Mobility-based Traffic Forecasting in a Multimodal Transport System

TL;DR

This paper explores using machine learning to predict traffic congestion in multimodal transportation networks based on population mobility data, aiming to improve traffic management and social welfare.

Contribution

It introduces a novel approach to predict traffic congestion by analyzing population mobility patterns with machine learning in a multimodal transport system.

Findings

Mobility data can effectively predict traffic congestion.

Machine learning models show promising accuracy in traffic forecasting.

The approach can inform better traffic management strategies.

Abstract

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).