La mobilité quotidienne dans les grandes villes du monde : application de la théorie des réseaux bayésiens - Giovanni Fusco (Cybergeo)
At the frontier between multivariate statistics and artificial intelligence, Bayesian networks are a powerful tool to model systemic knowledge. A particularly interesting use of Bayesian networks is the search of causal links between variables in a database. In this paper, we analyze a database on urban daily mobility covering more than one hundred big world cities. Through the generating algorithms of Bayesian networks, we produced two networks derived from two different hypotheses on the causal hierarchy among the variables. Car ownership is identified as a crucial variable when considering the network resulting from the hypothesis that daily mobility determines urban structure. This network has a higher likelihood than the one resulting from the opposite hypothesis. On the other hand, the two networks converge on the importance of the offer of urban freeway and of the average speed of the road network. These two factors explain the modal split and the costs and externalities of urban transportation.