Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure.

Area of Science:

• Computer Science
• Transportation Engineering
• Data Structures

Background:

• Urban traffic congestion significantly impacts daily life, wasting time and resources.
• Intelligent Transportation Systems offer potential solutions for mitigating traffic challenges.

Purpose of the Study:

• To propose a novel and scalable solution for modeling, storing, and controlling traffic data.
• To enhance urban traffic prediction and congestion avoidance using advanced data structures.

Main Methods:

• Utilized range query data structures, specifically K-ary Interval Tree and K-ary Entry Point Tree.
• Developed a traffic data modeling and control system.
• Conducted experiments and validation on a Brooklyn, New York traffic congestion simulation scenario.

Main Results:

• Demonstrated the validity, reliability, performance, and scalability of the proposed solution.
• Simulated up to 10,000 vehicles with microsecond access times to traffic information.
• Achieved congestion-free route generation in complex scenarios in under 1.5 seconds.

Conclusions:

• The proposed approach is the first scalable method for urban traffic prediction and congestion avoidance using range query data structure traffic modeling.
• The solution effectively reduces time spent in traffic, run-time, and memory usage.