Real-Time Public Transport Operations: Library of Control Strategies

2017 ◽  
Vol 2647 (1) ◽  
pp. 26-32 ◽  
Author(s):  
Mahmood Mahmoodi Nesheli ◽  
Avishai (Avi) Ceder
Keyword(s):  
Real Time ◽  
Public Transport ◽  
Large Scale ◽  
Real Life ◽  
Small Scale ◽  
Control Procedure ◽  
Service Reliability ◽  
Real Time Control ◽  
Time Control ◽  
Control Actions

Modern public transport (PT) operations have evolved into a complex multimodal system in which small-scale disorder can propagate. Large-scale disruptions to passengers and PT agencies result. Various studies have been developed to model PT control at the operational level; however, the main downside of possible real-time control actions is the lack of intelligent modeling and a systematic process that can activate such actions immediately. This study presents a real-time control procedure to increase service reliability and to improve successful coordinated transfers in a complex PT system. The developed method aims at minimizing total travel time for passengers and reducing the uncertainty of meetings between PT vehicles. A library of operational tactics is first built to serve as a basis of the real-time decision-making process. The methodology developed is applied to a real-life case study in Auckland, New Zealand. The results showed improvements in system performance and confirmed the use of real-time control actions to maintain reliable PT service.

A real-time control method-based simulation for high-speed trains on large-scale rail network

2017 ◽  
Vol 28 (10) ◽  
pp. 1750126 ◽  
Author(s):  
Yutong Liu ◽  
Chengxuan Cao ◽  
Yaling Zhou ◽  
Ziyan Feng
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
High Speed ◽  
Large Scale ◽  
Simulation Method ◽  
Real Time Control ◽  
Rail Network ◽  
Time Control ◽  
High Speed Trains ◽  
Rail Traffic

In this paper, an improved real-time control model based on the discrete-time method is constructed to control and simulate the movement of high-speed trains on large-scale rail network. The constraints of acceleration and deceleration are introduced in this model, and a more reasonable definition of the minimal headway is also presented. Considering the complicated rail traffic environment in practice, we propose a set of sound operational strategies to excellently control traffic flow on rail network under various conditions. Several simulation experiments with different parameter combinations are conducted to verify the effectiveness of the control simulation method. The experimental results are similar to realistic environment and some characteristics of rail traffic flow are also investigated, especially the impact of stochastic disturbances and the minimal headway on the rail traffic flow on large-scale rail network, which can better assist dispatchers in analysis and decision-making. Meanwhile, experimental results also demonstrate that the proposed control simulation method can be in real-time control of traffic flow for high-speed trains not only on the simple rail line, but also on the complicated large-scale network such as China’s high-speed rail network and serve as a tool of simulating the traffic flow on large-scale rail network to study the characteristics of rail traffic flow.

Real Time Control of a Multiple Arm System by Large Scale Multiprocessing

1992 ◽  
Vol 25 (20) ◽  
pp. 37-42
Author(s):  
Tasuku Hoshino ◽  
Katsuhisa Furuta
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Real Time Control ◽  
Time Control

scholarly journals The open real-time control (RTC)-Tools software framework for modeling RTC in water resources sytems

2014 ◽  
Vol 17 (1) ◽  
pp. 130-148 ◽  
Author(s):  
D. Schwanenberg ◽  
B. P. J. Becker ◽  
M. Xu
Keyword(s):  
Decision Support ◽  
Water Resources ◽  
Feedback Control ◽  
Real Time ◽  
Large Scale ◽  
Water Systems ◽  
Software Framework ◽  
River Rhine ◽  
Real Time Control ◽  
Time Control

Real-time control-Tools is a novel software framework for modeling real-time control and decision support in water resources systems. It integrates different control paradigms ranging from simple feedback control strategies with triggers, operating rules and controllers to advanced optimization-based approaches such as model predictive control (MPC). A key feature of the package is the modular integration of modeling components, related adjoint models, and optimization algorithms which makes it well suited for the control of large-scale water systems. Interfaces enable its integration into Supervisory Control and Data Acquisition systems, operational stream flow forecasting, and decision support systems as well as hydraulic modeling packages. This paper presents an overview of the novel software framework, gives an introduction into the underlying control theory for which it has been developed and discusses the related software architecture. A first case describes an innovative combination of binary decision trees and feedback control in application to the modeling of a highly regulated River Rhine reach along the German–French border. Two additional cases present the efficient application of MPC to the short-term management of two large-scale water systems in the Netherlands and the USA.

scholarly journals Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection

2017 ◽  
Vol 37 (4-5) ◽  
pp. 421-436 ◽  
Author(s):  
Sergey Levine ◽  
Peter Pastor ◽  
Alex Krizhevsky ◽  
Julian Ibarz ◽  
Deirdre Quillen
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Spatial Relationship ◽  
Robotic Platform ◽  
Robotic Grasping ◽  
Real Time Control ◽  
Time Control ◽  
Wear And Tear ◽  
Hand Eye Coordination ◽  
Novel Objects

We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images independent of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real time to achieve successful grasps. We describe two large-scale experiments that we conducted on two separate robotic platforms. In the first experiment, about 800,000 grasp attempts were collected over the course of two months, using between 6 and 14 robotic manipulators at any given time, with differences in camera placement and gripper wear and tear. In the second experiment, we used a different robotic platform and 8 robots to collect a dataset consisting of over 900,000 grasp attempts. The second robotic platform was used to test transfer between robots, and the degree to which data from a different set of robots can be used to aid learning. Our experimental results demonstrate that our approach achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing. Our transfer experiment also illustrates that data from different robots can be combined to learn more reliable and effective grasping.

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