# Real-Time Predictive Control Strategy Optimization

arXiv: https://arxiv.org/abs/1901.04571 · HTML: https://mlsanity.com/paper/1901.04571/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2019.

## Built on by (most cited first, 1 of 1)

- [Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes](https://mlsanity.com/paper/2504.08074/) (2025)

## Abstract (arXiv)

Traffic congestion has lead to an increasing emphasis on management measures for a more efficient utilization of existing infrastructure. In this context, this paper proposes a novel framework that integrates real-time optimization of control strategies (tolls, ramp metering rates, etc.) with guidance generation using predicted network states for Dynamic Traffic Assignment systems. The efficacy of the framework is demonstrated through a fixed demand dynamic toll optimization problem which is formulated as a non-linear program to minimize predicted network travel times. A scalable efficient genetic algorithm is applied to solve this problem that exploits parallel computing. Experiments using a closed-loop approach are conducted on a large scale road network in Singapore to investigate the performance of the proposed methodology. The results indicate significant improvements in network wide travel time of up to 9% with real-time computational performance.