用户在交通系统中处于核心地位，但在交通建模与决策中其角色常被简化。传统方法主要通过出行流、配送请求和充电负荷等与需求相关的输入来表征用户。然而，数字化、电气化及基于平台的服务创造了更直接的用户-系统交互，用户对价格、激励措施、服务可用性和信息的响应方式能够影响运营和规划决策。本综述构建了一种以概念为导向、基于分类学的综合框架，阐述用户相关信息在交通决策中的表征与应用方式。我们未主要按交通领域组织文献，而是区分了三种用户角色：被动需求者、运营代理者和系统共同塑造者。结合交通电气化、城市物流、共享出行、多式联运以及出行即服务（MaaS）等领域的研究，本文考察了用户偏好、灵活性、支付意愿、接受度、参与度和反馈如何被纳入建模与决策框架。综合结果显示，尽管在测量和预测用户行为方面已取得进展，但用户相关变量仍主要用于行为分析，而非作为系统决策的可操作输入。其在运营决策中的整合尚不完整，而从运营用户响应及结果向长期规划的系统性反馈则更为有限。关键挑战包括闭合运营与规划之间的回路、对异质性及策略性用户进行建模、将优化与用户接受度相结合，以及确保隐私、公平性和可部署性。这些发现共同表明……

Users are central to transportation systems, yet their roles are often simplified in transportation modeling and decision-making. Conventional approaches primarily represent users through demand-related inputs, such as trip flows, delivery requests, and charging loads. However, digital, electrified, and platform-based services create more direct user-system interactions, with users responding to prices, incentives, service availability, and information in ways that can influence operational and planning decisions. This review develops a concept-oriented, taxonomy-based synthesis of how user-related information is represented and used in transportation decision-making. Rather than organizing the literature primarily by transport domain, we distinguish three user roles: passive demand, operational agents, and system co-shapers. Drawing on studies across transportation electrification, urban logistics, shared mobility, multimodal transport, and Mobility-as-a-Service (MaaS), the review examines how user preferences, flexibility, willingness to pay, acceptance, participation, and feedback are incorporated into modeling and decision frameworks. The synthesis shows that progress has been made in measuring and predicting user behavior, but user-related variables are still often used mainly for behavioral analysis rather than as actionable inputs to system decisions. Their integration into operational decision-making remains incomplete, and systematic feedback from operational user responses and outcomes to long-term planning is even more limited. Key challenges include closing the loop between operation and planning, modeling heterogeneous and strategic users, integrating optimization with user acceptance, and ensuring privacy, equity, and deployability. Together, these findings point toward a shift from user-behavior analysis to user-informed and user-driven transportation system design.