predict.io has been set up as a company to address individual, as well as societal problems related to smart mobility by building a smart, integrated, technology-based, easy to use and cost-free system that can easily be integrated in all kinds of mobility solutions. The outcome of the project will be an ensemble of algorithms that had been tested on large scale in the operational environment of different European cities.
The SOUTHPARK project is set up to fulfill two overall objectives:
First, it will enable innovative mobility solutions in Europe’s metropolitan areas. Predict.io will integrate the technology with its automated start and stop detection in different mobility apps. This will be achieved when the SDK generates 1 million data points a day for real-time applications as well as business analytics. We will follow the already developed commercialisation plan with the predict.io sales approach to explore and pursue business opportunities in the mobility sector and recruit a large-scale sample of data points. The goal is to enable more convenient, safer, environmentally friendlier, and efficient mobility solutions.
The objective has been met. The SDK and analytics suite has been successfully launched across geographies and is currently producing ~75 Million data points per day. The target to produce 1 Million data points per day has been far exceeded. Test users like the City of Barcelona are benefiting from the technology and are evaluating long term use.
Second, the SOUTHPARK project will enhance the existing predict.io algorithms with a component that enables the technology to self-adopt to new local settings. In order to achieve this objective predict.io will repeatedly collect and analyse locally deviating data sources in order to identify, attune and optimise the locally diverging features for its STOP detection system. Thereby it will train and enhance the predict.io algorithms by testing and cross-testing large-scale datasets in the end generating a self-adopting component that can adjust itself to any type of local mobility situation. Fulfilling this objective will reduce the adaptation costs and time to localise to new local settings by estimated 75%.
We have successfully built a system that can automatically learn from user input which significantly lowered the cost for expansion to new territories. Furthermore this laid the foundations for our rapid testing of new verticals. Today we are able to produce ground truth data from live users in order to feed the inference algorithms with patterns to search for. Regardless which type of inference we aim at (parking spot, transport mode, venue, visit intent, etc.) the performance is measurable and changes to the algorithms can be done on the fly. Before launch of the system building a new inference took us between 3 and 6 months. Now we are able to train the system within 1 week with user input. Hence, objective 2 has also been met very successfully.
