Travel Demand and Traffic Prediction with Cell Phone Data: Calibration by Mathematical Program with Equilibrium Constraints

2020 ◽  
Author(s):  
Ronan Doorley ◽  
Luis Alonso ◽  
Atnaud Grignard ◽  
Nuria Macia ◽  
Kent Larson
Keyword(s):  
Cell Phone ◽  
Travel Demand ◽  
Mathematical Program ◽  
Traffic Prediction ◽  
Equilibrium Constraints ◽  
Data Calibration

Capturing Electricity Market Dynamics in the Optimal Trading of Strategic Agents using Neural Network Constrained Optimization

2021 ◽  
Author(s):  
Mihály Dolányi ◽  
Kenneth Bruninx ◽  
Jean-François Toubeau ◽  
Erik Delarue
Keyword(s):  
Neural Network ◽  
Electricity Markets ◽  
Electricity Market ◽  
Mathematical Program ◽  
Market Dynamics ◽  
Equilibrium Constraints ◽  
Market Outcomes ◽  
Optimal Trading ◽  
Optimal Bidding ◽  
Upper Level

In competitive electricity markets the optimal trading problem of an electricity market agent is commonly formulated as a bi-level program, and solved as mathematical program with equilibrium constraints (MPEC). In this paper, an alternative paradigm, labeled as mathematical program with neural network constraint (MPNNC), is developed to incorporate complex market dynamics in the optimal bidding strategy. This method uses input-convex neural networks (ICNNs) to represent the mapping between the upper-level (agent) decisions and the lower-level (market) outcomes, i.e., to replace the lower-level problem by a neural network. In a comparative analysis, the optimal bidding problem of a load agent is formulated via the proposed MPNNC and via the classical bi-level programming method, and compared against each other.

scholarly journals Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping

2020 ◽  
Vol 12 (4) ◽  
pp. 704 ◽  
Author(s):  
Xiangyang Kong ◽  
Yongqiang Zhao ◽  
Jize Xue ◽  
Jonathan Cheung-Wai Chan ◽  
Seong G. Kong
Keyword(s):  
Convex Function ◽  
Hyperspectral Image ◽  
Mathematical Program ◽  
Sparse Approximation ◽  
Hyperspectral Images ◽  
Equilibrium Constraints ◽  
Spectral Distortion ◽  
Spectral Correlation ◽  
Evaluation Measures ◽  
Global And Local

This paper presents a global and local tensor sparse approximation (GLTSA) model for removing the stripes in hyperspectral images (HSIs). HSIs can easily be degraded by unwanted stripes. Two intrinsic characteristics of the stripes are (1) global sparse distribution and (2) local smoothness along the stripe direction. Stripe-free hyperspectral images are smooth in spatial domain, with strong spectral correlation. Existing destriping approaches often do not fully investigate such intrinsic characteristics of the stripes in spatial and spectral domains simultaneously. Those methods may generate new artifacts in extreme areas, causing spectral distortion. The proposed GLTSA model applies two ℓ 0 -norm regularizers to the stripe components and along-stripe gradient to improve the destriping performance. Two ℓ 1 -norm regularizers are applied to the gradients of clean image in spatial and spectral domains. The double non-convex functions in GLTSA are converted to single non-convex function by mathematical program with equilibrium constraints (MPEC). Experiment results demonstrate that GLTSA is effective and outperforms existing competitive matrix-based and tensor-based destriping methods in visual, as well as quantitative, evaluation measures.

Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal

2016 ◽  
Vol 17 (9) ◽  
pp. 2466-2478 ◽  
Author(s):  
Merkebe Getachew Demissie ◽  
Santi Phithakkitnukoon ◽  
Titipat Sukhvibul ◽  
Francisco Antunes ◽  
Rui Gomes ◽  
...  
Keyword(s):  
Developing Countries ◽  
Cell Phone ◽  
Travel Demand ◽  
Urban Mobility ◽  
Passenger Travel

Estimating Dynamic Origin-Destination Data and Travel Demand Using Cell Phone Network Data

2013 ◽  
Vol 11 (2) ◽  
pp. 76-86 ◽  
Author(s):  
Ming-Heng Wang ◽  
Steven D. Schrock ◽  
Nate Vander Broek ◽  
Thomas Mulinazzi
Keyword(s):  
Cell Phone ◽  
Travel Demand ◽  
Network Data
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