This paper introduces the Multi-Activity Transport & Mobility (MATraM) Agent-Based Model (ABM), a novel framework designed to advance activity-based transport modelling by incorporating dynamic activity adaptation. Traditional transport models simulate system performance using varying levels of abstraction, including flow-based, queue-based, and interaction-based mobility representations. While these approaches differ in their treatment of movement and congestion, they typically rely on pre-defined trip patterns that limit responsiveness to changing conditions. In particular, conventional activity-based models generate trips from fixed daily schedules, constraining their ability to capture behavioural flexibility and uncertainty. MATraM addresses this limitation by enabling agents to flag activities modification requests in response to sub-optimal travel conditions, such as increased travel times. By coupling with an activity scheduling and modification framework, the model integrates adaptive decision-making into the generation and execution of daily activity schedules. This allows for a more realistic representation of how individuals adjust their behaviour in response to transport system dynamics, leading to emergent mobility and congestion patterns. The ABM is presented following the ODD protocol, outlining its purpose, structure, and implementation. MATraM includes detailed representations of agents, their activity schedules, and the transport network, alongside submodels governing routing, scheduling, and behavioural adaptation. By bridging activity-based modelling with interaction-based mobility simulation, MATraM provides a flexible and extensible platform for exploring transport dynamics under uncertainty. This work contributes to the development of next-generation transport models capable of capturing the complex interplay between individual behaviour and system-level outcomes.

翻译：[translated abstract in Chinese] 本文提出了一种多活动交通与出行（MATraM）智能体模型（ABM），这是一个通过融入动态活动适应性来推动基于活动的交通建模发展的新型框架。传统交通模型通过不同抽象层次模拟系统性能，包括基于流量、基于队列和基于交互的出行表征。尽管这些方法在运动和拥堵处理上存在差异，但它们通常依赖预定义的出行模式，限制了对变化条件的响应能力。具体而言，传统的基于活动模型从固定日程生成出行，约束了其捕捉行为灵活性和不确定性的能力。MATraM通过使智能体能够在遇到次优出行条件（如旅行时间增长）时标记活动修改请求，解决了这一局限性。通过与活动调度与修改框架耦合，该模型将自适应决策整合到日常活动计划的生成与执行过程中。这使得个体如何根据交通系统动态调整行为得以更真实地呈现，从而产生涌现性出行与拥堵模式。该ABM遵循ODD协议进行阐述，概述其目的、结构与实现。MATraM包含智能体、活动计划和交通网络的详细表征，以及控制路径规划、调度和行为适应的子模型。通过衔接基于活动的建模与基于交互的出行仿真，MATraM为探索不确定性下的交通动态提供了一个灵活且可扩展的平台。本工作有助于发展能够捕捉个体行为与系统层面结果之间复杂交互的下一代交通模型。