DB4IoT with INRIX Trips

  • 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 […]

  • Figure 6. Select link analysis using INRIX Trip Paths

INRIX Trip Reports vs. INRIX Trip Paths

By |May 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 and perform select link analysis in tools such as DB4IoT with INRIX Trips.

INRIX is now introducing INRIX Trip Paths. In March INRIX provided us with an INRIX Trip Reports dataset […]

  • 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 […]