# Integrated optimization of spatiotemporal resources at the intersection for delay minimization using genetic algorithm

## TL;DR

This paper introduces a genetic algorithm to optimize traffic signal control and lane allocation at intersections to minimize delays.

## Contribution

A novel genetic algorithm is proposed for integrated spatiotemporal resource optimization at intersections.

## Key findings

- The model adapts to diverse traffic flow patterns effectively.
- Solutions are obtained in 40–55 seconds, suitable for real-time control.
- The approach outperforms previous studies in efficiency and quality.

## Abstract

Integrated optimization of spatiotemporal resources at the intersection (IOSTRI) is crucial for traffic signal control, where both the lane allocation and signal control plans are optimized in a unified framework. This paper addresses the IOSTRI problem with delay minimization, formulating it as a binary mixed-integer nonlinear program (BMINLP) model that fully incorporates all possible uses of shared lanes and lane utilization adjustments. A genetic algorithm tailored to the model’s characteristics is designed, where four modules named lane converter, signal plan converter, flow calculation function and delay calculation function are used to calculate the fitness of each solution. Numerical results show the proposed model and algorithm’s ability to adapt to diverse traffic flow distribution patterns. High-quality solutions are obtained within 40–55 seconds, representing a significant improvement over previous studies and satisfying the requirements for real-time adaptive control of a single intersection.