Ford and Hewlett-Packard use big data to better manage fleets

Ford Motor Company engineers have completed a real-world driving experiment with HP, discovering which commuting commonalities could provide future breakthroughs for better managing fleets, personalized services and recommendations for individual drivers. Using HP’s Big Data Discovery Experience Services and the HP Haven big data platform, the engineering team gathered data and analyzed it to determine possibilities for lowering operating costs and optimizing underutilized vehicles for fleets as well as personal driving.

Ford Motor Company engineers have completed a real-world driving experiment with HP, discovering which commuting commonalities could provide future breakthroughs for better managing fleets, personalized services and recommendations for individual drivers. Using HP’s Big Data Discovery Experience Services and the HP Haven big data platform, the engineering team gathered data and analyzed it to determine possibilities for lowering operating costs and optimizing underutilized vehicles for fleets as well as personal driving.

The Ford Fleet Insights experiment included HP fleet vehicles that were equipped with wireless sensors plugged into each vehicle. Ford data scientists and IT leaders used the HP Vertica analytics engine, part of the HP Haven platform, to explore patterns and multiple dimensions of fleet driver activity. Also, each driver could access their data using a custom smartphone app to recall trip details, if needed.

Observations during the experiment included:

  • Traveling employees often left their vehicles unused at the airport for days. These vehicles could be utilized more effectively by nearby drivers
  • Regardless of location, most drivers visited the same national coffee house and refueled with the same brand of gasoline
  • 70 percent of trips took place during weekdays and typical trip distances were 13 miles or less.

Trips fell into four groups:

  • City block driving (34 percent): Involved frequent direction changes, driving near the speed limit, idling at stoplights with short distances.
  • Freeway driving (21 percent): Involved few driving direction changes with large deviations from the speed-limit depending on traffic, and long trip durations and driving distances with less stop and go than City Block Commute.
  • Non rush-hour Driving (29 percent): Short trip duration and short-distance with less stops and idling
  • Rush-hour driving (16 percent): Short trip duration and short-distance with frequent stops and idling during peak drive hours
Source and photos: Ford and Hewlett-Packard use big data to better manage fleets, For and HP, 24 June 2015.

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