Collision Risk Modeling and Analysis for Lateral Separation to Support Unmanned Traffic Management

Risk Analysis ◽  
2021 ◽  
Author(s):  
Valtteri Kallinen ◽  
Aaron McFadyen
Keyword(s):  
Traffic Management ◽  
Risk Modeling ◽  
Collision Risk ◽  
Lateral Separation ◽  
Modeling And Analysis

Dynamic collision risk modeling under uncertainty

Robotica ◽  
2012 ◽  
Vol 31 (4) ◽  
pp. 525-537 ◽  
Author(s):  
F. Belkhouche ◽  
B. Bendjilali
Keyword(s):  
Configuration Space ◽  
Collision Detection ◽  
Sensory System ◽  
Dynamic Environment ◽  
Rate Of Change ◽  
Risk Modeling ◽  
Collision Risk ◽  
Moving Vehicles ◽  
Probability Of Collision ◽  
Geometric Variables

SUMMARYThis paper introduces a probabilistic model for collision risk assessment between moving vehicles. The uncertainties in the states and the geometric variables obtained from the sensory system are characterized by probability density functions. Given the states and their uncertainties, the goal is to determine the probability of collision in a dynamic environment. Two approaches are discussed: (1) The virtual configuration space (VCS), and (2) the rates of change of the visibility angles. The VCS is a transformation of observer that reduces collision detection with a moving object to collision detection with a stationary object. This approach allows to create simple geometric collision cones. Error propagation models are used to solve the problem when going from the VCS to the configuration space. The second approach derives the collision conditions in terms of the rate of change of the limit visibility angles. The probability of collision is then calculated. A comparison between the two methods is carried out. Results are illustrated using simulation, including Monte Carlo simulation.

scholarly journals Modeling and Robustness Study of Railway Transport Networks Using P-Timed Petri Nets

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Anis Mhalla ◽  
Mohanned Gaied
Keyword(s):  
Petri Nets ◽  
Traffic Management ◽  
Control Strategies ◽  
Discrete Event ◽  
Transport Systems ◽  
Population Mobility ◽  
Railway Transport ◽  
Transport Networks ◽  
Modeling And Analysis ◽  
Increasing Demand

The importance of public transport systems continues to grow. These systems must respond to an increasing demand for population mobility and traffic disturbances. Rail transport networks can be considered as Discrete Event Systems (DES) with time constraints. The time factor is a critical parameter, since it includes dates to be respected in order to avoid overlaps, delays, and collisions between trains. P-time Petri Nets have been recognized as powerful modeling and analysis tools for railway transport systems. Temporal disturbances in these systems include railway infrastructure, traffic management, and disturbances (weather, obstacles on the tracks, malice, social movement, etc.). The developments presented in this paper are devoted to the modeling and the study of the robustness of the railway transport systems in order to evaluate the stability and the efficiency of these networks. In this study two robust control strategies towards time disturbances are presented. The first one consists of compensating the disturbance as soon as it is observed in order to avoid constraints violation. The second one allows generating, by the control, a temporal lag identical to the disturbance in order to avoid the death of marks on the levels of synchronization transitions of the P-time Petri net model.

Quantitative modeling and analysis of supply chain risks using Bayesian theory

2014 ◽  
Vol 25 (5) ◽  
pp. 631-654 ◽  
Author(s):  
Fazleena Badurdeen ◽  
Mohannad Shuaib ◽  
Ken Wijekoon ◽  
Adam Brown ◽  
William Faulkner ◽  
...  
Keyword(s):  
Risk Management ◽  
Management Practices ◽  
Back Propagation ◽  
Bayesian Belief Networks ◽  
Risk Identification ◽  
Bayesian Theory ◽  
Risk Modeling ◽  
Content Type ◽  
Modeling And Analysis ◽  
Qualitative Approaches

Purpose – Globally expanding supply chains (SCs) have grown in complexity increasing the nature and magnitude of risks companies are exposed to. Effective methods to identify, model and analyze these risks are needed. Risk events often influence each other and rarely act independently. The SC risk management practices currently used are mostly qualitative in nature and are unable to fully capture this interdependent influence of risks. The purpose of this paper is to present a methodology and tool developed for multi-tier SC risk modeling and analysis. Design/methodology/approach – SC risk taxonomy is developed to identify and document all potential risks in SCs and a risk network map that captures the interdependencies between risks is presented. A Bayesian Theory-based approach, that is capable of analyzing the conditional relationships between events, is used to develop the methodology to assess the influence of risks on SC performance Findings – Application of the methodology to an industry case study for validation reveals the usefulness of the Bayesian Theory-based approach and the tool developed. Back propagation to identify root causes and sensitivity of risk events in multi-tier SCs is discussed. Practical implications – SC risk management has grown in significance over the past decade. However, the methods used to model and analyze these risks by practitioners is still limited to basic qualitative approaches that cannot account for the interdependent effect of risk events. The method presented in this paper and the tool developed demonstrates the potential of using Bayesian Belief Networks to comprehensively model and study the effects or SC risks. The taxonomy presented will also be very useful for managers as a reference guide to begin risk identification. Originality/value – The taxonomy developed presents a comprehensive compilation of SC risks at organizational, industry, and external levels. A generic, customizable software tool developed to apply the Bayesian approach permits capturing risks and the influence of their interdependence to quantitatively model and analyze SC risks, which is lacking.

Collision Risk Modeling in the Northern Pacific Airspace under Separation Reduction and Improvements in Navigational Performance

2006 ◽  
Vol 14 (4) ◽  
pp. 257-282 ◽  
Author(s):  
Theresa Brewer-Dougherty ◽  
Brian Colamosca ◽  
Christine Gerhardt-Falk ◽  
Dale Livingston ◽  
Lauren Martin ◽  
...  
Keyword(s):  
Risk Modeling ◽  
Collision Risk ◽  
Northern Pacific

Collision risk modeling of air traffic

2003 ◽  
Author(s):  
Henk Blom ◽  
Bert Bakker ◽  
Mariken Everdij ◽  
Marco van der Park
Keyword(s):  
Air Traffic ◽  
Risk Modeling ◽  
Collision Risk

Future Air Traffic Management: Quantitative En Route Safety Assessment Part 2 – New Approaches

2002 ◽  
Vol 55 (3) ◽  
pp. 363-379 ◽  
Author(s):  
Peter Brooker
Keyword(s):  
Traffic Management ◽  
Safety Assessment ◽  
Hazard Analysis ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Abstract Concepts ◽  
Collision Risk ◽  
Route Safety ◽  
The Right ◽  
Quantitative Safety Assessment

This is the second of two papers on Quantitative Safety Assessment – vital to the successful introduction of future Air Traffic Management systems. The focus is en route European controlled commercial traffic, particularly the mid-air collision risk. Part 2 develops soundly based and practical methods for safety assessment. The objective is to determine the key questions and the best ways to answer them. Aspects covered include lessons from Hazard Analysis and Airproxes together with ‘realistic’ risk budgeting. Two abstract concepts are introduced: Position Integrity and Reasonable Intent (essentially the need to be on the ‘right’ flight path), and their implications for risk calculations are discussed.

scholarly journals Agent-Based Modelling for Distributed Decision Support in an IoT Network

2020 ◽  
Author(s):  
Majid Butt ◽  
Indrakshi Dey ◽  
Merim Dzaferagic ◽  
Maria Murphy ◽  
Nicholas Kaminski ◽  
...  
Keyword(s):  
Traffic Management ◽  
Autonomous Agents ◽  
Road Traffic ◽  
Mac Protocol ◽  
Spectrum Utilization ◽  
Medium Access ◽  
Modeling And Analysis ◽  
Agent Based ◽  
Traffic Management System ◽  
High Level

<div>An increasing number of emerging applications, e.g.,</div><div>Internet of Things (IoT), vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-Based Modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to</div><div>model the communication aspects of a road traffic management system as an example of an IoT network. We model, analyze and compare various Medium Access Control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high level MAC protocol by using three different approaches: Centralized Decision Maker, DESYNC and decentralized learning MAC (L-MAC). The results clearly</div><div>show the importance of coordination between multiple decision makers in order to improve the information reporting error and spectrum utilization of the system.</div>

System Risk Importance Analysis Using Bayesian Networks

2018 ◽  
Vol 25 (01) ◽  
pp. 1850004 ◽  
Author(s):  
Ali Noroozian ◽  
Reza Baradaran Kazemzadeh ◽  
Seyed Taghi Akhavan Niaki ◽  
Enrico Zio
Keyword(s):  
Decision Making ◽  
Bayesian Networks ◽  
Structural Characteristics ◽  
Informed Decision ◽  
Informed Decision Making ◽  
Risk Modeling ◽  
Modeling And Analysis ◽  
Importance Analysis ◽  
System Risk

Importance measures (IMs) are used for risk-informed decision making in system operations, safety, and maintenance. Traditionally, they are computed within fault tree (FT) analysis. Although FT analysis is a powerful tool to study the reliability and structural characteristics of systems, Bayesian networks (BNs) have shown explicit advantages in modeling and analytical capabilities. In this paper, the traditional definitions of IMs are extended to BNs in order to have more capability in terms of system risk modeling and analysis. Implementation results on a case study illustrate the capability of finding the most important components in a system.

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