Real-time Conflict Research Articles

Real-time conflict prediction: a comparative study of machine learning classifiers

Real-time conflict prediction models (RTConfPM) are an innovative approach to deal with real-time road safety analysis, even in absence of reliable crash data. This paper presents a RTConfPM to predict rear-end conflicts, using Time-To-Collision (TTC) values recorded with radar sensors on multiple motorway cross-sections to define unsafe situations, and traffic conditions recorded on the same sections as inputs to the model. Several classifiers were trained and compared: K-Nearest Neighbors (KNN), Naïve Bayes (NB), Discriminant Analysis (DA), Decision Trees (DT) and Support Vector Machine (SVM). All the proposed models provide better performance than most of crash-based real-time models found in the literature, with KNN and SVM significantly better than the others when considering Recall indicator.

Left-turn conflict identification at signal intersections based on vehicle trajectory reconstruction under real-time communication conditions

Connected vehicle (CV)technologies offer promising solutions to several problems in transportation systems. The trajectory data generated from CV technology can be used to identify real-time conflicts in intersections. To perform such identification, accurate vehicle localisation should be obtained to clearly recognise the conflicts between left-turning vehicles and straight-through vehicles in the opposite direction at the signal control intersection. This study presents a CV framework that uses the two-way time of arrival to locate the vehicles on the basis of the Intelligent Vehicle Infrastructure Cooperative Environment. Kalman Filter (KF) is used to improve the accuracy of the vehicle location, and the corresponding algorithm is used to estimate the vehicle trajectory to obtain the vehicle kinematics information via the on-board system. The traffic conflict areas of the left-turning vehicles and straight-through vehicles in the opposite direction are determined through vehicle trajectory extrapolation, and the left-turn collision at the signal intersection is identified using the post-encroachment time algorithm and vehicle movement information. In addition, Anderson–Darling and modified Kolmogorov–Smirnov tests are performed to verify the goodness of fit of the data. Results show that the vehicle speed and localisation errors of the proposed method decreased by 66.67 % and 83.33 % compared with the results before filtering, respectively. Moreover, the results of the conflict recognition method based on CV trajectory reconstruction is consistent for both goodness of fit tests under real-time communication conditions. This study can provide driving decision for drivers of left-turning vehicles under the Intelligent Vehicle Infrastructure Cooperative Environment and provide technical support for the research and development of left-turn anti-collision systems.

Resolving Conflict in Real Time: Operationalizing Culture Transformation in Deployments.

Preparation is the key to performance. The Army invests substantially in team and unit preparation prior to deployments. However, despite the time and training to build camaraderie and confidence in one another, conflict still arises within units. Most training does not address the underlying mindset that is the source of conflict. Army medicine has utilized training material that addresses the mindset source of conflict to improve team and organizational collaboration and outcomes in hospital settings. We adapted this current Army training to conditions in a deployment environment and improved the culture and reduced the conflict in the unit.

Conflict in Africa during COVID-19: Social Distancing, Food Vulnerability and Welfare Response

We study the effect of social distancing, food vulnerability, welfare and labour COVID-19 policy responses on riots, violence against civilians and food-related conflicts. Our analysis uses georeferenced data for 24 African countries with monthly local prices and real-time conflict data reported in the Armed Conflict Location and Event Data Project (ACLED) from January 2015 until early May 2020. Lockdowns and recent welfare policies have been implemented in light of COVID-19, but in some contexts also likely in response to ongoing conflicts. To mitigate the potential risk of endogeneity, we use instrumental variables. We exploit the exogeneity of global commodity prices, and three variables that increase the risk of COVID-19 and efficiency in response such as countries colonial heritage, male mortality rate attributed to air pollution and prevalence of diabetes in adults. We find that the probability of experiencing riots, violence against civilians, food-related conflicts and food looting has increased since lockdowns. Food vulnerability has been a contributing factor. A 10% increase in the local price index is associated with an increase of 0.7 percentage points in violence against civilians. Nonetheless, for every additional anti-poverty measure implemented in response to COVID-19 the probability of experiencing violence against civilians, riots and food-related conflicts declines by approximately 0.2 percentage points. These anti-poverty measures also reduce the number of fatalities associated with these conflicts. Overall, our findings reveal that food vulnerability has increased conflict risks, but also offer an optimistic view of the importance of the state in providing an extensive welfare safety net.

Blockchain‐enabled virtual coupling of automatic train operation fitted mainline trains for railway traffic conflict control

Railway traffic conflicts are common in the day-to-day operation of trains due to the limited track capacity, the varying priority of trains, localised weather conditions, maintenance operations etc. The conventional conflict resolution strategy focuses on delaying the trains by considering the braking distance of a preceding train, cancellation or intermediate stopping of the trains etc. This strategy can solve the problem of railway operators than the passenger's problem of missed connecting trains, missed business opportunities or personal appointments etc. To ensure both operator and passenger satisfaction, this paper proposes a novel blockchain-enabled virtual coupling of automatic train operation fitted mainline trains for railway traffic conflicts. The immutable blockchain databases of the trains and track infrastructures help to forecast the traffic conflicts in real time. Seven variants of virtual coupling strategies are described in this study. Based on the chosen strategy, the reference model of the automatic train operation of mainline trains is virtually coupled or synchronised. Finally, the simulation results and theoretical analyses using several case studies are carried out to confirm the sufficiency of the proposed system and method. The major advantage of the proposed study is that it can be an overlay to the existing European Railway Traffic Management System Level-2.

PolySafe: A Formally Verified Algorithm for Conflict Detection on a Polynomial Airspace

Abstract This paper presents a strategy for verifying that an aircraft following a polynomial path complies with a given set of safety criteria in continuous time. Such criteria ensure that a minimal separation between the aircraft and a set of obstacles, which can be either static or dynamic, is maintained. Dynamic obstacles are also assumed to follow a known polynomial path. Dynamic obstacles may, for example, correspond to a separation volume around another flying aircraft. In the most general case, the separation criteria vary in time depending upon the position and relative velocity between the aircraft and the obstacle. The efficiency and scalability of the proposed algorithm, to be called PolySafe, make it suitable for real-time conflict detection and path re-planning of aircraft flying in a complex and crowded airspace. PolySafe has been formally verified to guarantee the detection of conflicts within a finite time horizon.

Real-Time Conflict Resolution of Task-Constrained Manipulator Motion in Unforeseen Dynamic Environments

This paper introduces conflict resolution in task-constrained real-time adaptive motion planning (RAMP) to enable a robot manipulator performing tasks in an environment with dynamically unknown obstacles. The method continuously improves and maintains diverse task constrained as well as unconstrained robot trajectories to allow the manipulator switching to a better trajectory at any time and seamlessly resolving conflicts between satisfying task constraints and avoiding dynamically unknown obstacles. If dynamic obstacles block all available task-constrained trajectories, the algorithm allows the manipulator to change goals on the fly to be free of task constraints and resume the task whenever there is a collision-free, task-constrained trajectory. The method is validated in different dynamic environments with different task constraints in both simulation and real-world experiments.

A Low-Altitude Flight Conflict Detection Algorithm Based on a Multilevel Grid Spatiotemporal Index

Flight conflict detection is fundamental to flight dispatch, trajectory planning, and flight safety control. An ever-increasing aircraft population and higher speeds, particularly the emergence of hypersonic/supersonic aircrafts, are challenging the timeliness and accuracy of flight conflict detection. Traditional trajectory conflict detection algorithms rely on traversing multivariate equations of every two trajectories, in order to yield the conflict result and involve extensive computation and high algorithmic complexity; these algorithms are often unable to provide the flight conflict solutions required quickly enough. In this paper, we present a novel, low-altitude flight conflict detection algorithm, based on the multi-level grid spatiotemporal index, that transforms the traditional trajectory-traversing multivariate conflict computation into a grid conflict state query of distributed grid databases. Essentially, this is a method of exchanging "storage space" for "computational time". First, we build the spatiotemporal subdivision and encoding model based on the airspace. The model describes the geometries of the trajectories, low-altitude obstacles, or dangerous fields and identifies the grid with grid codes. Next, we design a database table structure of the grid and create a grid database. Finally, we establish a multilevel grid spatiotemporal index, design a query optimization scheme, and examine the flight conflict detection results from the grid database. Experimental verification confirms that the computation efficiency of our algorithm is one order of magnitude higher than those of traditional methods. Our algorithm can perform real-time (dynamic/static) conflict detection on both individual aircraft and aircraft flying in formation with more efficient trajectory planning and airspace utilization.

A Satisficing Game Theoretic Approach to Multi-Aircraft Collision Avoidance

We propose a distributed approach to multi-aircraft real-time conflict resolution based on satisficing game theory which considers reducing flight collisions and improving economic efficiency. In our conflict game model, aircraft as exclusively self-interested agent are assigned different statuses by a priority ranking mechanism proposed. According to the state of one’s own priority, they take others’ preferences into consideration to choose the most satisficing conflict resolution strategy from policy set including speed and heading modification strategies. The simulation results on the random flight scenario of two concentric circles show that our method which adopts speed and heading adjustment to avoid conflicts is promising in terms of safety and system efficiency.

Cognition, conflict, and doctrine: How groupthink fails on a Clausewitz landscape

Real-time conflict takes place on Clausewitz landscapes most notably marked by fog-of-war and frictional limits, uncertainties, and misperceptions. Imposition of such factors on an opponent is, in fact, a standard tactic of confrontation, from courts of law to commerce, from political campaigns to the battlefield. Time-constrained optimization models of institutional effectiveness, based on ‘anytime algorithm’ methods, suggest that the burden of doctrinal groupthink may become synergistic with fog-of-war and friction to greatly compromise the ability of an institution to respond to shadow price demands imposed by a contending agent or environment. A different, and more direct, approach via a ‘simple’ stochastic model, provides similar insight.

Real-time Conflict Resolution Algorithm for Multi-UAV Based on Model Predict Control

A real-time conflict resolution algorithm based on model predictive control (MPC) is introduced to address the flight conflict resolution problem in multi-UAV scenarios. Using a low-level controller, the UAV dynamic equations are abstracted into simpler unicycle kinematic equations. The neighboring UAVs exchange their predicted trajectories at each sample time to predict the conflicts. Then, under the predesignated resolution rule and strategy, decentralized coordination and cooperation are performed to resolve the predicted conflicts. The controller structure of the distributed nonlinear model predictive control (DNMPC) is designed to predict potential conflicts and calculate control variables for each UAV. Numerical simulations of multi-UAV coordination are performed to verify the performance of the proposed algorithm. Results demonstrate that the proposed algorithm can resolve the conflicts sufficiently in real time, while causing no further conflicts.

The First International Workshop on Satellite Analysis of Tropical Cyclones: Summary of Current Operational Methods to Estimate Intensity

ABSTRACT This paper summarizes the results and findings from the first International Workshop on Satellite Analyses of Tropical Cyclones (IWSATC). Estimating tropical cyclone intensity in the absence of penetrating reconnaissance aircraft is mainly left to satellite-based remote sensing methods. The primary method used globally since the latter part of the last century is the Dvorak Technique. While this method has withstood the test of time, it is not without limitations, and has been subject to regional modifications by various national tropical cyclone analysis centers. Differences between agencies in both the application of the Dvorak Technique and in general operational procedures to derive final intensity estimates can lead to real-time warning conflicts, as well as issues with final best track values for climatological analyses. As coastal populations increase, the WMO recognizes these issues as potentially serious problems, and organized a workshop in 2011 to bring together tropical cyclone experts to 1) discuss current operational analysis practices, 2) learn about developing satellite techniques, and 3) come up with a set of recommendations to start down a path towards a global congruence on intensity estimation procedures.

A Simulation Study of Predicting Real-Time Conflict-Prone Traffic Conditions

Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative of pre-collision traffic dynamics. In this paper, this is overcome through the use of highly disaggregated vehicle-based traffic data from a traffic micro-simulation (i.e., VISSIM) and the corresponding traffic conflicts data generated by the surrogate safety assessment model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety so that traffic collisions data are not needed. Three classifiers (i.e., support vector machines, k-nearest neighbours, and random forests) are then employed to examine the proposed idea. Substantial efforts are devoted to making the traffic simulation as representative of the real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e., 30 s, 1 min, 3 min, and 5 min) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of random forests with 5-min temporal aggregation in the classification results. However, attention should be given to the calibration and validation of the simulation software so as to acquire more realistic traffic data, resulting in more effective prediction of conflicts.

Security Policy Violations in SDN Data Plane

Software-defined networking (SDN) utilizes a centralized controller to distribute packet processing rules to network switches. However, rules are often generated by the applications developed by different organizations, so they may conflict with each other in data plane and lead to violations with security rules. The problem is similar to firewall conflicts in IP networks. Rule conflict resolution should incur negligible process delay, such that all rules can be correctly and safely enforced in the data plane in real time. However, since SDN allows users to use more than 35 fields to specify rules (including field transition rules), it is much more complicated to prevent enforcement of SDN rules from violating with security rules than to resolve firewall rule violation, and in particular, field transition rules are enforced. Therefore, it is extremely difficult to resolve such rule conflicts in real time before the rules are installed in SDN data plane. In this paper, we investigate the rule conflict problem in SDN and identify new covert channel attacks due to rule conflicts. To the end, we propose the covert channel defender (CCD) that prevents covert channel attacks by verifying and resolving rule conflicts. Specifically, CCD tracks all rule insertion and modification messages from applications running on the controller. It analyzes the correlation among rules based on multiple packet header fields and resolves any identified rule conflict in real time before rule installation. We implement CCD with the Floodlight controller and evaluate its performance with the real-world Stanford topology. We show that CCD can efficiently detect and prevent rule conflicts in the data plane that may raise covert channels within hundreds of microseconds and brings small overhead to the packet delivery.

Robust Safety Monitoring and Synergistic Operation Planning Between Time- and Energy-Efficient Movements of High-Speed Trains Based on MPC

When disturbances occur during high-speed train operations, real-time conflict prediction and operation re-planning play a critical role in guaranteeing the safety and efficiency of networked railway traffic. This paper attempts to propose a methodology to synergistically deal with the three critical aspects, i.e., safety monitoring, conflict resolution, and energy-efficient planning under the mechanism of model predictive control (MPC). A non-analytical rule-based high-level train movement model is employed to predict train movements and find out potential operation conflicts. It describes dynamic speed feedback adjustments under the precondition of keeping appropriate safety distances between trains. Conflict detection is implemented considering variation ranges of accelerations and decelerations to keep appropriate robustness. A predictive train rescheduling model is proposed incorporating the MPC mechanism and the non-analytical prediction model. After conflict prediction and resolution, the energy-efficient problems are formulated for overtaken trains toward junctions in a railway network. The corresponding solution algorithm is developed, taking into account the confinements of time-efficient movements of overtaking trains on energy-efficient movements of overtaken trains. Accordingly, operation synergies between overtaking and overtaken trains toward the junctions in a railway network can be achieved. An implementation algorithm is proposed, which holistically deals with robust safety monitoring and synergistic operation planning of high-speed trains in the prediction horizon, employing the train movement prediction model. Numerical experiments demonstrate the efficiency of the proposed methodology for train synergistic safe and efficient operations.

A real-time conflict solution algorithm for the train rescheduling problem

We consider the real-time resolution of conflicts arising in real-world train management applications. In particular, given a nominal timetable for a set of trains and a set of modifications due to delays or other resources unavailability, we are aiming at defining a set of actions which must be implemented to grant safety, e.g., to avoid potential conflicts such as train collisions or headway violations, and restore quality by reducing the delays. To be compatible with real-time management, the required actions must be determined in a few seconds, hence specialized fast heuristics must be used.We propose a fast and effective parallel algorithm that is based on an iterated greedy scheduling of trains on a time-space network. The algorithm uses several sortings to define the initial train dispatching rule and different shaking methods between iterations. The performance is further enhanced by using various sparsification methods for the time-space network. The best algorithm configuration is determined through extensive experiments, conducted on a set of instances derived from real-world networks and instances from the literature. The resulting heuristic proved able to consistently resolve the existing conflicts and obtaining excellent solution quality within just two seconds of computing time on a standard personal computer, for instances involving up to 151 trains and two hours of planning time horizon.

Resisting Temptation: Tracking How Self-Control Conflicts Are Successfully Resolved in Real Time

Across four studies, we used mouse tracking to identify the dynamic, on-line cognitive processes that underlie successful self-control decisions. First, we showed that individuals display real-time conflict when choosing options consistent with their long-term goal over short-term temptations. Second, we found that individuals who are more successful at self-control—whether measured or manipulated—show significantly less real-time conflict in only self-control-relevant choices. Third, we demonstrated that successful individuals who choose a long-term goal over a short-term temptation display movements that are smooth rather than abrupt, which suggests dynamic rather than stage-based resolution of self-control conflicts. These findings have important implications for contemporary theories of self-control.

Symmetry-Based Conflict Detection and Resolution Method towards Web3D-based Collaborative Design

In the process of web3D-based collaborative design, it is necessary to completely prevent operation conflicts among designers due to distributed environments and complex 3D models. Therefore, conflict detection and conflict resolution are of great significance to attain an acceptable result. In order to facilitate effective and smooth design work, a symmetry-based collaborative design framework is proposed using the X3D operation models. Combined considerations cover both models and operations, while different operation strategies are utilized for conflict resolution in web-based collaborative design. The strategy can achieve automatic operation, real-time conflict detection based on dynamically adjustable time, and conflict auto-detection and resolution with designers’ customization. A proof-of-concept system is developed for verification. The proposed resolution shows good performance, scalability and interactivity in a case study.

Identifying errors and inconsistencies in real time while using facilitated echocardiographic reporting.

Facilitated reporting using a discrete set of finding codes (FCs) is a common method of generating echocardiographic reports. The investigators developed a tool that allows echocardiographic reports to be evaluated in real time for errors, omissions, and inconsistencies on the basis of a defined group of "rules" applied to the FCs present in the report. At the time of report finalization, conflicts were displayed for the interpreting physicians, and their responses to each rule conflict were logged. Over the course of 1 year, 7,986 transthoracic echocardiographic reports were analyzed prospectively during study interpretation. Overall, 30 ± 4.7 FCs were used to generate finalized reports. An average of 2.4 ± 2.0 conflicts were detected per finalized study. Eighty-three percent of studies had at least one conflict identified. There was no significant correlation between physician experience and conflict rates, but time of day (earlier) and rate at which studies were being finalized (faster) were both correlated with increased conflict rate. Overall, physicians ignored identified conflicts 52% of the time and altered their readings to eliminate the conflicts 48% of the time. Overall, at least one change was made in 54% of all finalized studies. There was a small but significant trend for physicians to produce more conflicts over time as the tool was used. This study demonstrates that facilitated reporting of echocardiographic studies, using a discrete set of FCs, allows the generation of rules that can be used to identify discrepancies in echocardiographic reports before finalization. Conflicts are common in clinical practice, and the identification of these conflicts in real time allowed readers to review their interpretations and frequently resulted in alterations to echocardiographic reports.

Special Issue: Art and Science of Live, Virtual and Constructive Simulation and Intelligent Agents to Support Defense and Homeland Security Testing and Analysis

Modeling and simulation (M&S) has tremendous application in private and governmental sectors. From simple to complex constructive simulations, through object-oriented and agent-based simulations, all the way to the large complex distributed simulation environments, M&S affords the opportunity to examine just about anything of interest to the decision maker. We are pleased to present this combined special issue that provides works comprising this full range of M&S applications. Distributed environments composed of live, virtual and constructive (LVC) simulations have been used by the training and experimentation communities for over 10 years. The use of LVC simulation environments is being increasingly examined for potential analytical use, particularly in test and evaluation. The use of LVC for test and evaluation can increase the density and diversity of represented assets, which improves the robustness of the test. The integration of both real and simulated entities, often represented by different levels of realism and fidelity into a common synthetic battlespace or world, presents unique and interesting research challenges for both the analyst attempting to leverage this capability and secondly by the simulation architect attempting to produce credible data from a potentially complex distributed real-time software system. The analyst is often challenged with practical experimental design issues including a limited set of runs (i.e. small sample sizes) due to, for example, limited availability of pilots and operators for the live and virtual assets (i.e. the human subjects) and potential issues with unwanted noise and/or variance being introduced by including too much realism. The simulation architect is challenged with fundamental data consistency issues that arise when the desire to execute in real-time conflicts with the desire to interconnect geographically separated simulations. Additional cross-cutting challenges arise when the desire to reuse existing models and simulations often designed for different purposes are interconnected within the same simulated environment: from the analyst’s perspective of ‘‘will this affect the environment?’’ to the simulation engineer’s perspective of ‘‘are the models interoperable?’’. The first article by Hodson and Hill examines issues associated with using LVC simulation for testing purposes. Largely used in experimental and demonstration purposes, the promise of LVC simulation for test and evaluation purposes does not come without challenges, which are discussed in this lead article. The second article, ‘‘Effectiveness testing of non-lethal weapons’’ by Mezzacappa, argues that LVC simulation provides a crucial component of any research program on non-lethal weapons. The assessment of non-lethal weapons is problematic: it is hard to put humans in harm’s way. This paper discusses the US Army approach to non-lethal weapon assessment and the incorporation of LVC simulation into that testing. The third article, ‘‘Relational oriented systems engineering framework for flight training’’ by Dickerson and Holden addresses complex systems engineering concerns required to realize an LVC simulation for flight training experimentation. The authors introduce a Relational Oriented Systems Engineering (ROSE) methodology for the integration of systems within a system to realize a requisite LVC environment for complex system problems.