Machine learning-based human-robot interaction in ITS. Issue 1 (January 2022)

• Record Type: Journal Article
• Authors: Wang, Jingyao; Pradhan, Manas Ranjan; Gunasekaran, Nallappan
• Is Part Of: Information processing & management. Volume 59:Issue 1(2022)
• Publication Date: 2022-01
• Subjects: Human-computer interaction -- Machine learning -- Intelligent transportation system -- Intelligent traffic monitoring system; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2021.102750
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: The intelligent transportation system using machine learning for traffic flow. Information obtained from intelligent sensors is migrated to LoRa cloud platform. To enhance traffic monitoring and better traffic flow prediction systems. Abstract: In the last few years, intelligent transport systems (ITS) have drawn growing attention, and these applications would have a clear and more comfortable experience for transportation. ITS provides applications with a chance to address the future condition on the route beforehand. The major issues in ITS to accomplish a precise and effective traffic flow prediction system are essential. Therefore, in this paper, a machine learning-assisted intelligent traffic monitoring system (ML-ITMS) has proposed improving transportation protection and reliability to tackle several challenges. The suggested ML-ITMS uses mathematical models to improve the accuracy estimation of traffic flow and nonparametric processes. The Machine Learning-based (ML) method is one of the best-known methods of nonparametric. It requires less prior information about connections between various traffic patterns, minor estimation limitations, and better suitability of nonlinear traffic data features. Human-Robot Interaction (HRI) helps resolve crucial issues concurrently on both the customers and service supplier levels at both ends of the transport system. Thus the experimental results show the proposed ML-ITMS to enhance traffic monitoring to 98.6% and better traffic flow prediction systems than other existing methods.