Analytic methods require a lot of data. Analysing travel data gives very interesting insights because it can be helpful for several different parties like transportation companies, accommodation companies and the traveller himself/herself. However, data about how people spend their free time might be considered as private by some, making people more cautious when sharing these data. Traap – the TRAvel mAP wants to tackle this issue of convincing the user to share his/her travel data. The user of Traap and the entity running Traap should mutually benefit.

What Traap Does

Traap, the TRAvel mAP, shows the travel history of users. A user can log in to enter their travel data which will not be shared without consent (destination, arrival date and departure date). Traap then finds the coordinates of the travel destinations and displays these destinations on a world map. The travel destinations are marked with a circle of which its size varies according to how long this user stayed at that travel destination.

Value Proposition of Traap

The main idea of Traap is to collect people's travel data to use them for future analytical tasks. The data are kept anonymised. To convince more people to provide their data, Traap should have a strong network effect where people want to use the application because the critical mass has been reached. This critical mass can be reached by integrating Traap into existing social media services. Over there, users should be motivated to share their travel history with others and proudly present which travel destinations they have already visited. Besides letting others enjoy the travel diary of the user, users can also find experts for places where they want to go.

The range of data which is collected by Traap can be extended. Users should be encouraged to provide information not only about the travel destination of each trip but also about the activities performed at each travel destination, with each activity being given a rating. Traap provides different functionalities from the currently available products such as TripAdvisor because it has the ability to pair dates with activities and therefore provide the possibility to search for recommended activities in a certain time period.

To convince the user to provide even more data, Traap offers more possibilities to classify and visualise a huge amount of data. The map can have different categories such as work and travel. Then, a user could not only share where he/she travelled to but also where he/she has been to for work.

To motivate the user to continuously use Traap, gamification methods can be used. The user can increase his/her level with rating more travel destinations and providing more travel data.

To summarise the proposed value for the users, users can share their travel history in an appealing manner, find experts for specific locations and get recommendations based on the other users’ input.

How Collected Data Can Be of Value

First and foremost: Data is very valuable and can always be sold (assuming that the data is reliable).

Besides selling the data, a simple dashboard can give information about where users travelled to the most or when and where they preferred to perform certain activities. Also, the historical development of the quality of each activity can be tracked, as the average rating of the activity can be considered as the activity quality.

To do more advanced analytics, the different data can be combined (time, destination and activity) to find ideal travel destinations or routes. Users can give some information about their intended trip and Traap tries to fill the remaining information based on the data provided by all the users. The ideal trip should be planned based on the information about suitable activities at a certain location in a particular season.

How Traap Is Built

The whole system is built with the programming language Java. It runs on a web server and is available in a web browser. The application can also be installed as a PWA. The application frame is realised with Spring Boot and the frontend is implemented with the framework Vaadin Flow. The MVP of the hackathon does not use any database and the data is just stored in a hash map. The coordinates for the travel destination are requested from the OpenCage Geocoder API. The map in the frontend is implemented with the Vaadin plugin Leaflet4Vaadin.

Challenges at Hackathon

  • How to motivate users to provide accurate travel data?
  • How to keep the travel data input simple for users while still gaining accurate coordinates?
    • Consider using another API for geocoding because OpenCage somehow does not provide accurate coordinates.

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