Large scale automated data collection on the movement and activities of pedestrians is a challenging problem. In a previous work we developed a network of cheap sensors that can perform larger scale data collection of pedestrian movements using WiFi signals emitted by WiFi-enabled devices (such as smartphones). The devices are deployed during an entire summer (4 months period) on a pedestrianized street spanning 14 intersections. This data is then processed to produce indicators describing the pedestrians’ behaviours, such as time spent, pedestrian density variations through time, flow of pedestrians and the tracking of trajectories and destinations over time. The use of street-level land usage data allows further conclusions to be made about the reasons for these behaviours. The indicators developed, in addition to facility usage information, are then used to develop and estimate three different dynamic next location choice models. The three models compared are a multinomial logit model (MNL), a mixed multinomial logit model (MMNL) and a mixed multinomial logit model with agent effect (MMNL-AE). The latter model is determined to be the most representative of the observed pedestrian behaviours. It can forecast the next location, within the detected area, any individual pedestrian chooses, conditioned upon its previous and current locations. The model can subsequently be used to predict future events in similar places, and help with the planning, promotion and optimization of such events.
Beaulieu, A., & Farooq, B. (2019). A dynamic mixed logit model with agent effect for pedestrian next location choice using ubiquitous Wi-Fi network data. International Journal of Transportation Science and Technology, 8(3), 280–289. https://doi.org/10.1016/j.ijtst.2019.02.003