Traffic Guidance Based on LSTM Neural Network and Dual Tracking Dijkstra Algorithm

Zhang, Zhenghua; Fang, Chongxin; Zhang, Jiafeng; Jin, Qian; Quan, Stella

doi:10.1109/iciscae51034.2020.9236823

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Other2

Article1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Delay-tolerant Load Balancing Routing Model for RSU-Assisted Distributed Vehicular Adhoc Networks

Rajendran,

Kumar,

Kavitha

et al. 2023

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)

View full text Add to dashboard Cite

Delay-tolerant Load Balancing Routing Model for RSU-Assisted Distributed Vehicular Adhoc Networks

Rajendran,

Kumar,

Kavitha

et al. 2023

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)

View full text Add to dashboard Cite

Efficient UAV Path Planning and Energy Optimization with Cooperative System Model for Vehicular Adhoc Networks

Veeramuthu,

Mahalingam,

et al. 2023

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)

View full text Add to dashboard Cite

Development of ideological and political education course field dimensions based on multi-data chain network

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

This paper firstly classifies the ideological and political education course field dimensions, studies the specific composition of the learning field in three dimensions: information, technology and emotion, and discusses the necessity of learning field optimization in secondary school Civics class. Secondly, on the multivariate data chain network, the MIFS model is proposed to extract the ideological and political teaching feature values, followed by the LSTM neural network to train the information related to students’ Civics classroom contents and the average function of adaptive excitation function is used to replace the excitation function in the LSTM neural network to achieve the effect of improving the output accuracy. Then, the characteristics of learners and learning resources are parametrically represented, followed by an experimental analysis of ideological and political education based on multivariate data chain networks using evaluation indexes based on the data set. The results show that there is no difference between the MAE values of CNN and LSTM except for sample one, but the MSE values of LSTM are both lower than CNN, and LSTM is more stable than CNN, indicating that it is reasonable to choose LSTM when ideological and political education based on multivariate data chain network. This study provides a reference and reference for future field optimization in ideology and political science classes.

show abstract

Traffic Guidance Based on LSTM Neural Network and Dual Tracking Dijkstra Algorithm

Cited by 3 publications

References 20 publications

Delay-tolerant Load Balancing Routing Model for RSU-Assisted Distributed Vehicular Adhoc Networks

Delay-tolerant Load Balancing Routing Model for RSU-Assisted Distributed Vehicular Adhoc Networks

Efficient UAV Path Planning and Energy Optimization with Cooperative System Model for Vehicular Adhoc Networks

Development of ideological and political education course field dimensions based on multi-data chain network

Contact Info

Product

Resources

About