2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
257
TA68
Mockingbird 4- Omni
Graph Analytics for Complex Systems
Sponsored: Quality, Statistics and Reliability
Sponsored Session
Chair: Hoang Tran, Texas A&M University, College Station, TX,
United States,
Co-Chair: Satish Bukkapatnam, Texas A&M University, College Station,
TX, United States,
1 - Predicting Community Structure In Dynamic Networks: A Case Of
Online Educational Networks
Yi-Shan Sung, Penn State University, ,
Soundar Kumara
Community structure points to structural patterns in a network and reflects
functional associations between entities. However, it is challenging to obtain
timely updates of communities in a dynamic network in which changes are
frequently introduced over time. We develop a model to predict community
structure by integrating link prediction with community detection algorithms. We
test the model efficacy using the data from , which is an online
educational platform for science and engineering in nanotechnology. Predicting
community structure in nanoHUB networks will help in developing an efficient
recommendation system for the nanoHUB users and optimizing the resource
allocation.
2 - Detecting Changes In Complex Systems Via Network Inference
Hoang M Tran, Texas A&M University, College Station, TX,
United States, , Satish Bukkapatnam
We propose a network based method to do change detection in transient complex
systems. This is based on our approach to infer spurious-link-free network
structures from time series. A spectral graph based method is used to detect
process changes from these networks.
3 - Graph Reconstruction From High-dimensional Systems Of
Additive Differential Equations
Ali Shojaie, University of Washington, Seattle, WA, United States,
Shizhe Chen, Daniela Witten
We consider the task of learning a dynamical system from high-dimensional time-
course data. We model the dynamical system non-parametrically as a system of
additive ordinary differential equations. Most existing methods for parameter
estimation in ordinary differential equations estimate the derivatives from noisy
observations. This is known to be challenging and inefficient. We propose a novel
approach that does not involve derivative estimation. We show that the proposed
method can consistently recover the true network structure even in high
dimensions, and we demonstrate empirical improvement over competing
approaches.
4 - Modeling And Change Detection Of Dynamic Network Data By A
Network State Space Model
Na Zou, Texas A&M University, College Station, TX, 77845,
United States, Jing Li
Dynamic network data widely exist in social, biological, and engineering domains.
There are two types of variability in dynamic network data: variability of natural
evolution and variability due to assignable causes. Accurate and timely change
detection from dynamic network data is important. Change detection is a classic
research area in Statistical Process Control (SPC) and various approaches have
been developed for dynamic data in the form of univariate or multivariate time
series, but not in the form of networks. We propose a Network State Space Model
(NSSM) to characterize the natural evolution of dynamic networks and integrate
the NSSM with SPC for change detection.
TA69
Old Hickory- Omni
Economics IV
Contributed Session
Chair: Fouad El Ouardighi, Professor, ESSEC Business School, Avenue
B Hirsch BP 105, Cergy Pontoise, 95021, France,
1 - A Note On Real Estate Pricing With Exogenous Variables
Hiroshi Ishijima, Professor, Chuo University,
1-18 Ichigaya-tamachi Shinjuku, Tokyo, 1628478, Japan,
Akira Maeda
We develop a pricing model of real estate that incorporates conventional hedonic
attribute variables of real estate as well as exogenous variables, namely financial
asset prices; this model is based on a theoretical pricing model that we,
fundamentally develop. Specifically, our model features a pricing kernel expressed
as the product of a cash-flow pricing kernel (stochastic discount factor) and a
hedonic pricing kernel. Furthermore, we conduct an empirical analysis to
understand Japanese real estate prices comprehensively. Our analysis reveals that
the financial asset prices and conventional hedonic variables serve as the major
determinants of Japanese real estate prices.
2 - Ensemble Model For U. S. Stock Major Index Prediction Using
Economic Factors With Interactive Visualization
Yao-Te Tsai, Post-Doctoral Fellow, Auburn University, Auburn, AL,
36849, United States, Bin Weng, Fadel
Megahed, James Barth
The accuracy of the stock market prediction has been an attractive topic for
researchers and public. However, it has still been remaining one of the most
challenging tasks due to the non-linearity and non-stationary of the time series
data. Our objective is to discover information and trends from macroeconomic
perspectives to provide a foundation for the future stock market predictive model
development. we investigate how macroeconomic factors that drive the U.S.
major stock market index by applying the ensemble model. We also determine if
the index of each sector would be driven from different factors. The last task is to
predict the stock market index based on our variable selection.
3 - Capital Growth With Recycling And The Environmental
Kuznets Curve
Fouad El Ouardighi, Professor, ESSEC Business School, Avenue B
Hirsch BP 105, Cergy Pontoise, 95021, France,
We investigate how the relationship between capital growth and pollution
accumulation is affected by the source of pollution, that is, either production or
consumption. We are interested in polluting waste that cannot be naturally
absorbed, but for which recycling efforts are made to avoid massive accumulation
with harmful consequences in the long run. We distinguish the cases where
recycling efforts are capital-improving or capital-neutral. Based on both
environmental and social welfare perspectives, we determine how the influence
of the pollution source on capital growth and polluting waste accumulation is
affected by the fact that recycling is capital-improving or capital-neutral.
TA70
Acoustic- Omni
Transportation, Rail II
Contributed Session
Chair: Yalda Khashe, University of Southern California, 3230 Overland
Ave. APT 312, Los Angeles, CA, 90034, United States,
1 - Train Timetable Based Integer Programming Model For Passenger
Assignment Problem In Congest Urban Rail Line
Si Ma, Associate Professor, Southwest Jiaotong University,
Chengdu, China, , Gongyuan Lu,
Lin Wang
We optimized passenger assignment problem (PAP) considering passenger waiting
time, platform and car capacity to maximize transportation capacity in congest
urban rail line. Using passenger_flow-Train_path network to integrate passenger
behavior and timetable based train movement in space and time dimension, the
PAP is modeled as a maximum flow problem with multi-sources and multi-sinks.
In the real-world case of Chengdu urban rail line 1, the integer programming
model is solved efficiently by commercial solver.
2 - Face Recognition Based Ticket Checking Solution In
Speeding Train
Kui Yang, PH.D. Candidate, Southwest Jiaotong University,
Chendu, 610031, China, Gongyuan Lu,
Haifeng Yan
It is a big challenge to check rail ticket in almost every passenger section, due to
great workload and passenger inconvenience. Integrated with ticket section, a
face recognition based ticket checking solution is presented for strict and efficient
checking. This visual-aided solution can automatically identify checking candidate
in different section, avoid missing or multiple checking in the whole journey.
3 - Critical Systems Management Issues Of Implementing The
Positive Train Control Technology In A Regional Railroad
Yalda Khashe, University of Southern California, 3230 Overland
Ave. Apt 312, Los Angeles, CA, 90034, United States,
Positive Train Control (PTC) is a generic term referring to a range of fully
integrated technologies that overlay existing safety systems to prevent train-to-
train collision and improve worker safety. One of the challenges that railroad
industry is facing for implementing PTC is the complications of introducing this
new technology to an already existing system and its effect on the technological,
organizational and human subsystems and their interactions.
TA70