Optimizing Road Safety through Intelligent Congestion Management
- Resource Type
- Conference
- Authors
- Samara, Ghassan; Elhilo, Alaa; Asassfeh, Mahmoud Rajallah; Injadat, MohammadNoor; Qasem, Mais Haj; Alazaidah, Raed; Aljaidi, Mohammad; Al-Milli, Nabeel; Hnaif, Adnan A
- Source
- 2023 24th International Arab Conference on Information Technology (ACIT) Information Technology (ACIT), 2023 24th International Arab Conference on. :1-7 Dec, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Wireless communication
Machine learning algorithms
Transportation
Reinforcement learning
Road safety
Delays
Standards
Road Safety
Intelligent Congestion Management
Intelligent transportation systems
VANET
- Language
- ISSN
- 2831-4948
Intelligent transportation systems offer a promising avenue for enhancing road safety by facilitating communication between vehicles and infrastructure. This communication can significantly reduce road fatalities. Addressing congestion resulting from wireless communication between vehicles and infrastructure is a critical aspect of road safety. This paper focuses on effectively controlling this congestion while ensuring constant awareness in vehicles. The proposed technique involves determining an optimal transmission rate based on the current channel conditions, striking a balance between message awareness and congestion. This is achieved using a reinforcement learning and Markov decision process-based machine learning algorithm. Simulation results demonstrate the superiority of applying machine learning algorithms over traditional congestion control strategies. Additionally, future studies should consider incorporating other characteristics such as packet delay and packet loss for a comprehensive analysis.