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Advanced Analytics with Real-Time Traffic Data: Pitney Bowes and INRIX Offer New Solution

Monday, April 28th 2014
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Summary:

Pitney Bowes and INRIX will offer advanced location analytics that will be fed back to INRIX’s traffic data solution to support real-time traffic modeling for routing and navigation. Pitney Bowes’ and consequently, be delivered to solutions for routing and navigation. Pitney Bowes’ Senior Vice President of Location Intelligence and Customer Data, James Buckley provides the details of this new partnership.

Directions Magazine (DM): The announcement indicates that Pitney Bowes will offer advanced location analytics that will be fed back to INRIX to support real-time traffic modeling and consequently, be delivered to solutions for routing and navigation. Which of Pitney Bowes’s solutions are supporting location analytics? (MapMarker? Spectrum Geocoding?)

James Buckley (JB): The core delivery to INRIX is the Pitney Bowes Spectrum Spatial product. They are using our forward and reverse geocoding with a range of Pitney Bowes data. This is sitting underneath a substantial new development focused on location search and the ability to auto complete from an address or POI search.

DM: Can you be explicit in the mobile and social channels where consumers will be aided by the advanced analytics?

JB: Apple, Android, and Garmin, among others, are consumer applications that will be aided by the advanced analytics.

DM: The “smart crowd-sourcing” of public and private sources have typically come from arrangements with long-haul trucking and other types of fleet management companies. Is this still true and what are the public sources from which the data will be gathered (e.g. local DOTs?)

JB: One of the key differentiators of INRIX real time traffic services is its ‘smart crowd sourcing’ strategy, which seamlessly fuses data inputs from a wide variety of sources, which include fleets, public sector (local, state and federal DOTs), connected cars, as well as our own consumer mobile application, INRIX Traffic.  The intelligent use of these broad spectrum of sources, each of which is used to supplement and complement each other, ensures that INRIX can provide an accurate and comprehensive view of real time traffic conditions throughout the globe. Essentially, INRIX Smart Crowdsourcing transforms UGI and single source incidents from a “pin on a map” icons to properly map-snapped, directional incident data.

DM: It is indicated that demographics and behavior patterns will be included in the analysis. These are obviously not “real-time” data feeds. What weighting would you give to these types of socially-related data into the location model that would support real-time navigation adjustments?

JB: INRIX is encouraged by the potential of combining accurate Pitney Bowes geospatial data with other information such as demographics and learned behavioral patterns to enable compelling new scenarios and value for its customers.  At this time, however, it is too early to provide specifics on precisely how these data sources will be integrated together and weighted.  It is also likely that these data sources will be leveraged differently based on end-customer requirements and geographic conditions.

DM: Since you are incorporating demographics analysis into the real-time navigation decision process, do you ask the driver for personal information so that the choices of restaurants, hotels, etc. change based on the consumer preferences or are these kinds of psychographic profiles somehow assumed or estimated based on other data inputs?

JB: Although the technology makes it possible for this to happen, it has not yet been incorporated into the user experience. However, the consumer will always have the ability to control how, and what, information will be incorporated into such advanced navigation guidance scenarios. In the future, the personalization of the real time navigation decision process will be enabled by any number of data inputs that the user opts to share with INRIX and its customers.  These may include specifically asked for preference data (such as restaurant, hotel, or other service provider choices) or from ‘learned’ behavioral data that are gleaned automatically from usage over time.


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