Speed Optimization In Unplanned Traffic Using Bio-Inspired Computing And Population Knowledge Base

TL;DR

This paper proposes a bio-inspired computing approach combined with a population knowledge base to optimize speed and lane usage in unplanned traffic zones, aiming to improve traffic flow and safety.

Contribution

It introduces a novel speed optimization technique using bio-inspired algorithms and vehicle speed analysis for better lane management in unplanned traffic areas.

Findings

Encouraging results on real-life data demonstrate improved traffic flow.

The method effectively optimizes lane usage and vehicle speeds.

Potential for reducing traffic congestion and accidents.

Abstract

Bio-Inspired Algorithms on Road Traffic Congestion and safety is a very promising research problem. Searching for an efficient optimization method to increase the degree of speed optimization and thereby increasing the traffic Flow in an unplanned zone is a widely concerning issue. However, there has been a limited research effort on the optimization of the lane usage with speed optimization. The main objective of this article is to find avenues or techniques in a novel way to solve the problem optimally using the knowledge from analysis of speeds of vehicles, which, in turn will act as a guide for design of lanes optimally to provide better optimized traffic. The accident factors adjust the base model estimates for individual geometric design element dimensions and for traffic control features. The application of these algorithms in partially modified form in accordance of this novel Speed Optimization Technique in an Unplanned Traffic analysis technique is applied to the proposed design and speed optimization plan. The experimental results based on real life data are quite encouraging.