Abstract:
Traffic congestion has become a major economic and environmental problem for urban areas worldwide. Traffic prediction is an effective way to reduce congestion. Traffic prediction research has grown since the late 1970s. Early studies used ARIMA and its variants. Machine learning models’ power and flexibility have attracted researchers. Due to its complex and deep structure, deep neural networks have become popular due to their prediction power. Deep neural network models are popular for traffic prediction, but literature surveys are rare. We present a current survey of deep neural network traffic prediction. We will explain popular deep neural network architectures used in traffic flow prediction literatures, categorize and describe the literatures, discuss their commonalities and differences, and discuss the field’s challenges and future directions.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
