Ford and IBM collaborate on transport predictions


Sick of never being able to find a parking space? Ford and IBM may have the answer – collaborating on a pilot platform that can spot patterns, correlations and trends to help consumers make more efficient transportation decisions.

The Smart Mobility Experimentation Platform allows Ford research scientists to use data of 10 or 15 seconds at a time to spot tendencies and behaviours.

The platform uses IBM streaming analytics delivered via IBM’s Cloud to allow for continuous updating.

One of the applications Ford is exploring is using real-time analytics to direct people on the most efficient ways to use multi-modal travel options. By taking data feeds from various systems to provide alerts to problems on a subway or unusually heavy traffic, the platform could then direct commuters travelling in the network to ride a bicycle to reach the destination on time.

Ford is also conducting a GoPark Painless Parking experiment, in which it will deploy parking space prediction technology. The company will collect data from cars coming and going from parking spaces in a defined area to predict available spots (with permission from participants). Predictions can then be made on available city data plus observed parking patterns such as time of day and location. A mobile app can provide information about parking spaces that are available to use.

These applications are similar to those used in the stockmarket, where masses of data are aggregated quickly to enable rapid transactions, and to those used by energy providers, who use data to monitor grids, identify opportunities for predictive maintenance, and deploy crews automatically based on certain thresholds.