Augmented ε-Constraint-Based Optimization for Multi-Objective Multi-Modal Transport Networks Management

Abstract

The increasing urbanization, combined with shrinking space for transport infrastructure and private parking, significantly challenges urban accessibility. Moreover, the rising number of vehicles exacerbates congestion in city centers, leading to longer commute times, increased noise levels, and greater air pollution. These issues underscore the urgent need for creating low-car urban zones. One promising approach is an integrated traffic management system that considers various modes of transportation—such as cycling, walking, shared mobility, and public transport. However, multi-modal traffic management typically involves diverse stakeholders with potentially conflicting interests, which necessitates a balance of these interests through multi-objective optimization. Traditional approaches often employ a weighted sum method to transform multiple objectives into a single objective. This method significantly constrains the solution space and complicates the assignment of appropriate weights to different objectives. Therefore, generating a Pareto front for multi-modal traffic management could provide decision-makers with a set of efficient solutions, enabling them to select the most suitable option. The ε-constraint method is recognized for its ability to generate a Pareto front. The question we discuss here is whether this method can be effectively applied to managing multi-objective, multi-modal traffic networks. In this study, we answer this question by proposing an augmented ε-constraint-based optimization framework for multi-objective multi-modal traffic management. This framework is bi-level and can accommodate various traffic models and objectives that reflect the diverse interests of multiple stakeholders. Thus the multi-modal traffic management problem can be formulated as a multi-objective nonlinear optimization problem. The augmented ε-constraint method (Mavrotas, 2009) is employed to efficiently address the multiple objectives, and the multi-start sequential quadratic programming method is used to solve the nonlinear optimization problems, such that the Pareto front is obtained. We validate the effectiveness of our framework through a case study, whose preliminary results show that our method improves the traffic performance and provides insights into the trade-off among different objectives.