International Journal of Wireless and Ad Hoc Communication (IJWAC) Vol. 04, No. 02, PP. 50-60, 2022 Doi: https://doi.org/10.54216/IJWAC.040201 Received: January 11, 2022 Accepted: May 10, 2022 50 Information error-based Pythagorean fuzzy cloud technique for managing road traffic risk Mahmoud A. Zaher 1,* , Marwan Al-Akaidi 2 1 Faculty of Artificial Intelligence, Egyptian Russian University (ERU), Cairo, Egypt 2 Al-Farahidi University - Baghdad, Iraq Emails: mahmoud.zaher@eru.edu.eg ; marwan1@ieee.org Abstract This research proposes a novel procurement process for road traffic analysis by using the information error-based Pythagorean fuzzy cloud (PFC) method. First, a 20-factor assessment index method for road traffic was developed. The notion of PFCs was devised to represent the assessment information of an indication. Concurrently, the PFC-weighted Bonferroni mean (PFCWBM) operator was created to aggregate the evaluation data of multiple indications. Then, a method for evaluating and selecting road traffic based on the PFCWBM operator was developed. Furthermore, an application for demonstrating the efficacy of the suggested method was provided. Finally, the effectiveness of the proposed method was evaluated. Results demonstrate that our algorithm can define and assess complicated data with relatively high susceptibility and environmental adaptation. Keywords: Pythagorean fuzzy cloud; road traffic; information error; risk analysis 1. Introduction Individuals regularly participate in dangerous behaviors, some of which are voluntary, such as cigarette smoking or snowboarding, in which risk is an integral part of the activity’s appeal. Other forms of danger, such as eating or traveling to work or school, are inseparable from everyday activities, and they cannot be readily avoided. If the risk perceptions of people are correct (i.e., knowledge of the real effect of danger they will have to confront), then they can make educated choices and subject themselves to the optimum degree of risk. Numerous research has attempted to assess the mortality risk of people [1], and empirical data reveal that individuals have an inaccurate sense of mortality hazards. This bias affects not only the capacity of people to make well-informed choices but also the optimum allocation of resources by policymakers, who may base their judgments on objective risk metrics or expert evaluations. Additionally, skewed risk perception may influence choice elicitation. For example, if people overestimate the mortality rate, then the monetary estimates of lowering the risk of mortality may be favorably skewed [2],[3]. Therefore, understanding user risk perception is crucial not only from a research standpoint but from a political one. People overestimate and underestimate the likelihood of medium and large probabilities with respect to risk belief [4]–[9]. In fact, Benjamin and Dougan [10] found that perceived risk is impartial to age after re-examining the data from Lichtenstein et al. [6], albeit an individual’s risk perception is likely more accurate when danger is related to their age group. Benjamin et al. [11] support the aforementioned findings. By contrast, Armantier [9] claims that people are influenced by the ―anchoring effect,‖ but the ―salient and robust phenomena‖ also confirm Benjamin and Dougan’s [10] previous finding that people perceive the danger of their age group as being more correct.