This paper describes a method to estimate the parameters of a Recursive link-based Logit model (RL) using measurements of a set of spatially fixed proximity sensors, with limited hit rates, which can uniquely identify people, such as Wi-Fi-, RFID- or Bluetooth-sensors. The observed ‘route’ of an individual, where we focus on pedestrians in an urban or event context, is modelled as the sequence of sensors that have identified the individual during his or her trip. Obviously, these ‘routes’ contain large gaps, which makes traditional estimation techniques not applicable. Although we do not exactly know what happens within these gaps, we do have some specific insight about the individuals behavior between two identifications; we know with a certain probability which is related to the hit rate of the sensors, that the individual did not cross another sensor location between the two identifications. This paper therefore describes a method to estimate the parameters of an RL model that specifically exploits this knowledge. The framework also allows us to formulate a probabilistic link utilization estimation method, which can be used to estimate link flows in a network based on the sensor observations. The effectiveness of the methodology is demonstrated in simulation using an artificial network, after which the methodology is tested on a real data set, collected at a Dutch music event.

本文介绍了一种方法，该方法使用一组空间固定的接近传感器（具有有限的命中率）的测量结果来估计基于递归链接的Logit模型（RL）的参数，该传感器可以唯一地识别人，例如Wi-Fi-，RFID -或蓝牙传感器。我们将观察到的个人“路线”（我们将重点放在城市或事件环境中的行人上）建模为在个人旅行期间识别个人的传感器序列。显然，这些“路线”存在很大差距，这使得传统的估算技术不适用。尽管我们不完全知道在这些差距之内发生了什么，但我们确实对两个身份之间的个人行为有一些特定的见解。我们以一定的概率知道与传感器的命中率有关，该个人没有越过两个标识之间的另一个传感器位置。因此，本文描述了一种估计RL模型参数的方法，该方法专门利用了这一知识。该框架还允许我们制定概率性的链路利用率估算方法，该方法可用于基于传感器的观测值估算网络中的链路流量。该方法的有效性在使用人工网络进行的仿真中得到了证明，然后在荷兰音乐活动中收集的真实数据集上对该方法进行了测试。可以根据传感器的观测值来估算网络中的链路流量。该方法的有效性在使用人工网络进行的仿真中得到了证明，然后在荷兰音乐活动中收集的真实数据集上对该方法进行了测试。可以根据传感器的观测值来估算网络中的链路流量。该方法的有效性在使用人工网络进行的仿真中得到了证明，然后在荷兰音乐活动中收集的真实数据集上对该方法进行了测试。