Transport Occupancy Prediction
Big Data and Deep Learning for Transport Occupancy Prediction
Predicts the occupation of the service helping the operator, to improve the dimensioning of its services
Informs users of the predicted occupancy of the bus when it arrives at their stop
Encourages a staggered use of transportation services
Does not require on-board equipment, maximizing the return on investment
Features
- Deep Learning (neural networks) prediction
- Advanced causal inference models
- Variables: calendar, service operations, weather sequences, cultural events…
- Predictions can be integrated in mobile apps, web portals, bus stop panels, Google Maps, Moovit…
- Does not require on-board equipment, maximizing the return on investment
