Reinforcement learning-based SDN routing scheme empowered by causality detection and GNN.

In recent years, with the rapid development of network applications and the increasing demand for high-quality network service, quality-of-service (QoS) routing has emerged as a critical network technology. The application of machine learning techniques, particularly reinforcement learning and graph neural network, has garnered significant attention in addressing this problem. However, existing reinforcement learning methods lack research on the causal impact of agent actions on the interactive environment, and graph neural network fail to effectively represent link features, which are pivotal for routing optimization. Therefore, this study quantifies the causal influence between the intelligent agent and the interactive environment based on causal inference techniques, aiming to guide the intelligent agent in improving the efficiency of exploring the action space. Simultaneously, graph neural network is employed to embed node and link features, and a reward function is designed that comprehensively considers network performance metrics and causality relevance. A centralized reinforcement learning method is proposed to effectively achieve QoS-aware routing in Software-Defined Networking (SDN). Finally, experiments are conducted in a network simulation environment, and metrics such as packet loss, delay, and throughput all outperform the baseline.

Dynamic resource allocation in 5G networks using hybrid RL-CNN model for optimized latency and quality of service.

The rapid deployment of 5G networks necessitates innovative solutions for efficient and dynamic resource allocation. Current strategies, although effective to some extent, lack real-time adaptability and scalability in complex, dynamically-changing environments. This paper introduces the Dynamic Resource Allocator using RL-CNN (DRARLCNN), a novel machine learning model addressing these shortcomings. By merging Convolutional Neural Networks (CNN) for feature extraction and Reinforcement Learning (RL) for decision-making, DRARLCNN optimizes resource allocation, minimizing latency and maximizing Quality of Service (QoS). Utilizing a state-of-the-art "5G Resource Allocation Dataset", the research employs Python, TensorFlow, and OpenAI Gym to implement and test the model in a simulated 5 G environment. Results demonstrate the effectiveness of DRARLCNN, showcasing an impressive R2 score of 0.517, MSE of 0.035, and RMSE of 0.188, surpassing existing methods in allocation efficiency and latency. The DRARLCNN model not only outperforms existing methods in allocation efficiency and latency but also sets a new benchmark for future research in dynamic 5G resource allocation. Through its innovative approach and promising results, DRARLCNN opens avenues for further advancements in optimizing resource allocation within dynamic 5G networks.

IoT service composition based on improved Shuffled Frog Leaping Algorithm.

In the complex Internet of Things (IoT) environment, a plethora of IoT services with akin functions but varying qualities of service exist. To meet diverse customer needs and drive widespread application, service composition optimization becomes crucial. In the current era of rapid development in artificial intelligence, intelligent algorithms play a significant role in optimizing service composition. However, algorithms applied to IoT service composition optimization face common challenges of low search efficiency and insufficient optimization precision, including the Shuffled Frog Leaping Algorithm (SFLA) and Genetic Algorithm (GA). Therefore, this study seeks to enhance the perception of service quality in IoT service composition. It proposes an improved SFLA (ISFLA) based on the original SFLA. The algorithm integrates chaos theory and reverse learning theory for the acquisition of the initial population. It utilizes Euclidean distance to partition the population into groups and employs Gaussian mutation to optimize the optimal individual of each group. Finally, the entire population undergoes evolution through a local update method based on two strategies. Simulated experiments were conducted to search for optimal IoT service composition solutions of different scales. The results indicate that, compared to the SFLA, GA, ISFLA*, IGSFLA and SFLAGA, ISFLA achieves superior fitness values, better composition solutions, and exhibits faster convergence, higher stability, and greater overall operational efficiency.

Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service.

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.2 %). The studies under examination employ a diverse range of ML and DL methods, along with distributed systems, with Convolutional Neural Networks (CNNs) being the most commonly used (16.7 %), followed by Long Short-Term Memory (LSTM) networks (14.6 %) and shallow learning networks (12.5 %). In evaluating QoS, the predominant emphasis revolves around the accuracy parameter (80 %). This study highlights how ML, DL, and distributed systems reshape healthcare. It contributes to advancing healthcare quality, bridging the gap between technology and medical adoption, and benefiting practitioners and patients.

Multi-factor optimized mobile sink data collection framework for hybrid WSN-LTE assisted IoT network.

The convergence of wireless sensor network-assisted Internet of Things has diverse applications. In most applications, the sensors are battery-powered, and it is necessary to use the energy judiciously to extend their functional duration effectively. Mobile sinks-based data collection is used to extend the lifespan of these networks. But providing a scalable and effective solution with consideration for multi-criteria factors of quality of service and lifetime maximization is still a challenge. This work addresses this problem with a hybrid wireless sensor network-Long term evolution assisted architecture. The problem of maximizing lifetime and providing multi-factor quality of service is solved as a two-stage optimization problem involving clustering and data collection path scheduling. Hybrid meta-heuristics is used to solve the clustering optimization problem. Minimal Steiner tree-based graph theory is applied to schedule the data collection path for sinks. Unlike existing works, the lifetime maximization without QoS degradation is addressed by hybridizing multiple approaches of multi-criteria optimal clustering, optimal path scheduling, and network adaptive traffic class-based data scheduling. This hybridization helps to extend the lifetime and enhance the QoS regarding packet delivery within the proposed solution. Through simulation analysis, the introduced approach yields a noteworthy increase of at least 6% and reduces packet delivery delay by 26% compared to existing methodologies.

Enhancing Cybersecurity in Distributed Microgrids: A Review of Communication Protocols and Standards.

The effective operation of distributed energy sources relies significantly on the communication systems employed in microgrids. This article explores the fundamental communication requirements, structures, and protocols necessary to establish a secure connection in microgrids. This article examines the present difficulties facing, and progress in, smart microgrid communication technologies, including wired and wireless networks. Furthermore, it evaluates the incorporation of diverse security methods. This article showcases a case study that illustrates the implementation of a distributed cyber-security communication system in a microgrid setting. The study concludes by emphasizing the ongoing research endeavors and suggesting potential future research paths in the field of microgrid communications.

Assessing the quality of services at an academic library.

Customers usually have high expectations on the services they receive. The LibQUAL model was employed in this study to investigate the quality of services at an academic library. The participants were chosen from the five colleges in a university using simple random sampling. Two hundred participants were chosen from each college. In all, 1000 participants (including faculty members, postgraduate and undergraduate students) were selected for the study. The study revealed that users knew and used the library's services. The researchers delivered the questionnaires to the respondents at the library. The results showed that the library service quality (LSQ) fell short of users' expectations. There was a statistically significant difference in LSQ between gender of users. The users' expectations of the library's level of service were out of sync or mismatch. The variations could be attributed to differences in information needs of users. This requires the library to assess its services from users' perspective regularly. The use of the LibQUAL model provides useful information that library management can employ for developing service quality measurement scale and planning for service quality. The study also provides information about services that needs improvement, so that library staff could manage users' expectations and satisfaction in a better way. University authorities expect good return of investment made into the development of the library. Therefore, libraries should improve upon their services to boost the image of the Universities.

Toward QoS Monitoring in IoT Edge Devices Driven Healthcare-A Systematic Literature Review.

Smart healthcare is altering the delivery of healthcare by combining the benefits of IoT, mobile, and cloud computing. Cloud computing has tremendously helped the health industry connect healthcare facilities, caregivers, and patients for information sharing. The main drivers for implementing effective healthcare systems are low latency and faster response times. Thus, quick responses among healthcare organizations are important in general, but in an emergency, significant latency at different stakeholders might result in disastrous situations. Thus, cutting-edge approaches like edge computing and artificial intelligence (AI) can deal with such problems. A packet cannot be sent from one location to another unless the "quality of service" (QoS) specifications are met. The term QoS refers to how well a service works for users. QoS parameters like throughput, bandwidth, transmission delay, availability, jitter, latency, and packet loss are crucial in this regard. Our focus is on the individual devices present at different levels of the smart healthcare infrastructure and the QoS requirements of the healthcare system as a whole. The contribution of this paper is five-fold: first, a novel pre-SLR method for comprehensive keyword research on subject-related themes for mining pertinent research papers for quality SLR; second, SLR on QoS improvement in smart healthcare apps; third a review of several QoS techniques used in current smart healthcare apps; fourth, the examination of the most important QoS measures in contemporary smart healthcare apps; fifth, offering solutions to the problems encountered in delivering QoS in smart healthcare IoT applications to improve healthcare services.

Maternal satisfaction on quality of childhood vaccination services and its associated factors at public health centers in Addis Ababa, Ethiopia.

BACKGROUND: Vaccination is one of the most important public health interventions to reduce child mortality and morbidity. In Ethiopia, about 472,000 children die each year by vaccine-preventable diseases. A satisfied mother is assumed to use the services and complies with the service provider for better health care outcomes. However, there was no adequate evidence regarding maternal satisfaction with quality of childhood vaccination services. This study aimed to assess maternal satisfaction on quality of childhood vaccination services and its associated factors at public health centers in Addis Ababa, Ethiopia. METHODS: A facility-based cross-sectional study was conducted from 12 July to 12 August 2021 at public health centers in Addis Ababa, Ethiopia. A total of 366 mothers (caretakers) of under one-year-old children participated in the study. A systematic sampling technique with an interviewer-administered questionnaire and inventory checklist were used to collect the data. A binary logistic regression model was fitted. Adjusted Odds Ratio (AOR) with 95% confidence interval (CI) and p-value < 0.05 were used to identify the factors associated with the outcome. RESULTS: Nearly two-thirds (61.2%) of mothers (caretakers) were satisfied with the quality of childhood vaccination services. Service providers' greeting [AOR = 1.60; 95%CI: 1.37-1.99] and information about the types of vaccines [AOR = 1.54; 95%CI: 1.32-1.89] were positively associated with maternal satisfaction. On the contrary, long waiting time of mothers (caretakers) to receive services [AOR = 0.29; 95%CI: 0.14-0.62] was negatively associated with services. CONCLUSION: The overall maternal satisfaction towards the quality of childhood vaccination services in this study was found to be low. Minimizing waiting time at the health facility, enhancing greetings and providing adequate information regarding childhood vaccination for mothers (caretakers) improved their satisfaction with the services.

The Age of Information in Wireless Cellular Systems: Gaps, Open Problems, and Research Challenges.

One of the critical use cases for prospective fifth generation (5G) cellular systems is the delivery of the state of the remote systems to the control center. Such services are relevant for both massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC) services that need to be supported by 5G systems. The recently introduced the age of information (AoI) metric representing the timeliness of the reception of the update at the receiver is nowadays commonly utilized to quantify the performance of such services. However, the metric itself is closely related to the queueing theory, which conventionally requires strict assumptions for analytical tractability. This review paper aims to: (i) identify the gaps between technical wireless systems and queueing models utilized for analysis of the AoI metric; (ii) provide a detailed review of studies that have addressed the AoI metric; and (iii) establish future research challenges in this area. Our major outcome is that the models proposed to date for the AoI performance evaluation and optimization deviate drastically from the technical specifics of modern and future wireless cellular systems, including those proposed for URLLC and mMTC services. Specifically, we identify that the majority of the models considered to date: (i) do not account for service processes of wireless channel that utilize orthogonal frequency division multiple access (OFDMA) technology and are able to serve more than a single packet in a time slot; (ii) neglect the specifics of the multiple access schemes utilized for mMTC communications, specifically, multi-channel random access followed by data transmission; (iii) do not consider special and temporal correlation properties in the set of end systems that may arise naturally in state monitoring applications; and finally, (iv) only few studies have assessed those practical use cases where queuing may happen at more than a single node along the route. Each of these areas requires further advances for performance optimization and integration of modern and future wireless provisioning technologies with mMTC and URLLC services.

Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks.

The involvement of wireless sensor networks in large-scale real-time applications is exponentially growing. These applications can range from hazardous area supervision to military applications. In such critical contexts, the simultaneous improvement of the quality of service and the network lifetime represents a big challenge. To meet these requirements, using multiple mobile sinks can be a key solution to accommodate the variations that may affect the network. Recent studies were based on predefined mobility models for sinks and relied on multi-hop routing techniques. Besides, most of these studies focused only on improving energy consumption without considering QoS metrics. In this paper, multiple mobile sinks with random mobile models are used to establish a tradeoff between power consumption and the quality of service. The simulation results show that using hierarchical data routing with random mobile sinks represents an efficient method to balance the distribution of the energy levels of nodes and to reduce the overall power consumption. Moreover, it is proven that the proposed routing methods allow for minimizing the latency of the transmitted data, increasing the reliability, and improving the throughput of the received data compared to recent works, which are based on predefined trajectories of mobile sinks and multi-hop architectures.

Application-Aware Scheduling for IEEE 802.15.4e Time-Slotted Channel Hopping Using Software-Defined Wireless Sensor Network Slicing.

Given the improvements to network flexibility and programmability, software-defined wireless sensor networks (SDWSNs) have been paired with IEEE 802.15.4e time-slotted channel hopping (TSCH) to increase network efficiency through slicing. Nonetheless, ensuring the quality of service (QoS) level in a scalable SDWSN remains a significant difficulty. To solve this issue, we introduce the application-aware (AA) scheduling approach, which isolates different traffic types and adapts to QoS requirements dynamically. To the best of our knowledge, this approach is the first to support network scalability using shared timeslots without the use of additional hardware while maintaining the application's QoS level. The AA approach is deeply evaluated compared with both the application traffic isolation (ATI) approach and the application's QoS requirements using the IT-SDN framework and by varying the number of nodes up to 225. The evaluation process took into account up to four applications with varying QoS requirements in terms of delivery rate and delay. In comparison with the ATI approach, the proposed approach enhanced the delivery rate by up to 28% and decreased the delay by up to 57%. Furthermore, even with four applications running concurrently, the AA approach proved capable of meeting a 92% delivery rate requirement for up to 225 nodes and a 900 ms delay requirement for up to 144 nodes.

[Assessment of patient satisfaction with orthodontic services provided in medical organizations of various forms of ownership]. / Otsenka udovletvorennosti patsientov ortodonticheskimi uslugami v meditsinskikh organizatsiyakh razlichnoi formy sobstvennosti.

OBJECTIVE: The study of expectations and satisfaction with the quality of orthodontic care provided to children in public and private dental organizations. MATERIAL AND METHODS: The study was conducted at the clinical bases of the Borovsky Institute of Dentistry of the Sechenov First Moscow State Medical University, Vladimirsky Moscow Regional Research Clinical Institute, Videntis LLC in the period from January to April 2022. An anonymous questionnaire was developed for the study: "Questionnaire for patients to assess the quality and conditions of orthodontic medical services in a medical organization". All data are processed using the statistical software SPSS v. 20. RESULTS AND DISCUSSION: According to respondents, the quality of service in both public and private dental organizations depends on the material and technical equipment of the medical organization, the attitude of medical personnel, the duration of treatment and the qualifications of orthodontists. Satisfaction of orthodontic care in public dental organizations corresponded to a high level in 73.4% of cases, an average level of 15.6% of cases, a low level in 11.0% of cases; in private dental organizations, a high level was noted in 98.8% of cases, an average level in 1.2% of cases, a low level in 0% of cases (in private dental organizations, not a single respondent noted the quality of services provided as low). Among the main reasons for dissatisfaction with the service of patients, the lack of diagnostic equipment, the unfriendly attitude of the secondary medical and administrative staff, as well as the duration of treatment should be highlighted. CONCLUSION: A sociological survey to assess patient satisfaction is a tool for determining the effectiveness of any medical organization, while the assessment of the quality of service of respondents depends on the material and technical equipment of the dental organization, the attitude of medical personnel, the duration of treatment and the qualifications of orthodontists. In this regard, it is very important to apply this method of satisfaction assessment when providing high-quality orthodontic care to children both in public and private dental organizations in order to improve the quality of service in a dental medical organization.

Hedge transfer learning routing for dynamic searching and reconnoitering applications in 3D multimedia FANETs.

With the fast development of unmanned aerial vehicles (UAVs) and the user increasing demand of UAV video transmission, UAV video service is widely used in dynamic searching and reconnoitering applications. Video transmissions not only consider the complexity and instability of 3D UAV network topology but also ensure reliable quality of service (QoS) in flying ad hoc networks (FANETs). We propose hedge transfer learning routing (HTLR) for dynamic searching and reconnoitering applications to address this problem. Compared with the previous transfer learning framework, HTRL has the following innovations. First, hedge principle is introduced into transfer learning. Online model is continuously trained on the basis of offline model, and their weight factors are adjusted in real-time by transfer learning, so as to adapt to the complex 3D FANETs. Secondly, distributed multi-hop link state scheme is used to estimate multi-hop link states in the whole network, thus enhancing the stability of transmission links. Among them, we propose the multiplication rule of multi-hop link states, which is a new idea to evaluate link states. Finally, we use packet delivery rate (PDR) and energy efficiency rate (EER) as two main evaluation metrics. In the same NS3 experimental scenario, the PDR of HTLR is at least 5.11% higher and the EER is at least 1.17 lower than compared protocols. Besides, we use Wilcoxon test to compare HTLR with the simplified version of HTLR without hedge transfer learning (N-HTLR). The results show that HTRL is superior to N-HTRL, effectively ensuring QoS.

A Connected World: System-Level Support Through Biosensors.

The goal of protecting the health of future generations is a blueprint for future biosensor design. Systems-level decision support requires that biosensors provide meaningful service to society. In this review, we summarize recent developments in cyber physical systems and biosensors connected with decision support. We identify key processes and practices that may guide the establishment of connections between user needs and biosensor engineering using an informatics approach. We call for data science and decision science to be formally connected with sensor science for understanding system complexity and realizing the ambition of biosensors-as-a-service. This review calls for a focus on quality of service early in the design process as a means to improve the meaningful value of a given biosensor. We close by noting that technology development, including biosensors and decision support systems, is a cautionary tale. The economics of scale govern the success, or failure, of any biosensor system.

Paediatric emergency medicine practice in Nigeria: a narrative review.

The practice of paediatric emergency medicine in Nigeria is still evolving, and laden with enormous challenges which contribute to adverse outcomes of childhood illnesses in emergency settings. Deaths from childhood illnesses presenting as emergencies contribute to overall child mortality rates in Nigeria. This narrative review discusses existing structures, organization, and practice of paediatric emergency in Nigeria. It highlights some of the challenges and suggests ways of surmounting them in order to reduce deaths in the children emergency units in Nigerian hospitals. Important aspects of this review include current capacity and need for capacity development, equipment needs for emergency care, quality of service in the context of inadequate healthcare funding and the need for improvement.

Modeling and simulation of task rescheduling strategy with resource substitution in cloud manufacturing.

When a cloud manufacturing environment extends to multi-user agent, multi-service agent and multi-regional spaces, the process of manufacturing services faces increased disturbances. When a task exception occurs because of disturbance, it is necessary to quickly reschedule the service task. We propose a multi-agent simulation modeling approach to simulate and evaluate the service process and task rescheduling strategy of cloud manufacturing, with which impact parameters can be achieved through careful study under different system disturbances. First, the simulation evaluation index is designed. In addition to the quality of service index of cloud manufacturing, the adaptive ability of task rescheduling strategy in response to a system disturbance is considered, and the flexibility of cloud manufacturing service index is proposed. Second, considering the substitution of resources, the internal and external transfer strategies of service providers are proposed. Finally, a simulation model of the cloud manufacturing service process of a complex electronic product is constructed by multi-agent simulation, and simulation experiments under multiple dynamic environments are designed to evaluate different task rescheduling strategies. The experimental results indicate that the external transfer strategy of the service provider in this case has higher quality of service and flexibility of service. Sensitivity analysis indicates that the matching rate of substitute resources for internal transfer strategy of service providers and the logistics distance of external transfer strategy of service providers are both sensitive parameters, which have significant impacts on the evaluation indexes.

"In the Wild" Video Content as a Special Case of User Generated Content and a System for Its Recognition.

In the five years between 2017 and 2022, IP video traffic tripled, according to Cisco. User-Generated Content (UGC) is mainly responsible for user-generated IP video traffic. The development of widely accessible knowledge and affordable equipment makes it possible to produce UGCs of quality that is practically indistinguishable from professional content, although at the beginning of UGC creation, this content was frequently characterized by amateur acquisition conditions and unprofessional processing. In this research, we focus only on UGC content, whose quality is obviously different from that of professional content. For the purpose of this paper, we refer to "in the wild" as a closely related idea to the general idea of UGC, which is its particular case. Studies on UGC recognition are scarce. According to research in the literature, there are currently no real operational algorithms that distinguish UGC content from other content. In this study, we demonstrate that the XGBoost machine learning algorithm (Extreme Gradient Boosting) can be used to develop a novel objective "in the wild" video content recognition model. The final model is trained and tested using video sequence databases with professional content and "in the wild" content. We have achieved a 0.916 accuracy value for our model. Due to the comparatively high accuracy of the model operation, a free version of its implementation is made accessible to the research community. It is provided via an easy-to-use Python package installable with Pip Installs Packages (pip).

A paradigm shift for medical health care to focus on a service-value approach to achieve greater patient satisfaction.

PURPOSE: This study takes a divergent approach to exploring which construct is more predictive of patient satisfaction (SAT) in a service dominant economy within the context of a healthcare setting. DESIGN/METHODOLOGY/APPROACH: Applying a critical analysis of literature, a service value (SV) model for customer SAT is proposed in this study, which is validated and confirmed with survey data from outpatients at Moorfields Eye Hospital - a world class specialist hospital based in the UK. FINDINGS: Quality of service had the strongest impact on SV but SV had the strongest impact and mediation effect on patient SAT. RESEARCH LIMITATIONS/IMPLICATIONS: The study concludes that since SV rather than quality of service is more predictive of patient SAT, health service providers should focus more on SV in addition to quality of service, if they are to meet the dynamic expectations of their patients. PRACTICAL IMPLICATIONS: Health service providers should focus more on SV in addition to quality of service, if they are to meet the dynamic expectations of their patients. SOCIAL IMPLICATIONS: This poses a strong argument in favour of a paradigm shift in focus from quality of service-based model to service value-based model for greater patient satisfaction. ORIGINALITY/VALUE: This is the first study exploring the inter-relationship of four constructs of patient SAT within the context of a leading major UK healthcare hospital service.

Factors affecting public transport performance due to the COVID-19 outbreak: A worldwide analysis.

In this paper we investigate the public transport trip frequency variations, as well as the reasons that led to the shift away from public transport means, due to the COVID-19 pandemic. We studied relevant data from the Moovit platform, and we compared operational and trip frequency characteristics of public transport systems before and after the outbreak of the pandemic in 87 cities worldwide. On average, waiting times at public transport stops/stations increased while trip distances decreased, apparently due to the mobility restriction and social distancing measures implemented in 2020. Most of the Moovit users who said that they abandoned public transport in 2020 were found in Italy and Greece. We developed linear regression analysis models to investigate (among the 35 variables examined in the study) the relationship between public transport abandonment rates and socioeconomic factors, quality of service characteristics, and indicators of pandemic's spread. Empirical findings show that public transport dropout rates are positively correlated with the COVID-19 death toll figures, the cleanliness of public transport vehicles and facilities, as well as with the income inequality (GINI) index of the population, and thus reconfirm previous research findings. In addition, the waiting time at stops/stations and the number of transfers required for commute trips appeared to be the most critical public transport trip segments, which significantly determine the discontinuation of public transport use under pandemic circumstances. Our research findings indicate specific aspects of public transport services, which require tailored adjustments in order to recover ridership in the post-pandemic period.