Abstract: Described herein is a framework for optimal demand-based allocation. In accordance with one aspect of the framework, trip data is extracted from vehicle operation data received from one or more vehicle data devices servicing a region of interest. Vehicle demands for multiple tiles of the region of interest and mutual travel times between neighboring tiles are estimated based at least in part on the trip data. A set of candidate tiles for allocating vehicle stations may then be determined by minimizing total travel time based on the mutual travel times and vehicle demands.

Abstract: A search intention identifying method. The method includes: at a device having one or more processor and memory, obtaining a to-be-identified query character string, and obtaining a history web page set corresponding to the query character string, the history web page set comprising web pages clicked by using the query character string historically; obtaining a predetermined web page categorization model; obtaining a category of each web page in the history web page set according to the web page categorization model; collecting statistics on the number of web pages in each category in the history web page set, and performing calculation according to the number of the web pages in each category and a total number of web pages in the history web page set to obtain intention distribution of the query character string; and obtaining an intention identification result of the query character string according to the intention distribution.

Abstract: Described herein is a framework for optimal demand-based allocation. In accordance with one aspect of the framework, trip data is extracted from vehicle operation data received from one or more vehicle data devices servicing a region of interest. Vehicle demands for multiple tiles of the region of interest and mutual travel times between neighboring tiles are estimated based at least in part on the trip data. A set of candidate tiles for allocating vehicle stations may then be determined by minimizing total travel time based on the mutual travel times and vehicle demands.

Abstract: A framework for origin-destination (OD) analysis of vehicle trajectory data is described herein. In accordance with one aspect, a vehicle trajectory dataset is provided to an OD analyzer. The vehicle trajectory dataset includes vehicle trajectory data collected from a sensor network having a plurality of sensor stations for detecting vehicles. The sensor stations of the sensor network are distributed in a geographical area of interest, where the vehicle trajectory data include trajectories of vehicles captured by the sensor network. The vehicle trajectory dataset may be analyzed by the OD analyzer to determine an origin and a destination of trips for trajectories of the vehicles in the vehicle trajectory dataset.

Abstract: A framework for origin-destination (OD) analysis of vehicle trajectory data is described herein. In accordance with one aspect, a vehicle trajectory dataset is provided to an OD analyzer. The vehicle trajectory dataset includes vehicle trajectory data collected from a sensor network having a plurality of sensor stations for detecting vehicles. The sensor stations of the sensor network are distributed in a geographical area of interest, where the vehicle trajectory data include trajectories of vehicles captured by the sensor network. The vehicle trajectory dataset may be analyzed by the OD analyzer to determine an origin and a destination of trips for trajectories of the vehicles in the vehicle trajectory dataset.