Traffic Flow Prediction Using Big Data and Geographic Information Systems: A Survey of Data Sources, Frameworks, Challenges, and Opportunities

Ahmed, Sayed A.; Abdelhamid, Yasser M; Hefny, Hesham A.

doi:10.12785/ijcds/140147

IJCDS

2023

DOI: 10.12785/ijcds/140147

|View full text |Cite

Traffic Flow Prediction Using Big Data and Geographic Information Systems: A Survey of Data Sources, Frameworks, Challenges, and Opportunities

Sayed A. Ahmed¹,

Yasser M Abdelhamid²,

Hesham A. Hefny³

Abstract: Big Data has been utilized extensively in numerous fields, including transportation. Using several sources of data, traffic conditions can be accurately anticipated and forecasted to enhance the overall operation's efficacy. This study introduces and outlines the different data types that can be utilized in traffic prediction. In addition, the most popular Big Data processing frameworks will be characterized. It also reviews the applications of Big Data in traffic flow prediction. Furthermore, a conceptual fra… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Quantitative analysis and model prediction of urban traffic flow optimization by geographic information technology

Wang,

Pan

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

With the development of intelligent transportation, urban traffic flow prediction faces more complex traffic situations and higher accuracy requirements. In this paper, we collected multi-day traffic data from local cities and used a k-means clustering algorithm to analyze the data by clustering. A BP neural network prediction algorithm combined with k-means clustering analysis is designed to construct an “hour-day-week” urban traffic flow prediction model. The model is trained and applied to real-time urban traffic flow prediction to further analyze the performance effect of this model. The training time of this model is the shortest, which is about 67 minutes, and the MAE value is 3.02. The training speed is ahead of the longest training time in model 2, which is about 163 minutes, and the error is reduced by 8.04 compared with that of the highest prediction error in model 1. With different levels of noise added, the R², ACC, and RMSE values of the model in this paper are maintained at about 0.45, 0.63, and 0.8, respectively, indicating that different levels of noise have a significant effect on the performance of this model. This indicates that different degrees of noise have little effect on the performance of the model in this paper. In addition, the prediction effect is better after clustering analysis. The MAPE value of this paper’s model is the lowest, only 2.64%, which is 3.65%-7.52% lower than the other three control models. The experimental results above fully demonstrate the superior performance of this paper’s model, which suggests that this paper’s model is more effective in predicting urban traffic flow.

show abstract

Quantitative analysis and model prediction of urban traffic flow optimization by geographic information technology

Wang,

Pan

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

Integration Approaches for Heterogeneous Big Data: A Survey

Alma’aitah,

Quraan,

AL-Aswadi

et al. 2024

Cybernetics and Information Technologies

View full text Add to dashboard Cite

Modern organizations are currently wrestling with strenuous challenges relating to the management of heterogeneous big data, which combines data from various sources and varies in type, format, and content. The heterogeneity of the data makes it difficult to analyze and integrate. This paper presents big data warehousing and federation as viable approaches for handling big data complexity. It discusses their respective advantages and disadvantages as strategies for integrating, managing, and analyzing heterogeneous big data. Data integration is crucial for organizations to manipulate organizational data. Organizations have to weigh the benefits and drawbacks of both data integration approaches to identify the one that responds to their organizational needs and objectives. This paper aw well presents an adequate analysis of these two data integration approaches and identifies challenges associated with the selection of either approach. Thorough understanding and awareness of the merits and demits of these two approaches are crucial for practitioners, researchers, and decision-makers to select the approach that enables them to handle complex data, boost their decision-making process, and best align with their needs and expectations.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Traffic Flow Prediction Using Big Data and Geographic Information Systems: A Survey of Data Sources, Frameworks, Challenges, and Opportunities

Cited by 2 publications

References 82 publications

Quantitative analysis and model prediction of urban traffic flow optimization by geographic information technology

Quantitative analysis and model prediction of urban traffic flow optimization by geographic information technology

Integration Approaches for Heterogeneous Big Data: A Survey

Contact Info

Product

Resources

About