# Boosted Genetic Algorithm using Machine Learning for traffic control optimization

arXiv: https://arxiv.org/abs/2103.08317 · HTML: https://mlsanity.com/paper/2103.08317/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 47 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Deterministic Policy Gradient for Urban Traffic Light Control](https://mlsanity.com/paper/1703.09035/) (2017)
- [Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting](https://mlsanity.com/paper/1905.12254/) (2019)

## Built on by (most cited first, 4 of 4)

- [Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation](https://mlsanity.com/paper/2205.05197/) (2022)
- [A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments](https://mlsanity.com/paper/2209.01482/) (2022)
- [Evolutionary City: Towards a Flexible, Agile and Symbiotic System](https://mlsanity.com/paper/2311.14690/) (2023)
- [Adaptive traffic signal safety and efficiency improvement by multi objective deep reinforcement learning approach](https://mlsanity.com/paper/2408.00814/) (2024)

## Abstract (arXiv)

Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods under normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes or entire intersections are affected. This paper aims at tackling this problem and presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. With the purpose of producing fast and reliable decisions, we combine the fast running Machine Learning (ML) algorithms and the reliable Genetic Algorithms (GA) into a single optimization framework. As a benchmark, we first start with deploying a typical GA algorithm by considering the phase duration as the decision variable and the objective function to minimize the total travel time in the network. We fine tune the GA for crossover, mutation, fitness calculation and obtain the optimal parameters. Secondly, we train various machine learning regression models to predict the total travel time of the studied traffic network, and select the best performing regressor which we further hyper-tune to find the optimal training parameters. Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree, which is the best performing regressor, together in a single optimization framework. Comparison and results show that the new BGA-ML is much faster than the original GA algorithm and can be successfully applied under non-recurrent incident conditions.