Brief description: 

More and more vehicles are connected to the Cloud. New cars are already prepared for connectivity when they get out from factories and old ones can be adapted using a wide range of devices. This raw data provides a high potential value if you know how to process it. Even more, if additional data sources are considered such as road maps, weather information, vehicle features, among others. AnswareTech has been working on processing and aggregating all this information to provide valuable services available at online dashboard and mobile applications.

The main objectives of the services aim at reducing fuel and operational costs and increasing security and fuel performance by monitoring drivers' behaviour and educate them at improving the way they drive.

Services such as pattern behaviours, driver monitoring and provision of advices have been proven and validated through a pilot with 20k connected vehicles. This pilot was developed as part of Transforming Transport project funded by EU.

Main Features: 
  • Explore easily all vehicle trips related information such as route, type of route, traffic jams, elevation, weather, driver behaviour.
  • 15 factors for monitoring driving performance of every trip. From fine grain (trip) to wider overview (weeks or months).
  • Thanks to our fuel consumption algorithm developed by advanced Machine Learning techniques, you can find out and focous on the most potencial fuel reduction vehicles and trip types.
  • Intelligence Notification system that provides customized advices to drivers according to their behaviour.
  • Make better decisions by a data-driven approach.
  • Integrated API to retrieve the information from different backend systems.
Areas of Application: 
  • Fleet owners (car rental companies, businesses to deliver goods to customers or transport passengers): Management of connected vehicle fleets.
  • Insurance companies: implement services such as  as pay as you drive (PAYD) and pay how you drive (PHYD).
Customer Benefits: 
  • Promote safe and ecological driving driving
  • Reduce cost on fuel consumption
  • Increase fleet availability by reducing vehicles' breakdowns and accidents.
  • Identify inefficient pattern behaviour.
Technological novelty: 
  • Predictive and descriptive analysis of readily available in-vehicle data coming from the cloud for connected car service providers.
  • Machine Learning algorithms for estimating fuel consumption of vehicles.
Component / Service / App
Dashboard of Weekly summary of driver skills evolution
Dashboard showing details of a trip


AnswareTech SL
BDVA member
BDV Reference categories: 
Data Analytics
Data Management
Data Visualisation and Interaction
Safety and Security
Transport, storage and logistics
Urban Mobility
Readiness Level: 
Transforming Transport, a Horizon 2020 Big Data Value Lighthouse Project