Abstract As a major metropolitan city, London faces persistent road congestion and severe air pollution. To address these issues, static electronic road pricing (ERP) models have been implemented. While effective, these are inherently limited in flexibility. This paper explores dynamic ERP models to improve upon static pricing by minimizing air pollution and traffic congestion within the Congestion Charge Zone. The problem is formulated as a multi-stakeholder multi-objective optimization problem, incorporating the perspectives of three stakeholders—the government, vehicle owners, and environmental organizations—and three objectives: air pollution, traffic congestion, and price. The NSGA-II optimization algorithm was applied on a representative day and demonstrated substantial improvements. The concentration of PM ₂. ₅ —the more harmful pollutant—was reduced by up to 23%, while NO 2 levels fell by 2–3%. Traffic flow, used as a proxy for congestion, decreased by approximately 3–4% during peak hours. These improvements were achieved with only a modest increase in the mean price to £12. 51 (from a baseline of £11. 50), with a standard deviation of £1. 59 and a variance of £2. 43 across hourly prices. These results suggest that targeted dynamic pricing—when aligned with environmental and behavioural incentives—can deliver measurable gains in urban air quality and congestion without imposing a significant cost burden on drivers. A core novelty of this work lies in its practical, stakeholder-inclusive problem formulation. While the approach assumes infrastructure for automated price deduction and routing, this limitation can be addressed in future work through advances in vehicle–infrastructure communication systems.
Kaistha et al. (Thu,) studied this question.