Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Baidu Netcom Science and Technology Co., Ltd.

Xinjiang Lu

Hui Xiong

Dejing Dou

Yanyan Li

Jianguo Duan

Guanglei Du

Nengjun Zhu

A method of predicting traffic volume, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence technology, in particular to big data and deep learning technologies The method includes: generating, for a plurality of traffic regions, a function relation graph and a volume relation graph; generating a volume feature of a target traffic region among the plurality of traffic regions, according to a historical volume information of the target traffic region; generating a volume and function relation feature for the target traffic region, based on the function relation graph and the volume relation graph; and predicting a volume of the target traffic region according to the volume feature and the volume and function relation feature.

Method of predicting traffic volume, electronic device, and medium

This disclosure discloses a resident area prediction method, apparatus, device and storage medium, involving artificial intelligence technology, big data, deep learning and multi-task learning. The specific implementation plan is: acquiring a resident area data of a target user, and the resident area data including the resident area of the target user and the corresponding resident time; obtaining an association relationship between the resident areas of the target user by inputting the resident area data into an area relationship model, and the area relationship model is used to reflect a position relationship between the areas; determining a time-sequence relationship between the areas visited by the target user, according to the association relationship, the resident time and the visiting POI data; predicting a target resident area of the target user, according to the time-sequence relationship and the basic attribute information of the target user.

Resident area prediction method, apparatus, device, and storage medium

The present disclosure provides a region information processing method and apparatus, and relates to the field of artificial intelligence in computer technologies. The specific implementation is: acquiring a first distance between a first region and a second region, a first object set included in the first region, and a second object set included in the second region; determining spatial dependency information between the first region and the second region according to the first distance; determining object dependency information between the first region and the second region according to the first object set and the second object set; and determining a symbiosis between the first region and the second region according to the spatial dependency information and the object dependency information.

Region information processing method and apparatus

The present disclosure discloses a method, an apparatus, a device, and a storage medium for determining a point of interest area, and relates to the field of automatic driving. The implementation solution is that the method is applied to an electronic device, and includes: receiving a point of interest area determination request input by a first user, the point of interest area determination request including a target area coverage; and acquiring grid data of at least one block within the target area coverage in response to the point of interest area determination request; acquiring, for each block, positioning data of a second user within each preset time period and number of parent points of interest; clustering corresponding grid data according to the positioning data, the grid data and the number of the parent points of interest; determining at least one POI area in each block according to a clustering result.

Method, apparatus, device and storage medium for determining point of interest area

A method, device, and medium for recommending a region of interest are provided. The method includes: acquiring access data, the access data including correlation information between any two regions in a region group, in which a correlation between any two regions in the region group is acquired based on a region pair formed by the any two regions in the region group where sample users are located and the number of access times corresponding to the region pair, and in which the region group is acquired based on division of map data, and the map data includes boundary information of an entity in a real world; determining a region where a first user is currently located; and recommending region information of the region of interest for the first user based on the access data and the region where the first user is currently located.

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Beijing, China

Xinjiang Lu