Short-Term Traffic Flow Prediction Model Based on BP Neural Network Algorithm

Zhang, Dandan; Lü, Qian

doi:10.1007/978-3-031-31860-3_32

Cited by 2 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To predict the earthquake probability more accurately and ensure the stability of the model, we search a large number of different types of data. Through the database and the official website of China, we collected 33 types of data of 50 earthquakes in Chongqing over the past three years, amount to 1650 pieces of data [4][5].…”

Section: Data Collectionmentioning

confidence: 99%

Study on insurance in earthquake-prone areas based on BP neural network and logistic regression model

Wang,

Tao,

Min

2024

HSET

View full text Add to dashboard Cite

The frequent occurrence of extreme events poses major challenges to the insurance industry and affects its sustainable development. To provide insurance recommendations for extreme disaster areas, this paper combines BP neural network model, principal component analysis method and Logistic regression model to establish our insurance model. First, we processed the data. Then, the BP neural network mode used to correlate 33 kinds of earthquake correlation indexes with earthquake probability and make prediction. To make the data more representative, we conducted dimension reduction prediction through principal component analysis, screened out 8 important factors, and identified 3 principal components. We then performed Logistic regression analysis based on these three principal components and used the Logistic model to decide whether to conduct insurance. After the experiment, the accuracy of the model can reach 78.9%, which can provide a reliable prediction of the disaster in the region, to help the insurance company to make a decision whether to insure in the region.

show abstract

Section: Data Collectionmentioning

confidence: 99%

Study on insurance in earthquake-prone areas based on BP neural network and logistic regression model

Wang,

Tao,

Min

2024

HSET

View full text Add to dashboard Cite

show abstract

“…With the deepening research on deep learning, researchers have begun to explore the use of deep learning [ 11 ] methods for traffic flow prediction. In the field of traffic flow prediction, commonly used deep learning models include Convolutional Neural Networks (CNN) [ 12 ], Recurrent Neural Networks (RNN) [ 13 , 14 ], Graph Neural Networks (GNN) [ 15 , 16 ] and so on. For example, Yao et al proposed a traffic prediction method that combines CNN and Long Short-Term Memory (LSTM) to jointly model spatial and temporal dependencies [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal information enhanced multi-feature short-term traffic flow prediction

Huang,

He,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Accurately predicting traffic flow is crucial for optimizing traffic conditions, reducing congestion, and improving travel efficiency. To explore spatiotemporal characteristics of traffic flow in depth, this study proposes the MFSTBiSGAT model. The MFSTBiSGAT model leverages graph attention networks to extract dynamic spatial features from complex road networks, and utilizes bidirectional long short-term memory networks to capture temporal correlations from both past and future time perspectives. Additionally, spatial and temporal information enhancement layers are employed to comprehensively capture traffic flow patterns. The model aims to directly extract original temporal features from traffic flow data, and utilizes the Spearman function to extract hidden spatial matrices of road networks for deeper insights into spatiotemporal characteristics. Historical traffic speed and lane occupancy data are integrated into the prediction model to reduce forecasting errors and enhance robustness. Experimental results on two real-world traffic datasets demonstrate that MFSTBiSGAT successfully extracts and captures spatiotemporal correlations in traffic networks, significantly improving prediction accuracy.

show abstract

Short-Term Traffic Flow Prediction Model Based on BP Neural Network Algorithm

Cited by 2 publications

References 11 publications

Study on insurance in earthquake-prone areas based on BP neural network and logistic regression model

Study on insurance in earthquake-prone areas based on BP neural network and logistic regression model

Spatiotemporal information enhanced multi-feature short-term traffic flow prediction

Contact Info

Product

Resources

About