Abstract:
The present technique relates to an information processing apparatus, an information processing method, and a program which enable a pick-up demand of a taxi to be learned and predicted in a more efficient manner. An information processing apparatus includes: a control portion configured to divide a business region into a plurality areas and, using hired vehicle sequence data that is data for each area indicating that a business vehicle has picked up a customer, execute first clustering in which the plurality of areas are clustered using a first parameter and execute second clustering in which the plurality of areas are clustered using a second parameter. For example, the present technique can be applied to an information processing apparatus or the like that predicts a pick-up demand of a taxi.
Abstract:
The present technique relates to an information processing apparatus, an information processing method, and a program which enable a prediction result that contributes toward improving a pick-up ratio to be presented. The information processing apparatus includes: a display control portion configured to divide a business area of a business vehicle into a plurality of areas and cause a display portion to display, as a prediction result, a movement direction and a movement distance of passengers in an attention area being the area of attention among the plurality of areas for which a pick-up demand has been predicted per area. For example, the present technique can be applied to an information processing apparatus or the like for displaying a prediction result of a pick-up demand of a taxi.
Abstract:
There is provided an information processing apparatus including a setting unit that sets a search character string, a searching unit that searches information including the set search character string, and a determining unit that extracts a co-occurrence character string candidate group other than partial character strings appearing as only a part of other partial character strings, among all partial character strings appearing in a plurality of pieces of the information obtained as a search result, and determines a co-occurrence character string from the co-occurrence character string candidate group, on the basis of the extracted co-occurrence character string candidate group and kinds of characters used in characters before and after the co-occurrence character string candidate group.