoT Transportation Data for GHG Reporting

By |June 11th, 2019|

DB4IoT Transportation Data for GHG Reporting from Moonshadow provides detailed insight into the travel activity patterns in cities, metro areas and counties and can be used by consultants and local governments to gain a deeper understanding of when and where transportation is generating the most emissions.

DB4IoT Transportation Data for GHG Reporting delivers maps and spreadsheets that can be included in Community GHG Inventory Reports, Climate Action Plans and Transportation Plans that consultants prepare for cities, counties, MPOs and DOTs or can be used by local governments themselves to support new policies and plans. Moonshadow uses connected-vehicle data from millions of actual completed trips to generate DB4IoT Transportation Data for GHG Reporting.

More Evidence-Based Information

DB4IoT Transportation Data for GHG Reporting delivers more […]

  • Introducing the O-D CO2 Matrix

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

By |June 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 and time for each point. By aggregating this data over time, we can derive detailed insights about O/D (Origin/Destination) patterns over the time of day and day of the week […]

  • 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 […]