Development of an intelligent transport system for solving urban mobility problems
Development of an intelligent transport system for solving urban mobility problems
The sprawl of cities and economic development are putting more and more pressure on infrastructure and modes of urban and peri-urban transport, highlighting the concept of mobility which is intended to be not only efficient, but also sustainable. In this regard, the usual traffic models do not always take into account the greater or lesser sustainability of the transport offered to the user, thereby neglecting certain negative externalities which can prove to be both restrictive and negatively structuring.
This thesis aims to define and deploy insightful solutions allowing us to rethink mobility analysis methodologies in an efficient manner via the complementary exploitation of old and new protocols including, without being exhaustive, field surveys, the use of utility functions and the processing of massive data using, among other things, Deep and Machine Learning algorithms. These innovations “inside, around, and outside the box” will change both the perception of transport and the use made of it, constituting a clear opportunity to resolve problems that prevent the practice of truly sustainable mobility.
It should be noted that this thesis will study the city of Casablanca and, conceptually, its first peripheral ring in order to be able to deal with side effects.
Registration procedure
- To submit an application file, please consult this link
- Application deadline: 30/09/2023
A monthly scholarship for 36 months is offered as part of this doctoral thesis work.
Holder of a master's/engineering degree in artificial intelligence, applied mathematics, computer science (programming and big data analysis), econometrics and/or statistics.
Motivated candidate with a solid background in Python and Java programming languages, in addition to very good knowledge of econometrics and statistics.