Vehicular Cloud for Smart Driving Using Internet of Things.

Area of Science:

• Computer Science
• Traffic Engineering
• Network Reliability

Background:

• Traffic congestion is a significant problem in developed countries.
• Vehicular clouds offer potential for improving traffic management.
• Reliability of vehicular clouds depends on vehicle accessibility and movement patterns.

Purpose of the Study:

• To investigate stochastic attributes of traffic using a cloud-based approach.
• To analyze vehicle movement and queuing dynamics.
• To propose a model for traffic management and suggest alternate routes.

Main Methods:

• Stochastic investigation of traffic attributes within a street segment.
• Utilizing a traffic model for free-flow movement analysis (activity density, dwell time, vehicle quantity).
• Employing a queuing model for queue flow analysis (queue length, time in queue).

Main Results:

• Identified key parameters influencing traffic flow and queuing.
• Quantified vehicle movement characteristics in free-flow and queuing scenarios.
• Demonstrated the potential for a cloud-based system to manage traffic attributes.

Conclusions:

• Optimizing vehicle numbers and movement enhances vehicular cloud reliability.
• The proposed model provides insights into traffic dynamics.
• The research outcome can help reduce traffic congestion by suggesting alternate routes.