Using Bluetooth and WiFi to unravel real-world slow mode activity travel behaviour

Slow modes have an increasing share in urban mobility. The lack of accurate revealed data has so far hampered scientific research aimed at unravelling slow mode mobility.

Multiple types of data can be collected to gain better insight into slow mode transport and traffic operations, such as counts on specific locations (cross-sections), distributions of flows over the network and dynamics thereof. Typical data collection techniques for vehicular traffic, such as induction loops, cannot be applied, among other things due to the fact that slow modes are not restricted to lanes. Therefore, other, non-intrusive, ways to collect these data need to be investigated.In our paper we look at the applicability of Bluetooth (BT) and WiFi sensors to collect data on pedestrian and cycle flows, using two case studies. The first case study covers the data filtering process, to come from the raw sensor data to the information necessary for behavioural research. It describes the application of 9 sensors in the inner city of Amsterdam. The second case study deals with a BT/WiFi sensor network, installed in the station of Utrecht, the Netherlands. Using these data, we have successfully estimated choice models for the route choice and activity choice behaviour of departing train travellers, showing the potential use of BT/WiFi as a (revealed) data source for modelling travel behaviour in a station.