Network Capacity Maximization Using Route Choice and Signal Control With Multiple OD Pairs

Wang, Shangbo; Li, Changle; Yue, Wenwei; Mao, Guoqiang

doi:10.1109/tits.2019.2909281

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A Modelling Stochastic Correlation Based On Random Walk1

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Cited by 8 publications

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“…The correlation among agents is affected by many practical factors in a traffic signal control system, including current phases of signal light, dynamic traffic volumes, and speed limitation of vehicles. Traditionally, the raw message reflecting correlation is directly included to form a correlation model and then develop control policies [32], [33], which, however, inevitably leads to intensive computation. Moreover, a scalar message is usually inadequate to capture the stochastic correlation in complex traffic environments.…”

Section: A Modelling Stochastic Correlation Based On Random Walkmentioning

confidence: 99%

Learning the Structure of Hub Network Based on Graph Model

2019

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In this paper, we focus on the structure learning problem of the hub network. In the neighborhood selection framework, we use the L1 and L2 regularizers to incorporate the sparse and group prior of the hub network, so as to make the network easier to generate Hub. We employ the coordinate descent algorithm to solve the resulting model. Simulation and real data analysis show that the proposed method is effective and applicable in parameter estimation and model selection, and results illustrate the influence ability of the control parameter on the model.

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Section: A Modelling Stochastic Correlation Based On Random Walkmentioning

confidence: 99%