Discovering urban mobility patterns with PageRank based traffic modeling and prediction

Urban transportation system can be viewed as complex network with time-varying traffic flows as links to connect adjacent regions as networked nodes. By computing urban traffic evolution on such temporal complex network with PageRank, it is found that for most regions, there exists a linear relation between the traffic congestion measure at present time and the PageRank value of the last time. Since the PageRank measure of a region does result from the mutual interactions of the whole network, it implies that the traffic state of a local region does not evolve independently but is affected by the evolution of the whole network. As a result, the PageRank values can act as signatures in predicting upcoming traffic congestions, We observe the aforementioned laws experimentally based on the trajectory data of 12000 taxies in Beijing city for one month. (C) 2017 Elsevier B.V. All rights reserved.