Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems
- Title
- Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems
- Creator
- Neelakandan S.; Prakash M.; Bhargava S.; Mohan K.; Robert N.R.; Upadhye S.
- Description
- Recently, intelligent transportations system (ITS) has gained significant internet due to the higher needs for road safety and competence in interconnected road network. As a vital portion of the ITS, traffic flow prediction (TFP) is offer support in several dimensions like routing, traffic congestion, and so on. To accomplish effective TFP outcomes, several predictive approaches have been devised namely statistics, machine learning (ML), and deep learning (DL). This study designs an optimal stacked sparse autoencoder based traffic flow prediction (OSSAE-TFP) model for ITS. The goal of the OSSAE-TFP technique is to determine the level of traffic flow in ITS. In addition, the presented OSSAE-TFP technique involves the traffic and weather data for TFP. Moreover, the SSAE based prediction model is designed for forecasting the traffic flow and the optimal hyperparameters of the SSAE model can be adjusted by the use of water wave optimization (WWO) technique. To showcase the enhanced predictive outcome of the OSSAE-TFP technique, a wide range of simulations was carried out on benchmark datasets and the results portrayed the supremacy of the OSSAE-TFP technique over the recent state of art methods. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Source
- Studies in Systems, Decision and Control, Vol-412, pp. 111-127.
- Date
- 2022-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Intelligent transportation system; Machine learning; Parameter optimization; Traffic flow prediction; Urban traffic flow
- Coverage
- Neelakandan S., Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India; Prakash M., Data Science and Analytics Center, Karpagam College of Engineering, Coimbatore, India; Bhargava S., Department of Computer Science and Engineering, Poornima College of Engineering, Rajasthan, Jaipur, India; Mohan K., Lecturer Information Technology, University of Technology and Applied Sciences, Shinas, Oman; Robert N.R., Department of Computer Science, Christ University, Bangalore, 560029, India; Upadhye S., Department of Computer Application, Shri Ramdeobaba College of Engineering and Management, Maharashtra, Nagpur, India
- Rights
- Restricted Access
- Relation
- ISSN: 21984182
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Neelakandan S.; Prakash M.; Bhargava S.; Mohan K.; Robert N.R.; Upadhye S., “Optimal Stacked Sparse Autoencoder Based Traffic Flow Prediction in Intelligent Transportation Systems,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/18687.