Route choice behaviour is a key factor in determining pedestrian movement flows throughout the urban space. Agent-based modelling, a simulation paradigm that allows modelling individual behaviour mechanisms to observe the emergence of macro-level patterns, has not employed empirical data regarding route choice behaviour in cities or accommodated heterogeneity. The aim of this paper is to present an empirically based Agent-Based Model (ABM) that accounts for behavioural heterogeneity in pedestrian route choice strategies, to simulate the movement of pedestrians in cities. We designed a questionnaire to observe to what degree people employ salient urban elements (local and global landmarks, regions, and barriers) and road costs (road distance, cumulative angular change) and to empirically characterise the agent behaviour in our ABM. We hypothesised that a heterogeneous ABM configuration based on the construction of agent typologies from empirical data would portray a more plausible picture of pedestrian movement flows than a homogeneous configuration, based on the same data, or a random configuration. The city of Münster (DE) was used as a case study. From a sample of 301 subjects, we obtained six clusters that differed in relation to the role of global elements (distant landmarks, barriers, and regions) and meaningful local elements along the route. The random configuration directed the agents towards natural elements and the streets of the historical centre. The empirically based configurations resulted in lower pedestrian volumes along roads designed for cars (25% decrease) but higher concentrations along the city Promenade and the lake (40% increase); based on our knowledge, we deem these results more plausible. Minor differences were identified between the heterogeneous and homogeneous configurations. These findings indicate that the inclusion of heterogeneity does not make a difference in terms of global patterns. Yet, we demonstrated that simulation models of pedestrian movement in cities should be at least based on empirical data at the average sample-level to inform urban planners about areas prone to high volumes of pedestrians.

路线选择行为是决定整个城市空间的行人移动流量的关键因素。基于代理的建模是一种模拟范式，它允许对个体行为机制进行建模以观察宏观层面模式的出现，但尚未使用有关城市路线选择行为或适应异质性的经验数据。本文的目的是提出一种基于经验的基于代理的模型 (ABM)，该模型解释了行人路线选择策略中的行为异质性，以模拟城市中行人的运动。我们设计了一份问卷来观察人们在多大程度上使用了显着的城市元素（本地和全球地标、区域和障碍）和道路成本（道路距离、累积角度变化），并在我们的 ABM 中经验性地描述代理行为。我们假设，与基于相同数据或随机配置的同质配置相比，基于经验数据构建代理类型的异构 ABM 配置将描绘出更合理的行人运动流图。明斯特市 (DE) 被用作案例研究。从 301 名受试者的样本中，我们获得了六个集群，这些集群在全局元素（遥远的地标、障碍和区域）和沿途有意义的局部元素的作用方面有所不同。随机配置将特工引向自然元素和历史中心的街道。基于经验的配置导致为汽车设计的道路沿线的行人流量减少（减少 25%），但沿城市长廊和湖泊的集中度更高（增加 40%）；根据我们的知识，我们认为这些结果更合理。在异质和同质配置之间发现了微小的差异。这些发现表明，包含异质性并没有对全球模式产生影响。然而，我们证明了城市中行人运动的模拟模型至少应该基于平均样本水平的经验数据，以告知城市规划者容易出现大量行人的区域。