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.
- Crowd monitoring
- Discrete choice modeling
- Link flow estimation
- Recursive link-based logit model
- Route choice