## 1 Introduction  
In response to the escalating issue of urban traffic congestion, many cities have expanded their road infrastructure to increase capacity. However, the rapid pace of urbanization has led to budgetary constraints and land scarcity, making it difficult to meet growing traffic demand with road supply alone. Simply adding more roads often fails to alleviate peak-hour congestion, as the increased availability may encourage more usage during peak times [1]. Consequently, researchers have turned their focus to traffic demand management (TDM) strategies, such as congestion pricing and high-occupancy vehicle lanes, to find more effective solutions. Among these, the travel reservation strategy (TRS) has emerged as a promising TDM approach, gaining attention from both academia and industry. The core idea of TRS is to segment traffic resources and allow travelers to book their travel times and routes in advance. This method helps optimize traffic flow, balancing spatial and temporal distribution and improving the use of existing road resources. What distinguishes TRS from conventional TDM methods is its emphasis on the dynamic and divisible nature of roadways [2]. By maximizing road usage through advance scheduling, TRS enhances infrastructure efficiency and offers travelers flexibility and convenience in planning their trips. This innovative solution presents a new way to alleviate urban traffic congestion. In recent years, several studies have attempted to develop reserved travel models for highways and urban roads [3; 4; 5]. A common limitation in these studies is the assumption that all travelers will automatically participate in reserved travel, overlooking the willingness and choice of individual travelers. Successful implementation of such strategies depends not only on technical feasibility but also on user acceptance. For a policy to succeed, most travelers must comply with the reservation system. Surveys on TDM [6; 7] indicate that introducing appropriate incentives and penalties at the early stages can improve public acceptance, ensuring effective implementation. Thus, this paper proposes incentive and penalty mechanisms to encourage participation in reserved travel while deterring non-compliance, refining the strategy's effectiveness. Additionally, evaluations of TRS's effectiveness often focus on a single perspective, such as traveler or system efficiency, neglecting the complex interactions among various stakeholders like governments, transport operators, and users [8; 9; 10]. As an innovative approach, TRS can optimize traffic demand, improve travel experience, and reduce environmental pollution. However, its implementation also involves balancing the interests of different parties. For example, governments may initially bear financial burdens due to subsidies; travelers unable to reserve may incur extra costs; and transport operators might face challenges in service adjustments. Thus, the in-depth optimization of TRS is a complex issue requiring collaboration among all stakeholders. Designing a TRS that maximizes both demand and social welfare has become a key area of research. Traditional mathematical and statistical models have often been used to assess the effectiveness of TRS [9; 10; 11]. These studies show that TRS can improve traffic efficiency. However, a key limitation of these models is their static nature in traffic assignment, which struggles to account for dynamic decision-making processes of travelers regarding travel times and modes. To address this gap, this paper employs an Agent-Based Model (ABM) to simulate the heterogeneous behaviors of travelers with different time values in the reservation system. ABM offers a more accurate representation of travelers' choices and system optimization [12]. By capturing both micro-level decisions and macro-level outcomes, ABM provides deeper insights than traditional models, which often assume homogeneous behavior or rely on idealized assumptions like full traffic information and equilibrium flow.