The importance of mobility in today’s world
Understanding how many people travel between two cities in a specific period is crucial for multiple sectors. This information is essential not only for the efficient design of transport infrastructure, but also for public health, as evidenced during the health crisis caused by Covid-19. Knowing mobility patterns can help anticipate the spread of viruses and other infectious agents.
An advance in mobility research
A research team at Rovira i Virgili University (URV), in collaboration with experts from Northeastern University and the University of Pennsylvania, has developed a new mathematical model capable of predicting human mobility with unprecedented accuracy. The results have recently been published in the journal Nature Communications, and represent a significant step towards a clearer understanding of human journeys.
Model Tradals vs. model trendy
Historically, gravitational models have been the rule for analyzing mobility. Inspired by Newton’s gravitation law, these models are based on the population and the distance between cities to predict the flow of people. However, their simplicity may limit their accuracy. In contrast, automatic learning models that have recently emerged incorporate a wider range of variables, such as the density of services and road infrastructure, but are often difficult to interpret.
The innovation of the ‘scientific robot’
The SEESLAB team has managed to merge the best aspects of traditional and most modern models. With an algorithm called ‘scientific robot’, they have created a new model that not only equals the accuracy of automatic learning models, but is also more accessible and interpretable. Marta Sales-Pardo, a member of the team, emphasizes that this model allows to identify the mobility patterns in a clear and understandable way.
Extrapolation to different contexts
One of the highlights of this new model is its extrapolation capacity. Oriol Cabanas, one of the researchers involved, explains that his methodology can be applied to different territories with minimal adjustments, facilitating the analysis of mobility in various areas, both urban and rural.
Practical applications of the model
The applications of this model are wide and varied. In the field of urban planning, it can be instrumental in the planning of road infrastructure and public transport services, improving efficiency in the distribution of resources and helping to reduce congestion. In addition, its public health application could be decisive in managing the spread of infectious diseases.
Implications for sustainability
In addition, the ability to predict human mobility has a direct impact on sustainability. This model can help optimize energy consumption and minimize greenhouse gas emissions associated with transport, thus contributing to a more sustainable future.
Future of mobility research
The SEESLAB research group is not stopped here. They currently explore new variables to further increase the accuracy of the model, opening the door to a promising future in the study of human mobility.