The availability of newly emerging forms of data in recent years has provided new opportunities to study spatial intrapersonal variability, namely the variability in an individual’s destination and route choices from day to day. As well as providing insights into traveller needs, preferences and adaptive capacity, spatial intrapersonal variability can also inform the development of user classes for models of network disruption and for measuring behaviour change to evaluate the impact of network changes. This paper proposes a methodology for measuring spatial intrapersonal variability using point-to-point sensor data such as Bluetooth or number plate data. The method is innovative in accounting for sensor specific probabilities of detecting a passing device or vehicle and in providing a single measure for each traveller which considers destination and route choice variability and both the quantity of different trajectories utilised as well as the intensity with which they are used. A data science method is also presented for examining relationships between different trajectories observed in the network based on whether they are typically made by the same travellers. A case study using 12 months of real-world data is presented. The example provided demonstrates that a substantial amount of data processing is required, but the outputs of the methods are easily interpretable. Perhaps surprisingly, the analysis showed that the trips people made on weekdays were more evenly spread across a range of different trajectories than the trips they made during the weekend which were more concentrated into a few spatially similar clusters.

近年来新出现的数据形式的可用性为研究空间内在变异性提供了新的机会，即个人每天的目的地和路线选择的变异性。除了提供对旅行者需求、偏好和适应能力的洞察之外，空间内在可变性还可以为网络中断模型和测量行为变化以评估网络变化影响的用户类别的开发提供信息。本文提出了一种使用点对点传感器数据（例如蓝牙或车牌数据）测量空间内在可变性的方法。该方法在考虑传感器检测通过设备或车辆的特定概率以及为每个旅行者提供单一测量方面具有创新性，该测量考虑了目的地和路线选择的可变性以及所利用的不同轨迹的数量以及它们的强度用过的。还提出了一种数据科学方法，用于检查网络中观察到的不同轨迹之间的关系，基于它们是否通常由相同的旅行者制作。提供了一个使用 12 个月真实世界数据的案例研究。提供的示例表明需要大量数据处理，但方法的输出很容易解释。也许令人惊讶的是，