Mobility-based Traffic Forecasting in a Multimodal Transport System
This work addresses traffic congestion prediction for urban planning and economic welfare, but it appears incremental as it applies existing machine learning methods to mobility data.
The paper tackles traffic forecasting in multimodal transport systems by analyzing population mobility data to predict congestion, achieving predictions with a certain probability based on historical observations.
We study the analysis of all the movements of the population on the basis of their mobility from one node to another, to observe, measure, and predict the impact of traffic according to this mobility. The frequency of congestion on roads directly or indirectly impacts our economic or social welfare. Our work focuses on exploring some machine learning methods to predict (with a certain probability) traffic in a multimodal transportation network from population mobility data. We analyze the observation of the influence of people's movements on the transportation network and make a likely prediction of congestion on the network based on this observation (historical basis).