Method of predicting traffic volume, electronic device, and medium

Patent metadata

Field	Value
Publication number	US-12148295-B2
Application number	US-202217824966-A
Country	US
Kind code	B2
Filing date	May 26, 2022
Priority date	May 27, 2021
Publication date	Nov 19, 2024
Grant date	Nov 19, 2024

Abstract

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.

First claim

What is claimed is:

1. A computer-implemented method of predicting traffic volume, the method comprising:
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;
generating a traffic relation graph for the plurality of traffic regions;
generating a neighbor relation graph for the target traffic region among the plurality of traffic regions, based on the function relation graph, the volume relation graph and the traffic relation graph, wherein the generating the neighbor relation graph comprises:
acquiring a predetermined number of neighbor traffic regions adjacent to the target traffic region from the function relation graph, a predetermined number of neighbor traffic regions adjacent to the target traffic region from the volume relation graph, and a predetermined number of neighbor traffic regions adjacent to the target traffic region from the traffic relation graph, and
mapping feature vectors for the target traffic region and the predetermined number of neighbor traffic regions from the function relation graph, the predetermined number of neighbor traffic regions from the volume relation graph, and the predetermined number of neighbor traffic regions from the traffic relation graph to the neighbor relation graph based on a linear mapping method;

generating a geographic feature of the target traffic region by using a first attention network model based on the neighbor relation graph; and
predicting a volume of the target traffic region according to the volume feature, the volume and function relation feature and the geographic feature.

2. The method according to claim 1 , wherein the predicting a volume of the target traffic region comprises:
pooling the volume feature, the volume and function relation feature and the geographic feature, so as to obtain an aggregated feature of the target traffic region; and
predicting the volume of the target traffic region based on the aggregated feature of the target traffic region by using a multi-layer perceptron network model.

3. The method according to claim 2 , wherein the generating a volume and function relation feature for the target traffic region comprises:
determining, from the plurality of traffic regions, a first traffic region associated with the target traffic region in function, based on the function relation graph;
determining, from the plurality of traffic regions, a second traffic region associated with the target traffic region in volume, based on the volume relation graph;
generating a volume and function relation graph for the target traffic region, based on a historical function information and a historical volume information of the target traffic region, a historical function information and a historical volume information of the first traffic region and a historical function information and a historical volume information of the second traffic region; and
generating the volume and function relation feature for the target traffic region by using a second attention network model based on the volume and function relation graph.

4. The method according to claim 1 , wherein the generating a volume and function relation feature for the target traffic region comprises:
determining, from the plurality of traffic regions, a first traffic region associated with the target traffic region in function, based on the function relation graph;
determining, from the plurality of traffic regions, a second traffic region associated with the target traffic region in volume, based on the volume relation graph;
generating a volume and function relation graph for the target traffic region, based on a historical function information and a historical volume information of the target traffic region, a historical function information and a historical volume information of the first traffic region and a historical function information and a historical volume information of the second traffic region; and
generating the volume and function relation feature for the target traffic region by using a second attention network model based on the volume and function relation graph.

5. The method according to claim 4 , wherein the generating a volume and function relation graph for the target traffic region comprises:
for at least one traffic region of the target traffic region, the first traffic region or the second traffic region,
generating a plurality of function information segments based on the historical function information of the at least one traffic region, and a plurality of volume information segments based on the historical volume information of the at least one traffic region, and
selecting at least one function information segment from the plurality of function information segments and at least one volume information segment from the plurality of volume information segments, according to a temporal correlation between each of the function information segments and each of the volume information segments; and

generating the volume and function relation graph for the target traffic region, based on the target traffic region, the at least one selected function information segment and the at least one selected volume information segment.

6. The method according to claim 5 , wherein:
generating the plurality of function information segments comprises generating a function information sequence based on the historical function information, and performing sliding processing on the function information sequence by using a sliding window of a preset size, so as to obtain the plurality of function information segments; and
generating the plurality of volume information segments comprises generating a volume information sequence based on the historical volume information, and performing sliding processing on the volume information sequence by using the sliding window of the preset size, so as to obtain the plurality of volume information segments.

7. The method according to claim 1 , wherein the generating a volume feature of a target traffic region comprises generating the volume feature of the target traffic region by using a serialization network model, according to the historical volume information of the target traffic region among the plurality of traffic regions.

8. The method according to claim 1 , further comprising determining the plurality of traffic regions based on a road network information, wherein each of the plurality of traffic regions corresponds to a block contained in the road network information.

9. The method according to claim 1 , further comprising, for the target traffic region, determining an event information indicating that a volume change leads to a function change and an event information indicating that a function change leads to a volume change, according to the volume and function relation feature.

10. An electronic device, comprising:
at least one processor; and
a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions, when executed by the at least one processor, cause the at least one processor to at least:
generate, for a plurality of traffic regions, a function relation graph and a volume relation graph;
generate a volume feature of a target traffic region among the plurality of traffic regions, according to a historical volume information of the target t

Assignees

• Beijing Baidu Netcom Sci & Tech Co Ltd

Inventors

• Lu Xinjiang
• Dou Dejing

Classifications

• G08G1/0112

  from the vehicle, e.g. floating car data [FCD] · CPC title

• G08G1/012

  from other sources than vehicle or roadside beacons, e.g. mobile networks · CPC title

• G08G1/0133

  for classifying traffic situation · CPC title

• G08G1/0145

  for active traffic flow control · CPC title

• G08G1/0125 Primary

  Traffic data processing · CPC title