A Crowd Pre-Warning System Based on Mobile Locators and Behavior Prediction

2016 ◽  
Author(s):  
Zheng Hong ◽  
Deng Xiao ◽  
Deng Wenxuan
Keyword(s):  
Warning System ◽  
Behavior Prediction ◽  
And Behavior

scholarly journals Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

2020 ◽  
Vol 17 (19) ◽  
pp. 6962
Author(s):  
Yaqi Liu ◽  
Xiaoyuan Wang
Keyword(s):  
Probability Distribution ◽  
Prediction Models ◽  
Transition Probability ◽  
Warning System ◽  
Emotional States ◽  
Behavior Prediction ◽  
Emotion Induction ◽  
Car Following ◽  
And Behavior ◽  
The Relationship

Joining worldwide efforts to understand the relationship between driving emotion and behavior, the current study aimed at examining the influence of emotions on driving intention transition. In Study 1, taking a car-following scene as an example, we designed the driving experiments to obtain the driving data in drivers’ natural states, and a driving intention prediction model was constructed based on the HMM. Then, we analyzed the probability distribution and transition probability of driving intentions. In Study 2, we designed a series of emotion-induction experiments for eight typical driving emotions, and the drivers with induced emotion participated in the driving experiments similar to Study 1. Then, we obtained the driving data of the drivers in eight typical emotional states, and the driving intention prediction models adapted to the driver’s different emotional states were constructed based on the HMM severally. Finally, we analyzed the probabilistic differences of driving intention in divers’ natural states and different emotional states, and the findings showed the changing law of driving intention probability distribution and transfer probability caused by emotion evolution. The findings of this study can promote the development of driving behavior prediction technology and an active safety early warning system.

Personality and behavior prediction and consistency across cultures: A multimethod study of Blacks and Whites in South Africa.

2018 ◽  
Vol 114 (3) ◽  
pp. 465-481 ◽  
Author(s):  
Velichko H. Fetvadjiev ◽  
Deon Meiring ◽  
Fons J. R. van de Vijver ◽  
J. Alewyn Nel ◽  
Lusanda Sekaja ◽  
...  
Keyword(s):  
South Africa ◽  
Behavior Prediction ◽  
Blacks And Whites ◽  
And Behavior

scholarly journals Analysis and Prediction of Pedestrians’ Violation Behavior at the Intersection Based on a Markov Chain

2021 ◽  
Vol 13 (10) ◽  
pp. 5690
Author(s):  
Chengyuan Mao ◽  
Lewen Bao ◽  
Shengde Yang ◽  
Wenjiao Xu ◽  
Qin Wang
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Warning System ◽  
Demographic Characteristics ◽  
Chain Model ◽  
Behavior Prediction ◽  
Road Geometry ◽  
Using Data ◽  
Pedestrian Crossing ◽  
Prediction Approach

Pedestrian violations pose a danger to themselves and other road users. Most previous studies predict pedestrian violation behaviors based only on pedestrians’ demographic characteristics. In practice, in addition to demographic characteristics, other factors may also impact pedestrian violation behaviors. Therefore, this study aims to predict pedestrian crossing violations based on pedestrian attributes, traffic conditions, road geometry, and environmental conditions. Data on the pedestrian crossing, both in compliance and in violation, were collected from 10 signalized intersections in the city of Jinhua, China. We propose an illegal pedestrian crossing behavior prediction approach that consists of a logistic regression model and a Markov Chain model. The former calculates the likelihood that the first pedestrian who decides to cross the intersection illegally within each signal cycle, while the latter computes the probability that the subsequent pedestrians who decides to follow the violation. The proposed approach was validated using data gathered from an additional signalized intersection in Jinhua city. The results show that the proposed approach has a robust ability in pedestrian violation behavior prediction. The findings can provide theoretical references for pedestrian signal timing, crossing facility optimization, and warning system design.

scholarly journals A driver’s car-following behavior prediction model based on multi-sensors data

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Hui Wang ◽  
Menglu Gu ◽  
Shengbo Wu ◽  
Chang Wang
Keyword(s):  
Prediction Model ◽  
Warning System ◽  
Normal Operation ◽  
Support Vector ◽  
Area Network ◽  
Behavior Prediction ◽  
Accelerator Pedal ◽  
Car Following ◽  
Preceding Vehicle ◽  
Time Distance

AbstractThe prerequisite for the effective operation of vehicle collision warning system is that the necessary operation is not implemented. Therefore, the behavior prediction that the driver should perform when the preceding vehicle braking is the key to improve the effectiveness of the warning system. This study was conducted to acquire characteristics in the car-following behavior when confronted by the braking of the preceding vehicle, including the reaction time and operation behavior, and establish a behavior prediction model. A driving experiment on the expressway was conducted using devices, such as millimeter-wave radars and controller area network (CAN) bus data, to acquire 845 segments of car following when the brake lamps of the car ahead are on. Data analysis demonstrates that the mean of time distance of car following, mean of car-following distance, and time-to-collision (TTC) mean are closely related with whether or not the driver slowed the car down. The operation states of the driver were divided into keeping the unchanged state of the degree of accelerator pedal opening, loosening of accelerator pedal without braking, braking, and other special situations with the input variables of car-following distance, speed of driver’s car, relative speed, time distance, and TTC using the support vector machine (SVM) method to build a prediction model for the operation behavior of the driver. The verification result showed that the model predicts driving behavior with an accuracy rate of 80%. It reflects the actual decision-making process of the driver, especially the normal operation of the driver, to loosen the accelerator pedal without braking. This model can help to optimize the algorithm of the rear-end accident warning system and improve intelligent system acceptance.

scholarly journals War Games: North Korea'S Reaction to US and South Korean Military Exercises

2012 ◽  
Vol 12 (2) ◽  
pp. 275-294 ◽  
Author(s):  
Vito D'Orazio
Keyword(s):  
United States ◽  
South Korea ◽  
North Korea ◽  
Military Training ◽  
Warning System ◽  
The United States ◽  
South Korean ◽  
Military Exercises ◽  
War Games ◽  
And Behavior

Since 1976, the militaries of the United States and South Korea have been holding routine joint military exercises (JMEs) for the purposes of military training and deterrence against North Korea. These exercises are frequently cited as a cause of tension on the peninsula, causing North Korea to escalate its conflictual rhetoric and behavior. I empirically assess this claim using new data on US-ROK JMEs and machine-coded event data collected by the Integrated Crisis Early Warning System. The findings show that North Korea does not systematically escalate its conflictual rhetoric or behavior during or near the occurrence of JMEs. The results hold for both low- and high-intensity exercises and for rhetoric that has the United States and South Korea as its target.

SEMANTIC COMPUTING IN SOCIAL MEDIA

2013 ◽  
Vol 07 (03) ◽  
pp. 325-347
Author(s):  
DAVID ALFRED OSTROWSKI
Keyword(s):  
Social Media ◽  
Public Relations ◽  
Customer Service ◽  
Business Intelligence ◽  
Data Sources ◽  
Behavior Prediction ◽  
Topic Identification ◽  
Business Operations ◽  
And Behavior ◽  
Semantic Computing

The ever-increasing amount of information flowing through Social Media presents numerous opportunities for the generation of Business Intelligence. Challenges exist in the leveraging of these data sources due to their heterogeneity and unstructured content. This paper presents the application of Semantic Computing to Social Media for industrial application, focusing on topic identification and behavior prediction. The methodologies described can benefit many areas of an organization including support of marketing, customer service, engineering and public relations. Results demonstrate that business operations can be substantially enhanced through application of Semantic Computing to Social Media.

An Analytical Study on Machine Learning Techniques

2021 ◽  
pp. 137-157
Author(s):  
Law Kumar Singh ◽  
Pooja ◽  
Hitendra Garg ◽  
Munish Khanna ◽  
Robin Singh Bhadoria
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Ethical Issues ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Behavior Prediction ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Learning Machine ◽  
And Behavior

The last few months have produced a remarkable expansion in research and deep study in the field of machine learning. Machine learning is a technique in which the set of the methods are used by the computers to make prediction, improve prediction and behavior prediction based on dataset. The learning techniques can be classified as supervised and unsupervised learning. The focus is on supervised machine learning that covers all the predictions problem for which we had the dataset in which the outcome is already known. Some of the algorithm like naive bayes, linear regression, SVM, k-nearest neighbor, especially neural network have gain growth in this area. The classifiers of machine learning are completely unconstrained with the assumptions of statistical and for that they are adapted by complex data. The authors have demonstrated the application of machine learning techniques and its ethical issues.

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