Retsepile C Kalaoane

Walter Musakwa

Alain Kibangou

Trynos Gumbo

Innocent Musonda

Abraham R Matamanda

BIBLIOMETRIC ANALYSIS OF QUALITY OF SERVICE IN PUBLIC TRANSPORTATION: CURRENT AND FUTURE TRENDS

Keywords: PRISMA, Vosviewer, Quality of service, Public transport, Gender, Machine learning

Public transport is a fundamental aspect of urban life and its quality of service can significantly impact the daily life of passengers. Research on quality of service in public transport is vital to creating efficient and inclusive public transport systems. This study which is both timely and imperative, provides a systematic literature review and bibliometric analysis of quality of service in public transport with special focus on gender and machine learning. The study employed a systematic search in Web of Science using relevant keywords. Based on 270 published articles on the Web of Science, we evaluate the state of knowledge through keyword analysis, cooccurrences, and co-citations. It appears that key themes in quality of service in public transportation have shifted from efficiency to user satisfaction and perceptions.

However, there is limited literature on public transport service quality in the global south, particularly in Africa, compared to the global north. In particular, the study revealed that the total publications focusing on gender in Africa is two. Moreover, much of the literature on the African continent emanates from authors and countries outside the continent. It often does not reflect the lived realities of the state of public transport in Africa. Similarly, emerging issues pertaining to machine learning and gender in public transport also require consideration. This paper paves the way for policy, practice, and further research. The review will be useful reference for evolutions of quality of service in public transport research.

Introduction

The quality of public transport services is always a major challenge in African cities [START_REF] Luke | An analysis of the quality of public transport in Johannesburg, South Africa using an adapted SERVQUAL model[END_REF]. Indeed, the deteriorating quality of service in public transport is evident through poor access to economic opportunities and the unreliability of public transportation. To the other end, high costs and longer commuting hours for local and national travel have further limited access to human life's important needs [START_REF] Guevara | Correcting for endogeneity due to omitted crowding in public transport choice using the Multiple Indicator Solution (MIS) method[END_REF]. The poor quality of service makes it difficult to break the current habit of using motor vehicles and switching to alternative sustainable means of transport, such as public transport [START_REF] Mugion | Does the service quality of urban public transport enhance sustainable mobility[END_REF]. Over the years, public transport has been regarded as an effective tool to address multiple societal challenges of sustainability and liveability [START_REF] Friman | Improvement of public transport services for non-cycling travelers[END_REF][START_REF] Oort | Innovations in the appraisal of public transport projects[END_REF]). An effective public transport system cause the least harm to the environment while offering a mobility service to everyone choosing to use it [START_REF] Park | Investigating the barriers in a typical journey by public transport users with disabilities[END_REF].

Striving for improved quality of service in public transport to increase customer satisfaction has sparked the interest of researchers [START_REF] Paulley | The demand for public transport: The effects of fares, quality of service, income and car ownership[END_REF][START_REF] De Oña | Perceived service quality in bus transit service: A structural equation approach[END_REF][START_REF] Cantillo | Fare evasion on public transport: Who, when, where and how?[END_REF]). However, public transport studies are confronted with challenges of the lack of literature in the African continent on the quality of service, gender, and machine learning. A search on Web of Science (WoS) identified 270 published articles with only 2 articles focusing on public transport and gender issues in Africa. In Ghana, Adom-Asamoah et al. ( 2021) conducts a study on the quality of service in public transport by looking closely at Ghana's intra-city public transport.

Their study shows that passengers choose to minimise the frequency use of public transport due to poor customer care. Though the paper is categorized under gender considerations, its content does not address the gender aspect in public transport.

Additionally, [START_REF] Moghayedi | Appraising the nexus between influencers on the public transport choice by women commuters in South Africa[END_REF] utilized socio demographics information to analyse modal choice among women in South Africa when using public transport, employing a Structure Equation Model (SEM). Their research demonstrate that service quality and socio demographic background influence modal choice. The study further underscores the need for incorporations of new technologies in public transportation studies in Africa.

Globally, the focus on the quality of service, gender and machine learning in public transport has been identified in transport research [START_REF] Dell'olio | The quality of service desired by public transport users[END_REF][START_REF] Wei | Perspective: predicting and optimizing thermal transport properties with machine learning methods[END_REF]. Previous research has provided valuable insights on service quality, user satisfaction, performance, efficiency and quality of service in public transport [START_REF] De Oña | Quality of service in public transport based on customer satisfaction surveys: A review and assessment of methodological approaches[END_REF][START_REF] Tirachini | Estimation of crowding discomfort in public transport: Results from Santiago de Chile[END_REF][START_REF] Chen | Comparative Analysis of the Influence of Transport Modes on Tourism: High-Speed Rail or Air? City-Level Evidence from China[END_REF]2022). In one study, [START_REF] Haghighi | Using Twitter data for transit performance assessment: a framework for evaluating transit riders' opinions about quality of service[END_REF] develops an evaluation framework using unsupervised machine learning applied to twitter data, where people expressed their opinions about the satisfaction with services provided by Salt Lake City local transit system. Indeed, machine learning has been developed to explore different concepts in different disciplines [START_REF] Okoroafor | Machine learning in subsurface geothermal energy: Two decades in review[END_REF][START_REF] Sholevar | Machine learning techniques for pavement condition evaluation[END_REF]) including public transport.

In the field of gender studies, Dash et al. (2022) conducts an analysis on service quality and and passenger satisfaction among bus riders in Baku. Their findings indicate that inadequate bus services significantly reduce passenger satisfaction with sociodemographic information playing no role in this regard. Furthermore, Freista et al.

(2023) examined gender differences in perception of bus transport service quality in Brazil. Using an Importance Performance Analysis and Tarrant and Smith methodology for the analysis. The results indicate that factors such as waiting time, scheduling and maintenance were important to both men and women. However, gender differences emerged with women placing greater importance to safety and treatment while men prioritized cost and rules than the actual bus service quality.

Nonetheless, these studies have not addressed the gender dimensions extensively in public transport, lived experience on perception and expectations.

Inlight of evolving technology, dynamic changes in customer preferences, the increasing need for sustainable and inclusive transportation options, remains unexplored territory in understanding the dynamics of quality of service in public transport particularly concerning gender related issues and potential of machine learning techniques to enhance service quality. Consequently, the increase in knowledge in public transport research has made it hard to comprehend all the research done, making it important to undertake a bibliometric analysis and review of literature to identify emerging research focus and key areas. Thus, this paper aims to use Bibliometric analysis and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to document the the state of the art of knowledge of quality of service in public transport, identify new trends and emerging themes in the literature.

Following this introduction, we discuss the methodological issues relating to the bibliometric analysis and PRISMA method focusing on how the data was gathered and analysed for this paper. The following section presents findings obtained from the bibliometric analysis. Next, these findings are discussed and a conclusion is provided to wrap the study.

Materials and Methods

The review of the literature was executed using PRISMA protocols [START_REF] Moher | Implementing PRISMA-P: recommendations for prospective authors[END_REF] and bibliometric analysis.

Information Sources and Search

Web of Science (WoS) and Scopus are the main used database for research in bibliometric analysis. WoS was selected as the only database used in this paper.

Although both databases use interdisciplinary approaches, WoS contains comprehensive coverage in literature [START_REF] Mongeon | The journal coverage of Web of Science and Scopus: a comparative analysis[END_REF]. Introduced by the Institute of Scientific Information (ISI) in 1964, WoS has since evolved in scientific research coverage with the most impact and is a widely used database for systematic literature research [START_REF] Birkle | Web of Science as a data source for research on scientific and scholarly activity[END_REF]. WoS compared with Scopus was the best fit for this paper as [START_REF] Zhu | A tale of two databases: The use of Web of Science and Scopus in academic papers[END_REF] stated that WoS is widely used and effective in practice.

All studies relating to the research were eligible for review with no specification on publication years. The database search in WoS was conducted and classified using the following keywords. The database search and the subsequent analysis were both carried out on a global scale.

 "Quality of service" AND "Public transport"  "Quality of service" AND "Public transport" AND "Gender"  "Quality of service" AND "Machine learning" AND "Public transport"

Keywords Defined

Quality of service: Is a multifaceted concept. Generally, QoS is defined as the level of service and customer satisfaction that public transport users experience when using a public transport systems [START_REF] Machado | Finding service quality improvement opportunities across different typologies of public transit customers[END_REF]De Oña et al. 2021). It includes various factors that contribute to the overall experience of the passenger. This ranges from reliability, accessibility, travel time etc.

Machine Learning: Machine learning is a subset of Artificial Intelligence (AI), allowing a system to adapt and learn from the available data [START_REF] Bi | What is machine learning? A primer for the epidemiologist[END_REF]. It uses algorithms that progressively learn from data to enhance their ability to understand data patterns and make predictions [START_REF] Peng | Multiscale modeling meets machine learning: What can we learn?[END_REF].

The inclusion criteria were all articles published in the field of quality of service in public transport (Table 1). The search in the literature was directed by screening titles and abstracts for each article to confirm eligibility. The in-depth information on the selection process is presented in Figure 1 as guided by the PRISMA protocols. PRISMA is a method used successfully to extensively scan all literature to find answers to a specified research question for a study [START_REF] Selcuk | A guide for systematic reviews: PRISMA[END_REF]. The method is employed because it eliminate the bias resulting from the traditional methods of conducting a literature review. In Addition, PRISMA guidelines increase validity of the findings of the study [START_REF] Page | The PRISMA 2020 statement: an updated guideline for reporting systematic reviews[END_REF]) and present the results in a coherent manner. The review identified 26,642 studies in WoS, of which 10 were duplicates and consequently removed. 26,632 were used for this study. After examining the title and abstract of each article and refining the scope of quality of service in public transport, 26,468 studies were excluded because they did not meet the eligibility for the present review. Following the guidelines of PRISMA, a total of 164 articles were eligible for the present review in this paper.

Studies included in quality synthesis

We also searched the terms "Quality of service" AND "Public transport" AND "Gender" resulting in 26,642 studies in WOS, of which 10 were duplicates and therefore removed, resulting in 26,632 being used for the initial analysis. After examining the title and abstract for each article and refining the scope of quality of service in public transport, with special reference to gender, 26,623 studies were excluded. Exclusion was because they did not meet the eligibility for the present review. After the screening stage, 9 articles were taken further into the eligibility stage. Of the 9 articles, 1 was excluded for it was outside the scope and only referred to non-motorised transport.

The final 8 articles were eligible for the present review in this paper.

Lastly, for "Machine learning" AND "Public transport", we identified 162,293 studies in WOS. After examining the title and abstract of each article and refining the scope of machine learning in public transport, 162,195 studies were excluded because they did not meet the eligibility for the present review. Following the guidelines of PRISMA, 98

articles were eligible for the present review in this paper. The reviewed articles during the inclusionary phase (Figure 1) were selected based on the highest cited articles in each group.

Bibliometric analysis

Bibliometric analysis is used in academic literature to provide a quantitative evaluation of published literature (Van Nunen et al. 2017). It is the best technique used to comprehend a vast amount of academic literature on a given topic or concept. Based on statistical sampling, bibliometric analysis assesses the impact of a published article relating to a study and the extent of its impact on the rest of the literature [START_REF] Soosaraei | A decade bibliometric analysis of global research on leishmaniasis in Web of Science database[END_REF]. Figure 2 shows the procedure used in conducting the bibliometric analysis. The analysis enabled this study to identify knowledge in concepts, identify major authors, their influence and overall research impact on the quality of service, public transport and machine learning. Upon completion of the search phase, data retrieved from WoS was used for the analysis using VOSviewer software version 1.6.17 (Al [START_REF] Husaeni | Bibliometric using Vosviewer with Publish or Perish (using google scholar data): From step-by-step processing for users to the practical examples in the analysis of digital learning articles in pre and post Covid-19 pandemic[END_REF]. The data was then analysed to assess the cooccurrences, citations, and co-authorships using VOSviewer as depicted in Figure 2.

The interpretations of bibliometric maps and tables produced through VOSviewer are explained in Table 2.

Table 2: overall interpretation of bibliometric analysis maps

Concept Description

Clusters Groups of items, differentiated by a different colour.

Items

Items are the objects of interest. For example, researchers, publications

Total link strength

The total strength of the connection between researchers, keywords, in cases of authorship analysis, the higher the value, the higher the co authorship Links It is the relationship between items, these may be co authorship connections with researchers, keywords, publications Network A group of items and their connections (links)

In addition to the information provided in Table 2. The illustrated circles in the Figures represent the occurrence of the concept. The bigger the size of a circle, the higher the keyword occurrence in published articles relating to the quality of services in public transport using machine learning techniques. Thus, the larger the label and circle of the keywords, the more important it is. The distance between the keywords shows the relationship between the keywords. The shorter the distance between the keywords, the stronger their relation [START_REF] Yu | A bibliometric analysis using VOSviewer of publications on COVID-19[END_REF]. The colours are used to distinguish different clusters.

VOSviewer is a software that provides a visual analysis of the literature review such as trends, research focus and hotspots [START_REF] Zhang | Worldwide Trends of Research on Periprosthetic Osteolysis: A Bibliometric Study Based on VOSviewer[END_REF][START_REF] Soegoto | A bibliometric analysis of management bioenergy research using Vosviewer application[END_REF].

Although

Results and Discussions on bibliometric analysis

Publication trends of Quality of service

The initial publication on service quality in public transport globally emerged in the early 1990s. Most of the articles focused on supplying the quality of service in public transport through scheduling to increase efficiency in bus transit for the benefit of transit companies' production growth. Over time, there was a slump, probably due to the low influence of studies. It was not until 2010 that research grew in this phenomenon due to more people researching, which simultaneously increased citations (see Figure 3). issues in public transport [START_REF] Zhou | Public transportation operational health assessment based on multi-source data[END_REF], predicting car crashes using trained algorithms [START_REF] Ijaz | A comparative study of machine learning classifiers for injury severity prediction of crashes involving three-wheeled motorized rickshaw[END_REF]) and developing warning models for bus transport users [START_REF] Liu | Forecast and early warning of regional bus passenger flow based on machine learning[END_REF]. Overall, the highest citations by countries come from countries that fall under the group of 7 organisations that rank among the wealthiest countries globally. Moreover, most of these highly cited papers are written by authors from countries in the global north. These publications may not reflect African realities making it difficult to fully comprehend the dynamics that exist in public transportation services.

Quality of service in public transport

Keywords analysis

A co-occurrence network analysis was performed and the results yielded six clusters represented by red, green, navy blue, light blue, purple and yellow colour coding (Figure 6). The keyword analysis was based on the 270 publications identified in WoS.

In the red cluster, the dominant keywords are machine learning, transport, impact, COVID 19 and big data. Interconnected with this cluster is the green cluster. The Green cluster whose dominant keywords are modelling, framework, algorithms, networks and reliability. In the current technological era, big data has been leveraged

to solve current transportation challenges cities face. As a result, machine learning has been used across various studies to predict, understand and solve emerging issues in the transportation sector [START_REF] Wei | Perspective: predicting and optimizing thermal transport properties with machine learning methods[END_REF]. Indeed, models developed using machine learning techniques have been used to predict and mimic the realities in transportation and their impacts on solving the recent COVID-19 pandemic The research in public transport is highly focused on harnessing the use of machine learning in public transport (Table 3). Firstly, De Oña et al. (2015) sought to understand the complex nature of service quality in public transport, by exploring evolutions in methodological approaches to service quality and attributes affecting service quality. Data mining and cluster analysis were the best techniques to evaluate service quality [START_REF] De Oña | Quality of service in public transport based on customer satisfaction surveys: A review and assessment of methodological approaches[END_REF]. Indeed, the use of new techniques to evaluate service quality has increased over time. [START_REF] Yuan | Bus dynamic travel time prediction: Using a deep feature extraction framework based on rnn and dnn[END_REF] used embedding network, which has important impact compared to traditional machine learning models. El [START_REF] El Mahrsi | Clustering Smart Card Data for Urban Mobility Analysis[END_REF] assess mobility patterns in public transport by using smart card data.

Full-text analysis

Through passengers clustering, they could identify passengers travelling patterns that shed light on why traffic is experienced during morning hours, and inform the operators of the number of buses needed to accommodate the customers. Likewise, Hagenauer and Helbich (2017) used machine learning classifiers to build algorithms to evaluate service quality. Islam (2020) used ubiquitous real-time passenger information (URTPI) to enhance the perceived quality of public transport service and enables travellers to make better pre-trip and en-route travel choices. URTPI is an under-researched area because it has only 2 studies. Moreover, Hu (2016); Singh and Kumar (2022) developed a framework using algorithms to predict the arrival time of buses. The use of machine learning, as stipulated in the previous sections, is used to identify, predict and understand issues in public transportation. On the other hand, during the COVID-19 pandemic, among others, public transportation health concerns rose. Thus, [START_REF] Gkiotsalitis | Periodic Optimization of Bus Dispatching Times and Vehicle Schedules Considering the COVID-19 Capacity Limits: A Dutch Case Study[END_REF] introduced an optimization model that was used to prevent the spread of COVID-19 Different attributes determine quality of service in public transport. [START_REF] Paulley | The demand for public transport: The effects of fares, quality of service, income and car ownership[END_REF] evaluate the factors that influence public transport demand in Britain. Findings revealed that fares in buses tend to increase over time, negatively affecting service quality in bus transport. Dell'oli (2011) postulates that waiting time, cleanliness, and comfort are the most influential attributes affecting modal choice for public transport users. Moreover, Dell'olli states that the most valued attributes for potential users are trip duration, waiting time and occupancy. However, the desired quality of services in public transport is dependent on a variety of issues. De [START_REF] De Oña | Perceived service quality in bus transit service: A structural equation approach[END_REF] uses a weighted mean to analyse important factors in public transport service quality. The results are that service, comfort and driver's behaviour had the highest weight and the methodology was proven through the findings to be the best in determining important attributes in public transport. [START_REF] Holmgren | Meta-analysis of public transport demand[END_REF] states car ownership, petrol prices and any variable that affects the supply determine service quality.

In addition, Gender differences in public transport have been identified globally as well. [START_REF] Salva | Role of Social Climate in Habitual Transit Use by Young Adults to Work and Leisure Activities: Evidence from Colombia and Mexico[END_REF] stipulate that gender difference exists in public transport because both males and females encounter different experiences while in transit. De Oña (2015) analysed the quality of service and travel behaviour among different groups based on their shared socio-economic profile. The findings indicate that punctuality and information availability while using public transport are held in high regard among all demographics. Nevertheless, accessibility received a lower rating from females compared to men. In a study conducted in Reggio, Calabria by [START_REF] Arabikhan | Gender-based analysis of zones of tolerance for transit service quality considering intelligent transportation systems[END_REF] focus on investigating expectations and perceptions of service quality based on gender.

Notably, women exhibits a high tolerance of poor quality of service in the realm of public transport. Often, this is associated to the societal norms as confirmed by Uteng and Turner (2019), women play more supporting roles in transportation while men are involved in decision making and policy making.

To evaluate quality of service in public transport, [START_REF] Efthymiou | Factors affecting bus users' satisfaction in times of economic crisis[END_REF] states that the improvement in service quality has attracted new users due to access to information, and service delivery. However, the attribute's importance differs over time. Fare evasion, a concept used when one uses public transport without paying, is a problem experienced by many countries. It sparked research interest as it puts constraints on bus transport, eventually affecting service quality. [START_REF] Cantillo | Fare evasion on public transport: Who, when, where and how?[END_REF] use Chile as a case study to identify the people involved and the location where fare evasions take place. Although most of the people involved in fare evasion are men, women ultimately end up suffering due to protective measures to prevent bus fare evasion. In Athens, Greece, women fare evasion heavily depends on the evaders' socio-economic profile, especially gender [START_REF] Milioti | Key drivers of fare evasion in a metro system: Evidence from Athens, Greece[END_REF]. The results indicate the gender dimensions that exist in the quality of service in public transport.

The increase in public transport usage is determined by the quality of service in public transport. Hence, [START_REF] Chen | Comparative Analysis of the Influence of Transport Modes on Tourism: High-Speed Rail or Air? City-Level Evidence from China[END_REF] argue that improving the quality of services in high-speed rail results in a positive impact on the economy. Efthymiou (2017) makes a comparison analysis of Athens's public transport demand between 2008 and 2013.

Results reveal an increase in public transport usage due to high maintenance costs of motor vehicles, increased vision for sustainability and improved services. Previous studies have focused more on efficiency in public transport for profitability [START_REF] Pullen | Definition and measurement of quality of service for local public transport management[END_REF]. However, the concern on service quality gained momentum around early 2000s. The emerging themes in research during that time shifted towards service delivery and attitudes among different passenger groups [START_REF] Morton | Customer perceptions of quality of service in public transport: Evidence for bus transit in Scotland[END_REF]. The results from the study revealed that socio-economic cohorts influences travel behaviour and satisfaction. Thus, from 2017, the research focused more on building an improved public transport system for customer satisfaction. To achieve these, different methodologies such as modelling, simulations and regression were explored to find solutions to maximise user satisfaction [START_REF] Tirachini | Estimation of crowding discomfort in public transport: Results from Santiago de Chile[END_REF][START_REF] Ingvardson | The relationship between norm, satisfaction and public transport use: A comparison across six European cities using structural equation modelling[END_REF].

Emerging trends and further research

The emerging trends and future trends from 2020 focus on machine learning, in particular, frameworks, big data, impact, optimization, city, networks, and patterns.

The emerging trends are due to the availability of big data [START_REF] Mitroshin | Big data and data mining technologies application at road transport logistics[END_REF], concern about the lived experiences of public transport users [START_REF] Yuxue | Sitting comfort analysis and prediction for high-speed rail passengers based on statistical analysis and machine learning[END_REF], and the urge to shift from car ownership to public transport to solve issues that come with increased car ownership in cities [START_REF] Goodarzian | Challenges to the sustainable development of vehicle transport[END_REF].

One study in particular by Liu (2020) leveraged a support vector machine to predict passenger flow. Results show that accurate predictions and early warning of passenger flow in a bus enabled the bus dispatchers to maintain efficient bus services. [START_REF] Aparicio | Understanding the impacts of the COVID-19 pandemic on public transportation travel patterns in the City of Lisbon[END_REF]; Bustamante et al. (2022) use machine learning to analyse travel patterns in public transport during the pandemic. The research focus does not incorporate issues on gender role in public transport. Socio-economic dynamics of a society shows a clear picture of the underlining issues. Thus, in the future more research on the gender dynamics in transportataion can be explored (Bustamante et al. 2022). Although research focusing on machine learning, service quality in public transport is increasing as discussed in this paper, there is limited research based on African cities.

Most of the emerging topics do not look at non-conventional modes of public transport like minibus taxis, yet, they form important part of mobility in African cities [START_REF] Moyo | Assessing spatial variations of traffic congestion using traffic index data in a developing city: lessons from Johannesburg, South Africa[END_REF]. Additionally, availability of big data has also opened a door for researchers to use data from social media as a reliable data source to create new application in transport, analyse and predict human behavior (Tran, 2022). Furthermore, as stated by [START_REF] Wang | Enhancing transportation systems via deep learning: A survey[END_REF] most of the research in this area should move towards integrating the use of machine learning through deep learning. Athough machine learning has done a lot of improvement in public transport system, research direction should move to harnessing the emerging AI and deep learning techniques in public transport research to find solutions and shortcomings of the public transport system [START_REF] Farazi | Application of machine learning to investigate heterogeneity in users' perception of intercity train service quality in developing countries[END_REF]. Also, there is a need to train deep reinforcement learning agent in real life scenarios within public transport system [START_REF] Farazi | Deep reinforcement learning in transportation research: A review[END_REF]

Conclusions

The paper provided a comprehensive transport literature and intensively examined literature pertaining to quality of service, gender and machine learning in public transport. This review will be useful reference for transport and evolutions of quality of service in public transport research. Research in quality of service can be dated back to 1992 (Figure 1) in public transport. Around early 2011 and 2012, issues on gender and machine learning were introduced in public transport. The machine learning techniques act as a blueprint to potentially bridge the gap between realities vs. In addition, introductions of algorithms paved the way for futuristic strategies, planning policies in public transport. Machine learning has proven to be a successful tool for mimicking and predicting realities in public transport [START_REF] Guevara | Correcting for endogeneity due to omitted crowding in public transport choice using the Multiple Indicator Solution (MIS) method[END_REF]. Horcher and Tirachini (2021) state that although machine learning and modelling have been used over time, the concepts need to be further explored in the Public transport sector.

The gender dimensions in quality of service and public transport suggest that it has not been fully incorporated, because women and men experience public transport differently. Most of the research focus on gender to investigate customer satisfaction and expectation. These demonstrate a shift in research from public transportorientated thinking to more concerns on user satisfaction [START_REF] Díez-Mesa | Bayesian networks and structural equation modelling to develop service quality models: Metro of Seville case study[END_REF].

These include understanding the socio-demographic background of both private and public transport users in understanding quality service satisfaction [START_REF] Rasca | Exploring the factors influencing the use of public transport by commuters living in networks of small cities and towns[END_REF]. It is expected that research on gender dimensions and machine learning in public transport will be expected to increase tremendously, especially amid SDGs and New Urban Agenda that strive for cities to join in the fight to achieve sustainability.

The Majority of the most influential publications are derived from countries in the global south, with only 2 articles published in Africa. The lack of research in Africa also opens a wide door for further research in the field of quality of service in public transport. The reseach will inform policy on the important attributes that users value to attract more people to use public transport.

Figure 1 :

 1 Figure 1: PRISMA flow diagram Adapted from Moher et al. (2016)

Figure 1 :

 1 Figure 1: PRISMA flow diagram

Figure 2 :

 2 Figure 2: A guide on the bibliometric analysis for this paper

  Citespace has been used in several articles (Kumar Sood et al. 2021; Nyathi et al. 2022), VOSviewer remains the primarily used software in the research fields because it can comprehend vast information while giving credible results. Transportation research have tapped into bibliometric analysis using VOSviewer software. In one study, Ceccato et al. (2021) used VOSviewer to analyse the safety of passengers during the trip duration and along transportation nodes. The study uncovered 245 articles related to safety concerns in railway modes from 1990 to 2020, which advocated for further research into passengers' safety concerns. Seemingly, Allam and Sharif (2022) studied the concept of smart mobility in western cities. The study mapped dynamics and identified thematic focus in smart mobility using VOSviewer.

Figure 3 :

 3 Figure 3: Times Cited and Publications over Time in quality of service and public transport Although there was a slight decline in 2019 in publications and citations on the topic, research on the quality of services in public transport has been growing which shows a spark of interest and investment in that field. The research has grown significantly, especially since 2015, with 10 publications. The increase in publications was during debates around sustainability that include sustainable modes of transport.

Figure 4 :

 4 Figure 4: Times Cited and Publications over Time in quality of service in public transport and genderIt can be noted that the quality of service in public transport, with special reference to gender issues, is an under-examined area of research (Figure4) globally. The

Figure 5 :

 5 Figure 5: Times Cited and Publications over Time in quality of service in public transport and Machine In addition to the gender dimension in research, machine learning has greatly impacted public transport research (Figure 5). Research in machine learning in public transport can be dated back to 2012. In 2012 researchers like Mendes-Moreira et al.(2012) focused more on using regression methods to predict travel time. The most cited body of work in this field is a study by[START_REF] Hagenauer | A comparative study of machine learning classifiers for modeling travel mode choice[END_REF], with 103 citations. The study compared the predictive performance of seven carefully chosen machine learning classifiers to analyze choice in different modes of transport using

(

  [START_REF] Gkiotsalitis | Periodic Optimization of Bus Dispatching Times and Vehicle Schedules Considering the COVID-19 Capacity Limits: A Dutch Case Study[END_REF]. A novel spatio-temporal model developed by[START_REF] Ciancia | Spatio-temporal model checking of vehicular movement in public transport systems[END_REF] detected vehicle movements and problems. The model was later used to detect faults in motor vehicle around the world.

Figure 6 :

 6 Figure 6: A Co-occurrence network analysis of keywords on quality of service in public transport researchThe third cluster assigned to the navy blue colour code is centred on user satisfaction, perceptions, expectations and attitudes in public transport. The cluster is closely related to the yellow cluster on themes such as customer satisfaction, user satisfaction, service quality, determinants and high-speed rail in public transport. The two clusters show that the experience of public transport users is important, as they help understand a public transport industry's ability to meet user expectation to improve service quality[START_REF] Joewono | User satisfaction with paratransit in competition with motorization in Indonesia: anticipation of future implications[END_REF]. Passenger satisfaction highly depends on service quality and perceived value[START_REF] De Vos | Does commute duration attenuate the effect of travel mode choice on commute satisfaction[END_REF]. Customer satisfaction is mostly explored in conventional modes of transport like buses and rail.In a bid to attract people to use high-speed rails which are the mobility options providing the least harm to the environment[START_REF] Lawrence | China's high-speed rail development[END_REF]. There are many cases where high-speed railways and Bus Rapid Transit (BRT) has positively impacted countries that have been devoted to increasing user satisfaction. For example, In China, high-speed rail has significantly boosted economic growth by

  service desired by public transport users ML-MMAS: Self-learning ant colony optimization for multi-criteria journey planning 2011 2022 by estimating expected passenger arrival time. He et al. (2022) research is on optimization to better service quality in public transport.

Figure 7

 7 Figure 7 represents the results of all existing, current and emerging trends in quality of service in public transport, gender and machine learning research from 2017 to 2020. The timelines are divided into four clusters indicated by the colour codes (navy blue, turquois blue, green and yellow).

Figure 7 :

 7 Figure 7: Emerging trends in research on the quality of service in public transport The navy blue cluster indicate the trend topics in research on quality of service in public transport from 2017 to 2018. A turquois blue cluster presents trend topics from 2018 to 2019. In addition, 2019 to 2020 are presented by a green cluster while 2020 to 2022 presented by a yellow cluster. Research from 2017-2018 focused on quality of service, contracts, performance, perceptions, behaviour, and simulation. Between 2018 and 2019, most research was based on public transport, customer satisfaction, service, models, and expectations. In 2019 to 2020, research focused on user satisfaction, perceptions, systems, travel and transport.

  practice. De Oña et al. (2021) uses machine learning techniques in European cities to assess the relationship between service quality, satisfaction and loyalty in Public transport. The study marked a milestone for European policies in putting more enthuses on service quality in public transport.

Table 1 :

 1 Inclusion and exclusion criteria for the data collection extracted from the database

	Inclusion criteria	Exclusion criteria
	All articles with topics in the quality of service in	Studies outside public transport scope
	public transport
	All articles with topics relating to gender	Unpublished thesis and dissertations
	dimensions in quality of service in public
	transport
	All articles with topics relating to machine	Newspapers, conference papers
	learning techniques used in public transport
	Language: English	Non-English language
	Countries: Looked from globally then Africa

Table 3

 3 provides insights into various research conducted in the field of quality of 335 service in public transport. The analysis is based on the most influential body of work

	336
	determined by the highest citation.

337

Table 3 :

 3 Analysis of research on the quality of service in public transport

	338
	CLUSTER	KEYWORDS

QOS & PT: (n=26, 642) QOS & PT & Gender: (n=26, 642) QOS & PT & ML: (n= 162, 293)

QOS & PT: (n=10) QOS & PT & Gender: (n=10) QOS & PT & ML: (n=0)

QOS & PT: (n=164) QOS & PT & Gender: (n=8) QOS & PT & ML: (n=98)

QOS & PT: (n=12) QOS & PT & Gender: (n=8) QOS & PT & ML: (n=12)