Dual-Layer Dynamic Optimization Model for Carbon-Conscious Transport Mode Allocation

Abstract

Within the framework of China’s “Dual Carbon” strategy, numerous cities have articulated visions and objectives for optimizing the travel structure of transport to further urban sustainable development goals. A critical challenge lies in minimizing CO₂ emissions from road networks while meeting diverse transport demands. However, at present the mode shares are set arbitrarily and may not be realistically achievable. When government officials establish travel structure targets, they may not adequately consider the intricate balance between residents’ travel demands and low-carbon development objectives. To address this issue, this paper presents a dual-layer optimal allocation model for transport modes, which simultaneously addresses travel demand management and carbon emission control. The upper-layer model evaluates carbon emissions with the help of speed-dependent emission factors for various transport modes, and the lower-layer model leverages the logit Stochastic User Equilibrium (logitSUE) model to yield the velocities of road segments under a diverse array of travel structures. A sophisticated fusion algorithm, integrating the Dial_MSA algorithm with a genetic algorithm (GA), is developed to solve the model. The proposed model and algorithm are tested on a large-scale real network and show its robustness and scalability. The optimal travel structure derived from this study can provide a theoretical foundation and empirical support for policymakers and urban planners in setting transport infrastructure goals and strategies.