Detecting Dynamic Critical Links within Large Scale Network for Traffic State Prediction

Can we expose the relationship between the physical dynamics of a network and its predictability? To contribute to this point, we propose a dimensionality reduction method for network states prediction based on spatiotemporal data. The method is intended to deal with large scale networks, where only a subset of critical links can be relevant for accurate multidimensional prediction (MIMO) performances. The algorithm is based on Latent Dirichlet Allocation (LDA) to highlight relevant topics in terms of networks dynamics. The feature selection trick relies on the assumption that the most representative links of the most dominant topics are critical links for short term prediction. The method is fully implemented to an original application field: short term road traffic prediction on large scale urban networks based on GPS data. Results highlight significant reductions in dimensionality and execution time, a global improvement of prediction performances as well as a better resilience to non recurrent traffic flow conditions.

I. Introduction

We study short term state prediction, the task of predicting the picture of a network at time , based on historical data. The directed network is composed of links, each link being characterized by a state at time . For many large scale networks such as energy, transport, or internet, a current challenge relies on the ability to scale up prediction performances based on complex spatiotemporal data. It is reached thanks to Multi-Input Multi-Output (MIMO) supervised regressions from K-NN [1] to NN [2] and M-SVR [3]. The main challenge of these approaches lies in the high-dimensionnality and dynamic of the system. From a supervised learning standpoint, this implies to either propose a dynamic compact representations of the individuals or to dynamically reduce inputs dimensions [4], [5], coping with relevance and redundancy [6]. This common objective is shared by many application fields [7] with various methods [8] and the interest in the ML community is still strong due to increasing dimensionality [9], constrained budget [10] or even security [11] constraints. A second challenge lies in the resilience to non recurrent network conditions such as traffic congestion. Hence, the research issue can be summarized as the selection of the best subset of critical links (inputs of the system) in spatiotemporal networks with following properties: •

Best trade-off between dimensionality reduction and generalization performances,

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Resilience to recurrent or non recurrent congested phases,

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Understanding of the relationship between dynamics of the physical system and network's preditability.