Two-level Evaluation of Public Transport Performances

The focus of this paper is evaluation of public transport (PT) performances. For this purpose the focus is on quantitative performance attributes of tramway network and two levels of the evaluation analysis are defined – the micro and macro level analysis. The two-level evaluation analysis can be used to solve the problem of ambiguity in conclusions about PT performances. The ambiguity appears when the set of collected data is incomplete, misinterpreted, partially analyzed or when some background impacts are neglected. For each level of analysis a set of performance indicators is defined and their mathematical formulations are presented. The selection of indicators is based on the analysis of expected events on the PT vehicle’s itinerary. The applied data collection methodology, necessary for testing of proposed evaluation approach, is described and the results are presented and discussed. It has been found that for a specific case the two-level evaluation analysis can be important for the control of PT performances and transport planning. KEY WORDS: evaluation, public transport performance, micro and macro level analysis, quality of servic

FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment

In the quest to optimize user experience, network, and service, providers continually seek to deliver high-quality content tailored to individual preferences. However, predicting user perception of quality remains a challenging task, given the subjective nature of human perception and the plethora of technical attributes that contribute to the overall viewing experience. Thus, we introduce a Fuzzy Logic-bAsed ModEl for Video Quality Assessment (FLAME-VQA), leveraging the LIVE-YT-HFR database containing 480 video sequences and subjective ratings of their quality from 85 test subjects. The proposed model addresses the challenges of assessing user perception by capturing the intricacies of individual preferences and video attributes using fuzzy logic. It operates with four input parameters: video frame rate, compression rate, and spatio-temporal information. The Spearman Rank–Order Correlation Coefficient (SROCC) and Pearson Correlation Coefficient (PCC) show a high correlation between the output and the ground truth. For the training, test, and complete dataset, SROCC equals 0.8977, 0.8455, and 0.8961, respectively, while PCC equals 0.9096, 0.8632, and 0.9086, respectively. The model outperforms comparative models tested on the same dataset

Modeling the Level of User Frustration for the Impaired Telemeeting Service Using User Frustration Susceptibility Index (UFSI)

Modern users are accustomed to always-accessible networks ready to serve all of their communication, entertainment, information, and other needs, at the touch of their devices. Spoiled with choices provided on the competitive markets, the risk of customer churn makes network and service providers sensitive to user Quality of Experience (QoE). Services that enable people to work and industries to function in these pandemic times, such as the telemeeting service, are becoming ever more critical, not just for the end-users but also for the providers. Nevertheless, the heterogeneity of end-users network environments and the uniqueness of the service (bidirectional video and audio transmissions and interactivity between the meeting peers) imposes specific QoE requirements. Hence, this paper focuses on understanding how different service quality degradations affect user perception and frustration with such impaired service. The impact of eight quality degradations was analyzed. Based on the conducted user study, we used the multiple regression analysis and developed three models capable of predicting user Level of Frustration (LoF) for the specific degradations that we have analyzed. The models work with the User Frustration Susceptibility Index (UFSI), which categorizes users into groups based on their tendency to become frustrated with the impaired service

State-of-the-Practice in Evaluation of Quality of Experience in Real-Life Environments

Continuous monitoring of achieved level of service quality in packet-switched networks represents an activity of major importance for network and service providers. This is paramount for network resources provisioning which have to satisfy expectations of fickle customers. However, providing enough resources to specific user does not automatically increase their Quality of Experience (QoE), hence understanding of the relationship between these two is crucial in the network management process. Essentially, this requires subjective testing of service quality which is usually done in controlled environments such as laboratories. Nevertheless, the most accurate subjective evaluation of QoE includes real-life experiments in the environments where the services are actually used. Therefore, the aim of this paper is to provide a review of the current state-of-the-practice in evaluating QoE in real-life environments

Measuring the effects of video streaming quality in Virtual Reality experience

In this paper we present a methodology of measuring Virtual Reality (VR) experience while simulating various video streaming conditions. For that, we used four different videos, a VR headset and a video editing software, where specific changes were made to video clips in order to make it feel a believable video streaming experience. Frames per second(fps), resolution and bitrate variables were manipulated to measure the changes in perception of video quality. While streaming videos these variables get affected by the quality of internet connection. Our objective was to simulate such a scenario, where an internet connection quality would vary throughout the duration of the whole video, during which we could measure subjective score of quality perception