The arrival of driverless vehicles represents a unique opportunity for a fundamental change in urban mobility and could lead to safer, healthier, more competitive and greener cities – but only if the vehicles are integrated into an effective public transport network. A future with automated and connected vehicles can have various outcomes depending on how they are designed, operated and regulated. Will they lead to more cars on the road, more urban sprawl and more congestion? Or will they contribute to shaping sustainable and livable cities, the regaining of urban space for people, less vehicles on the road and a higher quality of life?
The experience of operating fleets of automated vehicles remains limited today, therefore this training programme aims at learning from the current deployments operated by various mobility services providers worldwide. Participants will be learning from experienced trainers who are stakeholders of real-life driverless mobility services and the way forward for the introduction of automated vehicles in our cities.
The programme will cover the different modes like Bus, shuttles, and taxi, all deployed for shared mobility.
The SHOW Project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875530n. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commission. Neither the European Union nor the granting authority can be held responsible for them.
Find more information on the SHOW webpage.
This e-Learning module is compose of several modules that will made available to participants/learners to build a solid foundation and understanding of automated mobility.
The course can be taken at anytime from anywhere. Find more information on the SHOW webpage.
A recognised certification will be awarded to those who successfully complete all modules.
A good level of English an essential requirement to complete this course.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875530.