Time Series Prediction for Traffic Flow Forecasting Using CNN

Traffic problems are very common nowadays throughout the world. In India also heavy traffic also occurred in many populated cities. For this reason, the public loses their time and life, and this pollution impacts human health. So, traffic research is necessary for this situation. We concentrate on the traffic network to find a better solution or model to predict future traffic. Our proposed model uses a time series for traffic forecasting. It deals with time series analysis for traffic congestion, traffic control, and traffic prediction. This paper focuses on appropriate datasets with different vehicles in various time series. A novel time series forecasting model was used for this research, and it also predicted a 99% accuracy rate. A comparative study is also presented in this research.

Authors

Publication Details

Journal
Asian Journal of Research in Computer Science
Published
2025-04-30
DOI
https://doi.org/10.9734/ajrcos/2025/v18i5665
Citations
1
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
1.01
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article

Time Series Prediction for Traffic Flow Forecasting Using CNN

Anil Rai, Kollipara Rithwik, Bhagwati Sharan, N. Prakash
1 citations
Asian Journal of Research in Computer Science
Traffic Prediction and Management Techniques
1.01
article

Time Series Prediction for Traffic Flow Forecasting Using CNN

Anil Rai, Kollipara Rithwik, Bhagwati Sharan, N. Prakash
article en
1 citations

Abstract

Traffic problems are very common nowadays throughout the world. In India also heavy traffic also occurred in many populated cities. For this reason, the public loses their time and life, and this pollution impacts human health. So, traffic research is necessary for this situation. We concentrate on the traffic network to find a better solution or model to predict future traffic. Our proposed model uses a time series for traffic forecasting. It deals with time series analysis for traffic congestion, traffic control, and traffic prediction. This paper focuses on appropriate datasets with different vehicles in various time series. A novel time series forecasting model was used for this research, and it also predicted a 99% accuracy rate. A comparative study is also presented in this research.

Asian Journal of Research in Computer ScienceVol. 18(5)
Openalex Percentile: Top 26%
Traffic Prediction and Management Techniques
1.01
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