Bus station passenger flow volume prediction method based on GCN-LSTM combination model

A combined model and passenger flow technology, applied in forecasting, neural learning methods, biological neural network models, etc., can solve the problems of only considering the time series relationship and cannot obtain the law of traffic data changes well, and improve the forecasting effect. Effect

Pending Publication Date: 2021-02-26
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

Due to the high degree of uncertainty in the traffic characteristics, only considering the time series relationship cannot give a good understanding of the law of traffic data changes.

Method used

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  • Bus station passenger flow volume prediction method based on GCN-LSTM combination model
  • Bus station passenger flow volume prediction method based on GCN-LSTM combination model
  • Bus station passenger flow volume prediction method based on GCN-LSTM combination model

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Embodiment Construction

[0034] The steps of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0035] Step 1. Traffic flow data preprocessing

[0036] First, according to the bus route information and station information, the road topology graph is constructed for the bus station, where the graph is represented by G, V represents the set of all stations, and E represents the set of adjacent relations of the stations. It is stored in the form of an adjacency matrix, which is a two-dimensional matrix of s*s, and the value of the adjacency matrix is ​​0 or 1. Among them, s represents the number of bus stops, and the rows and columns represent the stations arranged in the order of the bus stop numbers. In the value of the adjacency matrix, 0 means that the two sites are not adjacent, and 1 means that the two sites are adjacent, and the adjacency matrix is ​​symmetrical Matrix, where the adjacency matrix is ​​denoted as A.

[0037] Secondly, feature ...

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Abstract

The invention discloses a bus station passenger flow volume prediction method based on a GCN-LSTM combination model, and belongs to the field of traffic prediction. According to the method, spatial correlation of passenger flow volume data is extracted by using a graph convolutional neural network, the data with spatial correlation information is input into a long-term and short-term memory cycleunit for time feature extraction, and finally, a prediction result is obtained through a full connection layer. According to the method, the spatiotemporal features of the traffic data are simultaneously concerned, so that the defect that the spatiotemporal features of the traffic data cannot be fully extracted by the existing method is overcome, the passenger flow volume data prediction precisionis improved, and the problem of short-time passenger flow volume prediction can be better solved.

Description

technical field [0001] The invention belongs to the technical field of traffic forecasting, in particular to a neural network-based forecasting method for short-term bus station passenger flow. Background technique [0002] With the continuous advancement of the urbanization process, the number of motor vehicles has increased rapidly, and the ensuing traffic congestion, frequent traffic accidents, and environmental pollution have become increasingly serious. The public transportation system is an important part of urban transportation, and public transportation has more prominent advantages compared with other transportation methods in terms of land saving, consumption reduction, emission reduction, and promotion of fairness and harmony. The good development of public transportation can alleviate urban traffic congestion. , reducing air pollution, improving people's travel patterns, and optimizing urban traffic structures are of great significance. Whether the public transp...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06N3/04G06N3/08
CPCG06Q10/04G06Q50/26G06N3/049G06N3/084G06N3/048G06N3/045
Inventor 桂智明姚思佳郭黎敏
Owner BEIJING UNIV OF TECH
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