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Entropic Analysis to Assess impact of Policies on Disorders and Conflicts within a system: Case Study of Traffic intersection as 12-Qubit Social Quantum System

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 Added by Rakesh Kumar Pandey
 Publication date 2020
  fields Physics
and research's language is English




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Entropic analysis of a scenario at a traffic intersection is attempted in detail. The model is utilized to define Conflict Entropy. It is shown that with the use of strategies (policies) like installing traffic lights and construction of flyovers the Entropy is reduced thereby making the traffic ordered. It is shown that these policies help in reducing the Entropy and eliminating the Conflict Entropy completely in both the cases. Such an analysis can find immense application in deciding a favorable policy and in formulation of artificial intelligence algorithms. A striking similarity of the traffic intersection is found with Quantum systems of twelve qubits that opens up a new scope of study of traffic flows to understand the behavior of Quantum Systems.

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Moderate length of time window can get the best accurate result in detecting the key incident time using extended spectral envelope. This paper presents a method to calculate the moderate length of time window. Two factors are mainly considered: (1) The significant vertical lines consist of negative elements of eigenvectors; (2) the least amount of interruption. The elements of eigenvectors are transformed into binary variable to eliminate the interruption of positive elements. Sine transform is introduced to highlight the significant vertical lines of negative elements. A novel Quality Index (QI) is proposed to measure the effect of different lengths of time window. Empirical studies on four real traffic incidents in Beijing verify the validity of this method.
134 - C. Negrevergne 2006
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A traffic incident analysis method based on extended spectral envelope (ESE) method is presented to detect the key incident time. Sensitivity analysis of parameters (the length of time window, the length of sliding window and the study period) are discussed on four real traffic incidents in Beijing. The results show that: (1) Moderate length of time window got the best accurate in detection. (2) The shorter the sliding window is, the more accurate the key incident time are detected. (3) If the study period is too short, the end time of an incident cannot be detected. Empirical studies show that the proposed method can effectively discover the key incident time, which can provide a theoretic basis for traffic incident management.
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