## 1 Introduction
ABM, by contrast, allows travelers to make decisions under incomplete information, reflecting the real-world complexities of decision-making, and offering higher accuracy in modeling individual heterogeneity [13; 14]. Congestion is a well-known issue caused by information asymmetry: each driver makes decisions based on their own needs, often ignoring the impact on others. This results in a situation where drivers contribute to congestion without being fully aware of their effect on others. The reservation system can help address this issue by providing a platform that makes drivers more aware of the problem. Through such a system, the queue on the road is transformed into a digital queue, where travelers can better anticipate and manage congestion. This paper focuses on applying the reservation strategy to highly congested urban roads such as expressways and major commuter routes. While full-scale reservation systems for entire urban networks are not yet feasible, the proposed system can help optimize traffic organization in specific high-congestion areas. Additionally, through gradual implementation, it can lay the foundation for a future intelligent, connected transportation network with reservation systems. In summary, research on travel reservation models has been gradually gaining attention, but two key issues remain to be addressed: (1) Existing studies predominantly focus on static reservation scheme design and strategy effectiveness analysis, lacking consideration of the synergy between urban multimode transportation systems and dynamic traffic information in the road network. Additionally, the travel choices of multiple user types are often homogeneous, neglecting the multi-dimensional decision-making environment of individual travelers; and (2) the approaches to travel services are overly simplistic, with insufficient integration of travel reward and penalty mechanisms, thereby failing to provide feedback on travel guidance at a macro level. To address these gaps, this study introduces a subsidy incentive and penalty mechanism for users and develops an Agent-Based Multi-Objective Optimization (AMO) model. On one hand, a multi-objective optimization model is constructed to comprehensively consider the interests of both managers and travelers, optimizing travel reward and penalty schemes aimed at maximizing social benefits. On the other hand, within the context of multidimensional decision variables in individual travel, an agent-based multimode dynamic traffic simulation model is constructed based on users' reservation travel information, real-time traffic conditions, and the current state of road supply and demand matching from multiple traffic information sources. This model explores the heterogeneous behaviors of travelers within the reservation system and analyzes the impact of reservation strategies on user travel behavior changes and system performance. The rest of this paper is organized as follows: Section 2 presents a comprehensive literature review relevant to the study. Section 3 details the construction process of the AMO model. Section 4 validates the model's effectiveness through a case study and provides experimental results. Finally, Section 5 concludes the paper and discusses potential directions for future research.