Data-driven study and Design of Innovative solutions for Urban Mobility

Improving the efficiency and sustainability of human mobility is one of the key problems to solve with the increase of world urban areas, population and pollution.

With Internet of Things (IoT), a multitude of everyday objects and sensors are always connected to the Internet, allowing them to communicate and interact with each other. IoT allows to remotely monitor, manage, and gather a huge amount of data about them and their surrounding environment. 

Regarding to mobility, IoT systems have been highly employed to monitor urban traffic, to track shared transport means such as car sharing, scooter sharing or bike sharing, and in general record data about mobility habits. 

Being able to monitor the mobility habits, we can now understand it and suggest new ways to improve urban mobility implementing the concepts of smart cities and smart mobility. 

The smart mobility concept includes a combination of technology paired with physical infrastructure and services, to improve people’s quality of life.  

Numerous applications are emerging covering both Intelligent Transportation and Real Time Traffic Management Systems for this. Part of these solutions includes a shift from a personally-owned modes of transportation, to the Mobility as a Service model and multi-modal systems where the transportation is seen as a service. In this scenario, new transport solutions are emerging. For instance, vehicle sharing (car, scooter, …) has become one of the most popular shared mobility options in Smart City environments. These solutions help reducing congestion and pollutions by reducing the number of cars in the urban area. This, and the rise of green solutions such as electric vehicles are posing severe challenges for the urban planners who have to deal with a quickly evolving ecosystem.

The aim of this project is to study the mobility on smart cities where citizens take advantage of public transportation, shared platforms, electric vehicles, and multi-modal means. To envision this future, the first objective is to study and model the current mobility habits. As such, the first research goal aims at collecting real mobility data from open-source platforms of different transport means such as car sharing, scooters, public transport, etc.

Then, these data will be used to describe current mobility habits and create a demand models for each transport means and the joint usage. These models abstract the urban mobility habits and will be feed into a model driven simulator able to emulate mobility in an urban scenario. The model will be parametrized, with different parameters that will be able to describe the possible future scenarios. With the built models and the simulations of the scenarios the candidate will be able to answer to many research questions about different aspects of the electric mobility in smart cities such as:  (i) the increasing usage of electric vehicles instead of combustion engine ones, (ii) how to efficiently use multiple transportation means in the same trip, (iii) evaluate different charging station infrastructures and charging policies for electric vehicles, (iv) evaluate the impact of the charging infrastructure to the power grid, (v) find trade-offs between quality of services and costs, (vi) impact of autonomous vehicles.

The candidate must have excellent knowledge of Object-Oriented programming languages, geographic information systems (e.g., with Geopandas tool) and simulation environments (e.g., SimPy)

Other appreciated skills are data analysis, knowledge of statistics and modelling, web crawling, database and big data tools.