Replaceable Encoding for Basic Variable of Traffic Network Transportation Optimization Problem

2013 ◽  
pp. 45-53
Author(s):  
Kang Zhou ◽  
Yule Zhu ◽  
Feng Jin ◽  
Xin Tao
Keyword(s):  
Optimization Problem ◽  
Traffic Network ◽  
Basic Variable ◽  
Network Transportation ◽  
Transportation Optimization

scholarly journals Optimal Control Design for Traffic Flow Maximization Based on Data-Driven Modeling Method

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 187
Author(s):  
Balázs Németh ◽  
Dániel Fényes ◽  
Zsuzsanna Bede ◽  
Péter Gáspár
Keyword(s):  
Traffic Flow ◽  
Optimization Problem ◽  
Control Design ◽  
Prediction Method ◽  
Data Driven ◽  
Traffic Network ◽  
Control Methods ◽  
Prediction And Control ◽  
Data Driven Modeling ◽  
And Control

This paper proposes enhanced prediction and control design methods for improving traffic flow with human-driven and automated vehicles. To achieve accurate prediction for the entire time horizon, data-driven and model-based prediction methods were integrated. The goal of the integration was to accurately predict the outflow of the traffic network, which was selected as the highway section in this paper. The proposed novel prediction method was used in the optimal design for calculating controlled inflows on highway ramps. The goal of the design was to reach the maximum outflow of the traffic network, even against disturbances on uncontrolled inflows of the network. The control design leads to an optimization problem based on the min–max principle, i.e., the traffic outflow is considered to be maximized by controlled inflows and to be minimized by uncontrolled inflows. The effectiveness of the prediction and the control methods through simulation examples are illustrated, i.e., traffic outflow can be maximized by the control system under various uncontrolled inflow values.

Optimal Lane Management in Heterogeneous Traffic Network

2019 ◽  
Author(s):  
Pouria Karimi Shahri ◽  
Shubhankar Chintamani Shindgikar ◽  
Baisravan HomChaudhuri ◽  
Amir H. Ghasemi
Keyword(s):  
Autonomous Vehicles ◽  
Optimization Problem ◽  
Optimal Allocation ◽  
Penetration Rate ◽  
Heterogeneous Traffic ◽  
Design Parameters ◽  
Optimization Approach ◽  
Traffic Network ◽  
The Road ◽  
Search Optimization

Abstract This paper aims to determine an optimal allocation of autonomous vehicles in a multi-lane heterogeneous traffic network where the road is shared between autonomous and human-driven vehicles. The fundamental traffic diagram for such heterogeneous traffic networks is developed wherein the capacity of the road is determined as a function of the penetration rate and the headways of autonomous and human-driven vehicles. In this paper, we define two cost functions to maximize the throughput of the network and minimize the variation between flow rates. To solve the proposed optimization problem, an exhaustive search optimization approach is performed. Several numerical examples are presented to highlight the different influence of different design parameters on the allocation of autonomous vehicles.

Exergy and sensibility analysis of each individual effect in a kraft multiple effect evaporator

TAPPI Journal ◽  
2019 ◽  
Vol 18 (10) ◽  
pp. 607-618
Author(s):  
JÉSSICA MOREIRA ◽  
BRUNO LACERDA DE OLIVEIRA CAMPOS ◽  
ESLY FERREIRA DA COSTA JUNIOR ◽  
ANDRÉA OLIVEIRA SOUZA DA COSTA
Keyword(s):  
Optimization Problem ◽  
Real Data ◽  
Kraft Pulping ◽  
Steam Temperature ◽  
Input Flow ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients ◽  
Multiple Effect ◽  
Sensibility Analysis ◽  
Exergetic Analysis

The multiple effect evaporator (MEE) is an energy intensive step in the kraft pulping process. The exergetic analysis can be useful for locating irreversibilities in the process and pointing out which equipment is less efficient, and it could also be the object of optimization studies. In the present work, each evaporator of a real kraft system has been individually described using mass balance and thermodynamics principles (the first and the second laws). Real data from a kraft MEE were collected from a Brazilian plant and were used for the estimation of heat transfer coefficients in a nonlinear optimization problem, as well as for the validation of the model. An exergetic analysis was made for each effect individually, which resulted in effects 1A and 1B being the least efficient, and therefore having the greatest potential for improvement. A sensibility analysis was also performed, showing that steam temperature and liquor input flow rate are sensible parameters.

Imaging through Scattering Media with a Learning Based Prior

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt
Keyword(s):  
Optimization Problem ◽  
Autonomous Driving ◽  
Scattering Media ◽  
Photon Scattering ◽  
Point Spread ◽  
Spread Function ◽  
Radiative Transport Equation ◽  
Spatio Temporal ◽  
Transient Imaging

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

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