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Metro passenger's path choice model estimation with travel time correlations derived from smart card data

TRANSPORTATION PLANNING AND TECHNOLOGY 2020年第2期43卷 141-157页

作者： Zhang, Yongsheng Yao, Enjian Zheng, Kangning Xu, Hao Beijing Jiaotong Univ Sch Traff & Transportat 3 Shangyuancun Beijing 100044 Peoples R China Univ Nevada Dept Civil & Environm Engn Reno NV 89557 USA

Smart card data provides a new perspective for estimating a metro passenger's path choice model in a large-scale urban rail transit network with multiple alternative paths between origin-destination pairs. However, existing research does not consider correlations of path travel times among alternative paths when using smart card data for estimation purposes, leading to biased estimations. This paper proposes an approach to estimating the path choice model considering path travel time correlations. In particular, a simplified form of measuring path travel time correlations caused by shared links is proposed to improve estimation efficiency. Then a framework for a linking path choice model and smart card data is developed based on a Gaussian mixture model;an expectation maximization-based estimation algorithm is also provided. Finally, taking the Guangzhou Metro in China as an example, the superiority of estimations based on smart card data considering correlations is observed in both statistical terms and predictions.

关键词： Urban rail transit path choice model smart card data travel time correlation expectation maximization algorithm application

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Likelihood Inference Based on Left Truncated and Right Censored Data From a Gamma Distribution

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IEEE TRANSACTIONS ON RELIABILITY 2013年第3期62卷 679-688页

作者： Balakrishnan, Narayanaswamy Mitra, Debanjan McMaster Univ Dept Math & Stat Hamilton ON L8S 4K1 Canada King Abdulaziz Univ Dept Stat Jeddah 21413 Saudi Arabia Indian Inst Technol Guwahati Dept Math Gauhati 781039 Assam India

The gamma distribution is used as a lifetime distribution widely in reliability analysis. Lifetime data are often left truncated, and right censored. The EM algorithm is developed here for the estimation of the scale and shape parameters of the gamma distribution based on left truncated and right censored data. The Newton-Raphson method is also used for the same purpose, and then these two methods of estimation are compared through an extensive Monte Carlo simulation study. The asymptotic variance-covariance matrix of the MLEs under the EM framework is obtained by using the missing information principle (Louis, 1982). Then, the asymptotic confidence intervals for the parameters are constructed. The confidence intervals based on the EM algorithm and the Newton-Raphson method are then compared empirically in terms of coverage probabilities. Finally, all the methods of inference discussed here are illustrated through a numerical example.

关键词： Asymptotic variance-covariance matrix coverage probability expectation maximization algorithm gamma distribution information matrix left truncation lifetime data maximum likelihood estimates missing information principle Monte Carlo simulation right censoring

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Decomposition optimization method for switching models using EM algorithm

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NONLINEAR DYNAMICS 2023年第10期111卷 9361-9375页

作者： Chen, Jing Mao, Yawen Hu, Manfeng Guo, Liuxiao Zhu, Quanmin Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China Univ West England Dept Engn Design & Math Bristol BS16 1QY England

This study proposes a decomposition optimization-based expectation maximization algorithm for switching models. The identities of each sub-model are estimated in the expectation step, while the parameters are updated using the decomposition optimization method in the maximization step. Compared with the traditional expectation maximization algorithm and the gradient descent expectation maximization algorithm, the decomposition optimization-based expectation maximization algorithm avoids the matrix inversion and eigenvalue calculation;thus, it can be extended to complex nonlinear models and large-scale models. Convergence analysis and simulation examples are given to show the effectiveness of the proposed algorithm.

关键词： Parameter estimation Switching model expectation maximization algorithm Decomposition optimization method Least squares algorithm

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Semi-supervised learning via manifold regularization

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The Journal of China Universities of Posts and Telecommunications 2012年第6期19卷 79-88页

作者： MAO Yu ZHOU Yan-quan LI Rui-fan WANG Xiao-jie ZHONG Yi-xin School of Telecommunication Engineering Beijing University of Posts and Telecommunications

This paper proposes a novel graph-based transductive learning algorithm based on manifold regularization. First, the manifold regularization was introduced to probabilistic discriminant model for semi-supervised classification task. And then a variation of the expectation maximization （EM） algorithm was derived to solve the optimization problem, which leads to an iterative algorithm. Although our method is developed in probabilistic framework, there is no need to make assumption about the specific form of data distribution. Besides, the crucial updating formula has closed form. This method was evaluated for text categorization on two standard datasets, 20 news group and Reuters-21578. Experiments show that our approach outperforms the state-of-the-art graph-based transductive learning methods.

关键词： manifold regularization semi-supervised learning transductive learning expectation maximization algorithm classification text categorization

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Statistical Trilateration With Skew-t Distributed Errors in LTE Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2016年第10期15卷 7114-7127页

作者： Muller, Philipp del Peral-Rosado, Jose A. Piche, Robert Seco-Granados, Gonzalo Tampere Univ Technol Dept Automat Sci & Engn FIN-33101 Tampere Finland Univ Autonoma Barcelona Dept Telecommun & Syst Engn Cerdanyola Del Valles 08193 Spain

Localization accuracy of trilateration methods in long term evolution (LTE) cellular networks, which are based on time-of-arrival, may be highly degraded due to multipath and non-line of sight conditions in urban and indoor environments. Multipath mitigation techniques usually involve a high computational burden and require wideband signals to be effective, which limit their adoption in certain low-cost and low-power mobile applications using narrow-band signals. As an alternative to these conventional techniques, this paper analyzes an expectation maximization (EM) localization algorithm that considers the skewness introduced by multipath in the LTE ranging error distribution. The EM algorithm is extensively studied with realistic emulated LTE signals of 1.4-MHz bandwidth. The EM method is compared with a standard nonlinear least squares (NLS) algorithm under ideal simulated conditions and using realistic outdoor measurements from a laboratory testbed. The EM method outperforms the NLS method when the ranging errors in the training and test stages have similar distributions.

关键词： Statistical trilateration skew-t distribution localization LTE expectation maximization algorithm

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Optimal planning of step-stress accelerated degradation test based on Tweedie exponential dispersion process with random effects

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第6期40卷 2976-3000页

作者： He, Qian Yan, Weian Liu, Weidong Bigaud, David Xu, Xiaofan Lei, Zitong East China Jiaotong Univ Sch Transportat Engn Nanchang Jiangxi Peoples R China Nanchang Univ Sch Adv Mfg Nanchang Jiangxi Peoples R China Univ Angers LARIS SFR MATHST Angers France

The step stress accelerated degradation test (SSADT) is an effective tool for assessing the reliability of highly reliable products. However, conducting an SSADT is expensive and time consuming, and the obtained SSADT data has an impact on the accuracy of the subsequent product reliability index estimations. Consequently, devising a cost-constrained SSADT plan that yields high-precision reliability estimates poses a significant challenge. This paper focuses on the optimal design of SSADT for the Tweedie exponential dispersion process with random effect (TEDR), a general degradation model capable of describing product heterogeneity. Under given budget and boundary constraints, the optimal sample size, observation frequency and observation times at each stress level are obtained by minimizing the asymptotic variance of the estimated quantile life at normal operating conditions. The sensitivity and stability of the SSADT plan are also studied, and the results indicate the robustness of the optimal plan against slight parameters fluctuations. We use the expectation maximization (EM) algorithm to estimate TEDR parameters and reliability indicators under SSADT, providing a systematic method for obtaining the optimal SSADT plan under budget constraints. The proposed framework is illustrated using the case of LED chips data, showcasing its potential for practical application.

关键词： expectation maximization algorithm optimal design random effects step stress accelerated degradation test Tweedie exponential dispersion process

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Online auto-calibration of triaxial accelerometer with time-variant model structures

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SENSORS AND ACTUATORS A-PHYSICAL 2017年 266卷 294-307页

作者： Ye, L. Argha, A. Celler, B. G. Nguyen, H. T. Su, S. W. Univ Technol Fac Engn & Informat Technol Sydney NSW Australia Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia

In this paper, an online auto-calibration method for MicroElectroMechanical Systems (MEMS) triaxial accelerometer (TA) is proposed, which can simultaneously identify the time-dependent model structure and its parameters during the changes of the operating environment. Firstly, the model as well as its associated cost function is linearized by a new proposed linearization approach. Then, exploiting an online sparse recursive least square (SPARLS) estimation, the unknown parameters are identified. In particular, the online sparse recursive method is based on an L-1-norm penalized expectation-maximum (EM) algorithm, which can amend the model automatically by penalizing the insignificant parameters to zero. Furthermore, this method can reduce computational complexity and be implemented in a low-cost Micro-Controller-Unit (MCU). Based on the numerical analysis, it can be concluded that the proposed recursive algorithm can calculate the unknown parameters reliably and accurately for most MEMS triaxial accelerometers available in the market. Additionally, this method is experimentally validated by comparing the output estimations before and after calibration under various scenarios, which further confirms its feasibility and effectiveness for online TA calibration. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Parameter estimation Tri-axial accelerometer Online calibration Model linearization expectation maximization algorithm

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Identification of Hammerstein-Wiener models

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AUTOMATICA 2013年第1期49卷 70-81页

作者： Wills, Adrian Schon, Thomas B. Ljung, Lennart Ninness, Brett Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia Linkoping Univ Div Automat Control SE-58183 Linkoping Sweden

This paper develops and illustrates a new maximum-likelihood based method for the identification of Hammerstein-Wiener model structures. A central aspect is that a very general situation is considered wherein multivariable data, non-invertible Hammerstein and Wiener nonlinearities, and colored stochastic disturbances both before and after the Wiener nonlinearity are all catered for. The method developed here addresses the blind Wiener estimation problem as a special case. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： System identification Hammerstein Wiener Block-oriented models Nonlinear models Dynamic systems Monte Carlo method Smoothing expectation maximization algorithm Particle methods Maximum Likelihood

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Pricing fraud detection in online shopping malls using a finite mixture model

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ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 2013年第3期12卷 195-207页

作者： Kim, Kwanho Choi, Yerim Park, Jonghun Seoul Natl Univ Dept Ind Engn Seoul 151744 South Korea

Although pricing fraud is an important issue for improving service quality of online shopping malls, research on automatic fraud detection has been limited. In this paper, we propose an unsupervised learning method based on a finite mixture model to identify pricing frauds. We consider two states, normal and fraud, for each item according to whether an item description is relevant to its price by utilizing the known number of item clusters. Two states of an observed item are modeled as hidden variables, and the proposed models estimate the state by using an expectation maximization (EM) algorithm. Subsequently, we suggest a special case of the proposed model, which is applicable when the number of item clusters is unknown. The experiment results show that the proposed models are more effective in identifying pricing frauds than the existing outlier detection methods. Furthermore, it is presented that utilizing the number of clusters is helpful in facilitating the improvement of pricing fraud detection performances. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Pricing fraud Fraud detection Online shopping e-Commerce expectation maximization algorithm Finite mixture model

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Estimation of 1-bit quantized time-series with Markov regime

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SIGNAL PROCESSING 1997年第3期58卷 273-292页

作者： Logothetis, A Krishnamurthy, V Poor, HV PRINCETON UNIV DEPT ELECT ENGNPRINCETONNJ 08544

In this paper we consider de-interleaving a finite number of stochastic parametric sources. The sources are modeled as independent autoregressive (AR) processes. Based on a Markovian switching policy, we assume that the different sources transmit signals on the same single channel, The receiver records the 1-bit quantized version of the transmitted signal and aims to identify the sequence of active sources. Once the source sequence has been identified, the characteristics (parameters) of each source are estimated. We formulate the parametric pulse train de-interleaving problem as a 1-bit quantized Markov modulated AR series, The algorithm proposed in this paper combines Hidden Markov Model (HMM) and Binary Time Series (BTS) estimation techniques. Our estimation scheme generalizes Kedem's (1980) binary time series algorithm for linear time series to Markov modulated time series. (C) 1997 Elsevier Science B.V.

关键词： expectation maximization algorithm autoregressive process hidden Markov models parameter estimation binary time series

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