Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques
National College of Business Administration and Economics · Skyline University College · +3 more institutions
Abstract
Smart cities have been developed over the past decade, and reducing traffic congestion has been the top concern in smart city development. Short delays in communication between vehicles and Roadside Units (RSUs), smooth traffic flow, and road safety are the key challenges of Intelligent Transportation Systems (ITSs). The rapid upsurge in the number of road vehicles has increased traffic congestion and the number of road accidents. To fix this issue, Vehicular Networks (VNs) have developed many new ideas, including vehicular communications, navigation, and traffic control. Machine Learning (ML) is an efficient approach to finding hidden insights into ITS without being programmed explicitly by learning from…
Citation impact
- FWCI
- 35.19
- Percentile
- 100%
- References
- 60
Authors
6- MSMuhammad Saleem
National College of Business Administration and Economics
- SASagheer Abbas
National College of Business Administration and Economics
- TMTaher M. Ghazal
Skyline University College, National University of Malaysia
- MAMuhammad Adnan KhanCorresponding
Gachon University, Riphah International University
- NSNizar Sahawneh
Skyline University College
Topics & keywords
- Computer science
- Intelligent transportation system
- Traffic congestion
- Floating car data
- Traffic flow (computer networking)
- Network congestion
- Traffic congestion reconstruction with Kerner's three-phase theory
- Advanced Traffic Management System
- Sustainable cities and communities