Bi-level model predictive control for metro networks: Integration of timetables, passenger flows, and train speed profiles

This paper deals with the train scheduling problem for metro networks taking into account time-dependent passenger origin–destination demands and train speed profiles. The aim is to adjust train schedules online according to time-dependent passenger demands so that passenger satisfaction and operational costs are jointly optimized. An...

Towards establishing an automated selection framework for underwater image enhancement methods

The majority of computer vision architectures are developed based on the assumption of the availability of good quality data. However, this is a particularly hard requirement to achieve in underwater conditions. To address this limitation, plenty of underwater image enhancement methods have received considerable attention during the last...

Benders Decomposition-Based Optimization of Train Departure Frequencies in Metro Networks

Timetables determine the service quality for passengers and the energy consumption of trains in metro systems. In metro networks, a timetable can be made by designing train departure frequencies for different periods of the day, which is typically formulated as a mixed-integer linear programming (MILP) problem. In this paper, we first apply...

Modeling and Efficient Passenger-Oriented Control for Urban Rail Transit Networks

Real-time timetable scheduling is an effective way to improve passenger satisfaction and to reduce operational costs in urban rail transit networks. In this paper, a novel passenger-oriented network model is developed for real-time timetable scheduling that can model time-dependent passenger origin-destination demands with consideration of a...

Scenario-Based MPC for Real-Time Passenger-Centric Timetable Scheduling of Urban Rail Transit Networks

Effective timetable scheduling strategies are essential for passenger satisfaction in urban rail transit networks. Most existing passenger-centric timetable scheduling approaches generate a timetable according to deterministic passenger origin-destination (OD) demands. As passenger OD demands in urban rail transit networks generally show a...

Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes

This paper proposes a method to encourage safety in Model Predictive Control (MPC)-based Reinforcement Learning (RL) via Gaussian Process (GP) regression. The framework consists of 1) a parametric MPC scheme that is employed as model-based controller with approximate knowledge on the real system's dynamics, 2) an episodic RL algorithm tasked...

Deep Learning for Object Detection and Segmentation in Videos: Toward an Integration With Domain Knowledge

Deep learning has enabled the rapid expansion of computer vision tasks from image frames to video segments. This paper focuses on the review of the latest research in the field of computer vision tasks in general and on object localization and identification of their associated pixels in video frames in particular. After performing a systematic...

Timetable Scheduling for Passenger-Centric Urban Rail Networks: Model Predictive Control based on a Novel Absorption Model

Timetable scheduling plays a key role in daily operations of urban rail transit systems, as it determines the quality of service provided to passengers. In order to develop efficient timetable scheduling methods, it is necessary to develop a proper model to integrate timetable-related and passenger-related factors in urban rail network...