International Business Machines Corporation

Francesco Calabrese

Eric P Bouillet

Fabio Pinelli

Michele Berlingerio

Olivier Verscheure

Adi Ionel Botea

Mathieu Sinn

Rahul Nair

Lea A Deleris

Donna Leigh Gresh

Ji W Yoon

Marco Luca Sbodio

Martin Stephenson

Alessandra Pascale

Bissan Ghaddar

Giusy Di Lorenzo

Olivier Versheure

A system, computer program product, and method is described to propose a method to automatically suggest a visualization providing the best insight for a given dataset, by maximizing an utility function computed on properties of the input data, and previously used visualizations by the same user and for the same data. In one example method for recommending a data visualization in a computer system includes receiving at least one input dataset and at least one set of previous visualizations. A utility function is accessed. A set of potential visualizations is computed using the input dataset and the set of previous visualizations. A recommendation is selected for a proposed visualization from the set of potential visualizations by maximizing a value of the utility function, whereby the proposed visualization is a new visualization not in the set of previous visualizations.

Data visualizations selection

Embodiments for targeted learning and recruitment by a processor. Attributes obtained from a user profile are used to identify a user as a potential candidate for performing an activity related to either the recruiting or the learning and providing a customized learning experience with targeted course suggestions for the user.

Targeted learning and recruitment

A method includes: a computer receiving historical data from at least one service vehicle; the computer receiving network definitions for the least one service vehicle; the computer receiving dynamic data from the at least one service vehicle; and the computer developing predictions and corrective actions to prevent service bunching, based upon the historical data, the network definitions, and the dynamic data.

Real-time prediction and correction of scheduled service bunching

Embodiments of the disclosure include a method for journey planning including receiving a journey planning request, the journey planning request having an origin and a destination in a transportation network. The method also includes calculating an optimized journey plan by identifying a plurality of routes through the transportation network from the origin to the destination and determining an uncertainty associated with each of the plurality of routes. Calculating an optimized journey plan also includes evaluating a robustness of each of the plurality of routes to the uncertainty associated with each of the plurality of routes and selecting the optimized journey plan based on the journey planning request and the robustness of each of the plurality of routes.

Hedging risk in journey planning

Embodiments of the disclosure include a computer system for journey planning, the computer system includes a journey planning server configured to perform a method. The method includes receiving a journey planning request, the journey planning request having an origin and a destination in a transportation network. The method also includes calculating an optimized journey plan by identifying a plurality of routes through the transportation network from the origin to the destination and determining an uncertainty associated with each of the plurality of routes. Calculating an optimized journey plan also includes evaluating a robustness of each of the plurality of routes to the uncertainty associated with each of the plurality of routes and selecting the optimized journey plan based on the journey planning request and the robustness of each of the plurality of routes.

Embodiments of the disclosure include a method for journey planning including receiving a journey planning request. The request includes an origin and a destination in a transportation network. The method also includes calculating an optimized journey plan based on the journey planning request and a route map corresponding to the transportation network and identifying a potential event in the transportation. The method further includes monitoring the transportation network for the potential event and based on detecting an execution of the potential event, calculating an improved journey plan.

Journey computation with re-planning based on events in a transportation network

Embodiments of the disclosure include a method for providing a consolidated estimated time of arrival in a scheduled transportation system. The method includes receiving at least one of a scheduled time of arrival, a real-time estimated time of arrival and a predictive estimated time of arrival and assigning a weight to each of the scheduled time of arrival, the real-time estimated time of arrival and the predictive estimated time of arrival. The method also includes calculating a confidence level and the consolidated estimated time of arrival based on the weights and the scheduled time of arrival, the real-time estimated time of arrival and the predictive estimated time of arrival.

Estimation of arrival times at transit stops

Embodiments of the disclosure include a method for de-noising data in a scheduled transportation system, the method includes receiving a plurality of digital traces that correspond to a piece of equipment in the scheduled transportation system. The method also includes identifying a plurality of journeys from the plurality of digital traces, wherein each of the plurality of journeys corresponds to the piece of equipment traversing one of a plurality of routes and generating a route map and schedule for the scheduled transportation system from the plurality of journeys and the plurality of digital traces.

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Dublin, Ireland

Francesco Calabrese