Using Wi-Fi ‘traces’ registered when a device makes a connection with a router, the team at EPFL were able to determine paths navigated by the institution’s students and staff. According to researcher Antonin Danalet, this methodology could prove to be a potent tool for future studies on pedestrian infrastructure.
More than 2 million points of data were collected from 789 Wi-Fi access points over a ten-day period in 2012, according to a report from Phys. By pairing this information with map data, researchers could then dip into the task at hand — finding out what were the key motivators in people’s lunchtime habits.
Routes were drawn up based on the Wi-Fi connections that were accessed, as well as other information like school timetables and sales figures from various local eateries. Once all this data was brought together, it could be used to answer questions about what factors influenced the question of what to eat for lunch.
Budget-conscious students were found to be wary of restaurants that priced food at premium rates, instead opting for eateries that were proximal to their classes and were also likely to have shorter queues. Interestingly, the type of food on offer didn’t seem to have much of an effect on the decision-making process.
While this study centered on university students eating lunch, Danalet is confident that the same techniques could be useful elsewhere. The researcher suggests that this model could be implemented to study pedestrian infrastructure in locations like music festivals, train stations, and hospitals.
