Analysing Spatial Intrapersonal Variability of Road Users Using Point-to-Point Sensor Data
Analysing Spatial Intrapersonal Variability of Road Users Using Point-to-Point Sensor Data
复制标题
DOI：
10.1007/s11067-021-09539-4
复制
发表时间：
2021-07
影响因子：
2.4
通讯作者：
Fiona Crawford;D. Watling;R. Connors
中科院分区：
文献类型：
--
作者：
Fiona Crawford;D. Watling;R. Connors
近年来，新出现的数据形式的可用性提供了新的机会来研究空间的个人内部变异性，即一个人的目的地和路线选择的变化，从一天到一天。除了提供对旅行者需求、偏好和适应能力的深入了解之外，空间的个人内部变异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的影响的用户类别的发展提供信息。本文提出了一种方法来测量空间的自我变异性使用点对点的传感器数据，如蓝牙或车牌数据。该方法在考虑传感器检测经过设备或车辆的特定概率以及为每个旅行者提供单个测量方面是创新的，该测量考虑目的地和路线选择可变性以及所使用的不同轨迹的数量以及它们被使用的强度。还提出了一种数据科学方法，用于根据网络中观察到的不同轨迹是否通常由相同的旅行者进行检查。使用12个月的实际数据的案例研究。所提供的示例表明，需要进行大量的数据处理，但这些方法的输出很容易解释。也许令人惊讶的是，分析表明，人们在工作日进行的旅行比他们在周末进行的旅行更均匀地分布在一系列不同的轨迹上，后者更集中在几个空间相似的集群中。
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.