Smart transportation: the disruptor-broker model

Here are a couple of low- and high-end examples of how real-time data is being applied to disrupt the public transportation sector:

  • As today’s Boston Globe reports, there is a “Data-driven pop-up bus service set to roll out”. When a critical mass of customers is ready for a ride, Bridj will dispatch other bus companies luxury buses to provide point-to-point service within the Boston metro area. The pricing, at $5, is higher than the $2 cost of a subway ride, but less than the $9 that one customer is quoted as otherwise paying for the Uber crowd-sourced taxi alternative.
  • At the other end of the spectrum, Evo-Lux finds and makes markets between excess helicopter capacity and shared-ride customers. The firm refers to its service as a “sky-limo”. Like Bridj, Evo-Lux is focused on providing a more comfortable and efficient ride for customers in congested metro markets, although Evo-Lux also provides transport outward to popular get-away locations.

In both cases, the disruptor is something of a broker, matching excess capacity to a small aggregation of customers with a commonality of temporal and locational need. A membership structure helps the company to identify transportation matches, and the result is a lower price than would be available for a fully private ride.