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Tomorrows bookings for Tourism

Tomorrows bookings for Tourism

Project Profile

The client wanted to explore the possibility to train a predictive model based on movement data, which can estimate a visitor density based on the origin market.

The POIS were defined by the client and coordinated with the mobile operator for signaling capabilities.

The experience in handling movement data as well as the technical estimability of its use by the customer was to be evaluated.

A small POC ecosystem between motion data and PowerBI visualization was created.

At a glance - essential project data

DurationFrom 4/1/2020 to 7/30/2020 with about 4 months of full engagement
Data and ToolsMarket - Tourism
 • Mobile phone data
 • Flight data VIE
 • Weather
 • Global vacation calendar
 • Power-BI
Integration • Web API for Model Usage
 • CSV on FTP
 • PowerBI Datasets
AI Methods
 • Deep - Learning
 • Time Series

Engagement Use-Case

Predict tourist visit history at locations or POIs over the course of the day over the booking year.

Client motivation / Solution aims

  • Increase efficiency of occupancy rates at a POI

  • Touristic forecasting of future visitation trends in tourist markets

  • Dynamic pricing of events

  • Occupancy forecasting and budgeting for POIs

  • Campaign optimization for visitor control

AI Approach

AI key technology used in our solutionAdjustment of mobile signaling due to structural breaks (roaming contracts, outages, new masts, etc). Adjusting the average length of stay per POI.
Qualification of the utilization by Google "popular times"
Solution Approach • Multiple DeepLearning Models
 • DNN
 • TimeSeries Prediction
Project ApproachSimply agile
Project TypeProof-Of-Concept (POC)
ML Integration and ML Operations • Operation Integration API
 • Visualization Power-BI

Insights and Details

Dwell time at POI

Feature Importance for optimization of model parameters

Prediction of SIM population by country of origin and time of creation