Using Big Data to Predict Demand

Posted on Posted in Industry, Technology, Uncategorized

Surge Pricing and Big Data

While the above is an extreme case of Surge Pricing, you may have felt a similar sting to
your bank account if you have used Uber or Lyft during a large concert, sporting event, or
festival in your area. But the pricing is not without justification. Uber’s data software monitors
traffic conditions and the trips of other Uber drivers in real-time, so the pricing algorithm
appropriately adjusts the travel price for the predicted total time and the overall demand for that
Uber driver. Due to the constant connectivity of smart devices, Uber can see all of their drivers
and passengers at a given moment, and they can then use this data to give both you and their
drivers a fair price, even if it is up to 8 times the normal fare in some cases.

Translating the Uber Model

To explain Uber’s pricing model, Data Scientist, Bernard Marr, puts it succinctly: “Fares
are calculated automatically, using GPS, street data and the company’s own algorithms which
make adjustments based on the time that the journey is likely to take. This is a crucial difference
from regular taxi services because customers are charged for the time the journey takes, not the
distance covered.” Apply this Big Data approach to the construction industry, and you start to
track vehicles, vehicle operators, vehicle usage, usage areas, the average journey before a
vehicle reaches a site, and a whole array of other valuable data points that you can then apply
to create a more efficient and fairly priced construction vehicle ecosystem.

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