Here Global B.v.

Oren Elisha

Yam Kaspi

Ofri Rom

Artiom Lapshin

Ya'ara Arkin

An approach is provided for large scale vehicle routing. The approach involves, for example, receiving a plurality of plans, wherein a plan of the plurality of plans assigns a vehicle, a driver of the vehicle, or a combination thereof a set of rides to traverse. The approach also involves clustering the plurality of plans into one or more clusters based on a proximity measure. The proximity measure indicates a proximity of a first plan of the plurality of plans to a second plan of a plurality of plans. The approach further involves, for each cluster of the one or more clusters, separately computing a solution to a multiple vehicle routing problem for the set of rides in said each cluster.

Method and apparatus for providing large scale vehicle routing

Various aspects of a method, a system, and a computer program product are disclosed herein in accordance with at least one example embodiment for spatial clustering of a plurality of cells of a geographical location. The method may include generation of cell clusters based on multiple criteria. The method may further include reception of mobility data from one or more external devices associated with the plurality of cells. The mobility data may be further processed to extract observations for generation of feature vectors from the observations. The feature vectors may be normalized to generate one or more clusters of a set of cells of the location. Cluster data associated with the clustering of the plurality of cells may be further used in one or more location based applications.

Methods and systems for spatial clustering based on mobility data

The present invention provides a method, apparatus, and computer program product for generating synthetic demand data of vehicle rides corresponding to a first location. The method comprises obtaining historic demand data of vehicle rides corresponding to a second location. The historic demand data comprises at least one first point of interest (POI) associated with at least one of a pick-up event or a drop-off event. The method further comprises retrieving map data associated with the second location and further, determining at least one second POI associated with the first location. The method further comprises generating the synthetic demand data of vehicle rides corresponding to the first location, based on the at least one first POI, the map data, and the at least one second POI.

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Raanana, Israel

Oren Elisha