从需求到系统共同塑造：交通系统中用户角色的综述

中文摘要 AI 辅助

用户是交通系统的核心，然而在交通建模和决策中，他们的角色常常被简化。传统方法主要通过需求相关的输入（如出行流量、配送请求和充电负荷）来表征用户。然而，数字化、电动化和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、服务可用性和信息的响应方式能够影响运营和规划决策。本综述发展了一种基于概念、分类学的综合方法，探讨用户相关信息如何在交通决策中被表征和使用。与主要按交通领域组织文献不同，我们区分了三种用户角色：被动需求、运营代理和系统共同塑造者。基于交通电气化、城市物流、共享出行、多式联运和出行即服务（MaaS）等领域的研究，本综述考察了用户偏好、灵活性、支付意愿、接受度、参与度和反馈如何被纳入建模和决策框架。综合结果表明，在测量和预测用户行为方面已取得进展，但用户相关变量仍常常主要用于行为分析，而非作为系统决策的可操作输入。它们与运营决策的整合仍不完整，从运营用户响应和结果到长期规划的系统性反馈甚至更为有限。关键挑战包括闭合运营与规划之间的循环、对异构和策略性用户建模、将优化与用户接受度整合，以及确保隐私、公平性和可部署性。综合这些发现，指向从用户行为分析向用户知情和用户驱动的交通系统设计的转变。

英文摘要

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.

发表机构

• Chalmers University of Technology（查尔姆斯理工大学）

机构由 AI 辅助整理，请以论文原文为准。