As traveling is usually associated with high levels of stress, passengers feel less satisfied with their experience as a whole and spend less altogether. The research question proposed in response to this issue was: what time do I need to leave my place in order to get to the gate on time? To assess the problem, this team’s proposed solution involved developing a mobile application that decreases the stress of passengers. Three main functionalities were developed for the proof-of-concept: predicting airport wait times, checking luggage status and receiving assistance by airline or airport staff.
Regarding the usage of data, this team mostly worked with passenger data from airlines and airports. The passengers flux dataset was a key resource for this solution, which tracks passenger movements within gate areas. By processing these historical datasets, it was possible to forecast the wait times for certain flights. In particular, this team worked on training a linear regression machine learning model in order to predict wait times for passengers before their arrival at the departure airport. In addition, they combined this prediction with an indoor positioning system, developing a proof-of-concept application that covers all trip phases from home to destination, all embedded in a mobile app.
This concept was also built around the idea of alleviating the stress of travel. In response, InnovATM opted to to make an API to provide a zen indicator, which included information about map positioning, estimates at bottlenecks and potential itineraries and activites at the assigned gate after arrival. First, they calculated the boarding time based on actual flights. Afterwards, they trained a model of all bottlenecks, dynamically updated with live traffic situation and passenger travel habits. Finally, they generated the path to the gate according to the passenger, computing the zen index based on remaining free time available after passenger arrival at the gate.
Using available data, InnovATM mostly focused on the pax movements dataset to group events according to a specific action (e.g. security check, boarding gate, etc.), and measure the time spent on each of these actions. By subtracting boarding time and estimated arrival to gate time, they were able to calculate available free time, assigning a tag representing the passenger zen index. They then created web-based applications in order to exemplify the information provided by the developed API, representing expected waiting times between different terminal check-point areas.
This team focused on provisioning a risk indicator of flight delay or cancellation due to extreme weather in advance. The developed a tool that compared 4D flight trajectories with forecasted adverse weather events to measure the delay risk. These weather estimations were then provided 3 days in advance. The goal was to improve passenger experience while also providing this information to airlines, NM, ANSPs and airports, helping them to plan their operations better.
At a glance, their objective was quite challenging considering the data available; nevertheless, its applicability in the aviation industry definitely has the potential to have a very strong impact. The main obstacle to this objective is trustworthy weather forecasting, with accurate information on weather needed 3 days in advance. SoftLab developed a descriptive tool to visualize how flight trajectories are affected by adverse weather grid layers. The risk was aggregated into high, medium and low categories, based on the patterns learnt by the tool.