conflict prediction
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Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 566
Author(s):  
Nicolette Formosa ◽  
Mohammed Quddus ◽  
Alkis Papadoulis ◽  
Andrew Timmis

With the ever-increasing advancements in the technology of driver assistant systems, there is a need for a comprehensive way to identify traffic conflicts to avoid collisions. Although significant research efforts have been devoted to traffic conflict techniques applied for junctions, there is dearth of research on these methods for motorways. This paper presents the validation of a traffic conflict prediction algorithm applied to a motorway scenario in a simulated environment. An automatic video analysis system was developed to identify lane change and rear-end conflicts as ground truth. Using these conflicts, the prediction ability of the traffic conflict technique was validated in an integrated simulation framework. This framework consisted of a sub-microscopic simulator, which provided an appropriate testbed to accurately simulate the components of an intelligent vehicle, and a microscopic traffic simulator able to generate the surrounding traffic. Results from this framework show that for a 10% false alarm rate, approximately 80% and 73% of rear-end and lane change conflicts were accurately predicted, respectively. Despite the fact that the algorithm was not trained using the virtual data, the sensitivity was high. This highlights the transferability of the algorithm to similar road networks, providing a benchmark for the identification of traffic conflict and a relevant step for developing safety management strategies for autonomous vehicles.


2021 ◽  
pp. 002200272110267
Author(s):  
Robert A. Blair ◽  
Nicholas Sambanis

Beger, Morgan, and Ward (BM&W) call into question the results of our article on forecasting civil wars. They claim that our theoretically-informed model of conflict escalation under-performs more mechanical, inductive alternatives. This claim is false. BM&W’s critiques are misguided or inconsequential, and their conclusions hinge on a minor technical question regarding receiver operating characteristic (ROC) curves: should the curves be smoothed, or should empirical curves be used? BM&W assert that empirical curves should be used and all of their conclusions depend on this subjective modeling choice. We extend our original analysis to show that our theoretically-informed model performs as well as or better than more atheoretical alternatives across a range of performance metrics and robustness specifications. As in our original article, we conclude by encouraging conflict forecasters to treat the value added of theory not as an assumption, but rather as a hypothesis to test.


PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250948
Author(s):  
Tian Chai ◽  
Han Xue

Ship collision accidents are the primary threat to traffic safety in the sea. Collision accidents can cause casualties and environmental pollution. The collision risk is a major indicator for navigators and surveillance operators to judge the collision danger between meeting ships. The number of collision accidents per unit time in a certain water area can be considered to describe the regional collision risk However, historical ship collision accidents have contingencies, small sample sizes and weak regularities; hence, ship collision conflicts can be used as a substitute for ship collision accidents in characterizing the maritime traffic safety situation and have become an important part of methods that quantitatively study the traffic safety problem and its countermeasures. In this work, an EMD-QPSO-LSSVM approach, which is a hybrid of empirical mode decomposition (EMD) and quantum-behaved particle swarm optimization (QPSO) optimized least squares support vector machine (LSSVM) model, is proposed to forecast ship collision conflicts. First, original ship collision conflict time series are decomposed into a collection of intrinsic mode functions (IMFs) and a residue with EMD. Second, both the IMF components and residue are applied to establish the corresponding LSSVM models, where the key parameters of the LSSVM are optimized by QPSO algorithm. Then, each subseries is predicted with the corresponding LSSVM. Finally, the prediction values of the original ship collision conflict datasets are calculated by the sum of the forecasting values of each subseries. The prediction results of the proposed method is compared with GM, Lasso regression method, EMD-ENN, and the predicted results indicate that the proposed method is efficient and can be used for the ship collision conflict prediction.


2021 ◽  
Vol 9 (2) ◽  
pp. 60-73
Author(s):  
Victor Ekong

This paper proposes a soft computing system driven by Neural Networks, Fuzzy Logic and Principal Component Analysis (PCA) for the prediction of conflict in the Niger Delta (ND) region of Nigeria. Identifying conflicts along the level of severity with which they arise in oil bearing host communities (OBHC) is of primary importance to government and other stakeholders, as the accompanying conflict risks mitigation course administered could be planned based on the level of severity of the conflict situations. The system is implemented using MATLAB and Microsoft Excel running on Microsoft Windows 10 operating system. The data set chosen for classification and experimental simulation is based on a statistical data obtained from a three-year field study of the nine states of the ND region of Nigeria. The average training and testing errors of 0.015514 and 0.053247 were obtained at 50 epochs for the model using a hybrid algorithm. PCA reduced the dimension of the original data set at a Cronbach’s alpha of over 0.8 with a 10-fold cross validation, thereby reducing the computational complexity and inference time of the model. The model predicted the conflict risk with an average accuracy of 92.85% and this compared favourably with domain experts conventional conflict prediction approaches. The result obtained gives a promising conclusion that the model is effective in predicting at high level of accuracy, the degree of conflict and presents a veritable decision support for conflict resolution and mediation agencies and stakeholders.


2021 ◽  
pp. 1-45
Author(s):  
Samuel Bazzi ◽  
Robert A. Blair ◽  
Christopher Blattman ◽  
Oeindrila Dube ◽  
Matthew Gudgeon ◽  
...  

How feasible is violence early-warning prediction? Columbia and Indonesia have unusually fine-grained data. We assemble two decades of local violent events alongside hundreds of annual risk factors. We attempt to predict violence one year ahead with a range of machine learning techniques. Our models reliably identify persistent, high-violence hot spots. Violence is not simply autoregressive, as detailed histories of disaggregated violence perform best, but socioeconomic data substitute well for these histories. Even with unusually rich data, however, our models poorly predict new outbreaks or escalations of violence. These “best case” scenarios with annual data fall short of workable early-warning systems.


2021 ◽  
Vol 52 ◽  
pp. 292-299
Author(s):  
Federico Orsini ◽  
Gregorio Gecchele ◽  
Massimiliano Gastaldi ◽  
Riccardo Rossi

2020 ◽  
Vol 1 (5) ◽  
Author(s):  
Vitalian Danciu ◽  
Cuong Ngoc Tran

Abstract The Software-Defined Networking (SDN) architecture facilitates the flexible deployment of network functions by detaching them from network devices to a logically centralized point, the so-called SDN controller, and maintaining a common communication interface between them. While promoting innovation for each side, this architecture also induces a higher chance of conflicts between concurrent control applications compared to existing traditional networks. We have discovered a new type of anomalies that we call hidden conflicts. They appear to occur only due to side-effects of control application’s behaviour and to be independent of and distinct from the class of conflicts between rules present in the network devices. We analyse the SDN interaction primitives susceptible to such disruptions and present experiments supporting our analysis, the result of which indicates the necessity of the knowledge on the control mechanics in detecting hidden conflicts. We present a hidden conflict prediction approach that employs speculative provocation to determine the deployed applications’ behaviour. The observed behaviour can be leveraged to predict undesired network state. Evaluation of our prediction prototype suggests that prediction functions should be integrated into control applications.


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