Fifty Years of Accident Analysis & Prevention: A Bibliometric and Scientometric Overview

A Dynamic Bayesian Network model to evaluate the availability of machinery systems in Maritime Autonomous Surface Ships

With their complex structure, multiple failure modes and lack of maintenance crew, the safety problem of Maritime Autonomous Surface Ships' (MASS) machinery systems are becoming an important research topic. The present study presents an availability model for ship machinery systems incorporating a maintenance strategy based on Dynamic Bayesian Networks (DBN). First, the availability of conventional ship machinery systems is evaluated and used as a benchmark based on the configuration and planned maintenance strategy. Secondly, the availability of MASS machinery systems is compared to the benchmark, before the introduction of any changes to the ship's configuration and planned maintenance strategy. Finally, the availability improvement strategies, including redundant designs and planned maintenance strategies at port, are proposed based on sensitivity analysis and planned maintenance cost minimization. To exemplify the model's application, a case study of a cooling water system is explored. Based on a sensitivity analysis using the model, it is possible to decide which components need to be redundant. Different redundancy designs and corresponding planned maintenance strategies can be adopted to meet the availability demand. It is also shown that redundancy and enhanced detection capabilities reduce much of the planned maintenance cost. This framework can be used in the early design stages to determine whether the MASS machinery systems' availability is at least equivalent to that of conventional ships, and has certain reference significance for redundant configuration designs and MASS planned maintenance strategy schedule.

A cooperative control method for safer on-ramp merging process in heterogeneous traffic flow

The on-ramp area is a high-risk conflict zone where traffic accidents frequently occur. Connected and automated vehicles (CAVs) have the potential to enhance the safety of the merging process through appropriate cooperative control methods. This paper proposes a cooperative control method for safer on-ramp merging processes in heterogeneous traffic flow. Firstly, the gap selection process of ramp vehicles is described, thus all feasible virtual platoon results can be summarized. Next, the vehicle bond (VB) is used to describe the connection mode between vehicles within the virtual platoon. A two-layered gap selection function is proposed to ensure a safer merging process. The first layer aims to minimize the number of empty VBs, while the second layer considers fairness with respect to delay. To evaluate the control effectiveness, time exposed time-to-collision (TET), cumulative risk (CR), and conflict-potential mergence ratio (CPMR) are selected as the safety evaluation indicators. The simulation results show that the gap selection control moves the merging positions of ramp vehicles forward, resulting less risk of merging. It significantly enhances the safety of on-ramp merging without compromising traffic efficiency. At a flow rate of 650 veh/h for both the mainline and ramp, and a CAV penetration rate of 0.1-0.9, the gap selection control group achieves a decrease rate of about 0.3-0.6 for average TET and CR compared to the non-control group. In the pure CAV environment, the decrease rate can reach about 0.9. Sensitivity analysis indicates that the gap selection control is effective across varying flow rates and steady speeds. The optimal control effect is achieved when the length of the communication area ranges from 100 to 200 m.

A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based bibliometric analysis

Introduction

Research on hepatocellular carcinoma (HCC) has grown significantly, and researchers cannot access the vast amount of literature. This study aimed to explore the research progress in studying HCC over the past 30 years using a machine learning-based bibliometric analysis and to suggest future research directions.

Methods

Comprehensive research was conducted between 1991 and 2020 in the public version of the PubMed database using the MeSH term "hepatocellular carcinoma." The complete records of the collected results were downloaded in Extensible Markup Language format, and the metadata of each publication, such as the publication year, the type of research, the corresponding author's country, the title, the abstract, and the MeSH terms, were analyzed. We adopted a latent Dirichlet allocation topic modeling method on the Python platform to analyze the research topics of the scientific publications.

Results

In the last 30 years, there has been significant and constant growth in the annual publications about HCC (annual percentage growth rate: 7.34%). Overall, 62,856 articles related to HCC from the past 30 years were searched and finally included in this study. Among the diagnosis-related terms, "Liver Cirrhosis" was the most studied. However, in the 2010s, "Biomarkers, Tumor" began to outpace "Liver Cirrhosis." Regarding the treatment-related MeSH terms, "Hepatectomy" was the most studied; however, recent studies related to "Antineoplastic Agents" showed a tendency to supersede hepatectomy. Regarding basic research, the study of "Cell Lines, Tumors,'' appeared after 2000 and has been the most studied among these terms.

Conclusion

This was the first machine learning-based bibliometric study to analyze more than 60,000 publications about HCC over the past 30 years. Despite significant efforts in analyzing the literature on basic research, its connection with the clinical field is still lacking. Therefore, more efforts are needed to convert and apply basic research results to clinical treatment. Additionally, it was found that microRNAs have potential as diagnostic and therapeutic targets for HCC.

Systematic literature review of 10 years of cyclist safety research

Cyclist safety is a research field that is gaining increasing interest and attention, but still offers questions and challenges open to the scientific community. The aim of this study was to provide an exhaustive review of scientific publications in the cyclist safety field. For this purpose, Bibliometrix-R tool was used to analyse 1066 documents retrieved from Web of Science (WoS) between 2012 and 2021. The study examined published sources and productive scholars by exposing their most influential contributions, presented institutions and countries most contributing to cyclist safety and explored countries open towards international collaborations. A keywords analysis provided the most frequent author keywords in cyclist safety shown in a word cloud with E-bike, behaviour, and crash severity representing the primary keywords. Furthermore, a thematic map of cyclist safety field drafted from the author's keywords was identified. The strategic diagram is divided in four quadrants and, according to both density and centrality, the themes can be classified as follows: 1) motor themes, characterized by high value of both centrality and density; 2) niche themes, defined by high density and low centrality; 3) emerging or declining themes, featured by low value of both centrality and density; and 4) basic themes, distinguished by high centrality and low density. The motor themes (i.e., the main topics in cyclist safety field) crash severity and bike network were further explored. The research findings will be useful to develop strategies for making bike a safer and more confident form of transport as well as to guide researchers towards the future scientific knowledge.

Current and Future Trends in Driver Behaviour and Traffic Safety Scholarship: An African Research Agenda

Road traffic accidents are strongly associated with driver behaviour. Africa, as a region, has the highest road accident fatality rate, but there is very little research dealing with this critical issue on the continent. This paper, therefore, sought to establish the state of driver behaviour and road safety scholarship in Africa to determine current research trends as well as potential future research directions. To this end, two bibliometric analyses were conducted, one which considered the issue from an African perspective and the other which considered the broader body of work. The analysis revealed a critical shortage of research related to driver behaviour in Africa. The existing body of research primarily focused on the identification of issues and tended to focus on narrow research problems within limited geographical areas. A need was identified for the collection of broader macro-level data and statistical analyses thereof to indicate regional traffic crash patterns; causes and effects; country-level studies, particularly countries with high traffic fatality rates and low levels of research; cross-country comparisons; and modelling. Future research directions should also include the link between driver behaviour, traffic safety and the sustainable developments goals, as well as policy-related research to determine current and potential future country-level policies.

A bibliometric analysis of autophagy in lung diseases from 2012 to 2021

Background

Autophagy refers to the process in which cells wrap their damaged organelles or unwanted proteins into a double-membrane structure and direct them to lysosomes for degradation. Autophagy can regulate many lung diseases such as pulmonary hypertension, acute lung injury, and lung cancer. However, few bibliometric studies on autophagy are available. The aim of the present study was to clarify the role of autophagy in lung diseases by bibliometric analysis.

Methods

Publications were retrieved from the 2012-2021 Science Citation Index Expanded of Web of Science Core Collection on 20 September 2022. Bibliometrix package in R software was used for data retrieval. VOSviewer and CiteSpace were used to visualize the research focus and trend regarding the effect of autophagy on lung disease.

Results

A total of 4,522 original articles and reviews on autophagy in lung diseases published between 2012 and 2021 were identified. China had the largest number of published papers and citations, whereas the United States (US) ranked first in the H-index and G-index. Moreover, cooperation network analysis showed close cooperation between the US, China, and some European countries, and the top 10 affiliates were all from these countries and regions. Bibliometric analysis showed that "autophagy" and "apoptosis" were the keywords with the highest frequency. During the past decade, most studies were concerned with basic research on pathways related to the regulatory role of autophagy in the inhibition and attenuation of lung diseases.

Conclusion

The study of autophagy in lung diseases is still in the development stage. The information published in these articles has helped researchers understand further the hot spots and development trends in the field more and learn about the collaboration network information regarding authors, countries, and institutions, as well as the paper citation correlation. More studies have been performed to gain deeper insights into the pathogenesis of autophagy by focusing on the links and effects between various diseases. More recently, research in this field has paid increasing attention to the function of autophagy in COVID-19-related lung diseases.

Understanding the domain of driving distraction with knowledge graphs

This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs were constructed to demonstrate bibliometric information and domain-specific research content respectively. In terms of bibliometric analysis, the evolution of publication and citation numbers reveals the accelerated development of this domain, and trends of multidisciplinary and global participation could be identified according to knowledge graphs from Vosviewer. In terms of research content analysis, a new framework consisting of five dimensions was clarified, including "objective factors", "human factors", "research methods", "data" and "data science". The main entities of this domain were identified and relations between entities were extracted using Natural Language Processing methods with Python 3.9. In addition to the knowledge graph composed of all the keywords and relationships, entities and relations under each dimension were visualized, and relations between relevant dimensions were demonstrated in the form of heat maps. Furthermore, the trend and significance of driving distraction research were discussed, and special attention was given to future directions of this domain.

Mapping the Global Evolution and Research Directions of Information Seeking, Sharing and Communication in Disasters: A Bibliometric Study

This paper aims to grasp developments and trends in research on information communication, information seeking and information sharing in disasters during 2000-2021. By using bibliometrics software CiteSpace and VOSviewer, the development trends of publications, disciplinary, journals, institutions and regional cooperation are mapped. Keyword co-occurrence analysis is used to further identify the evolution of the research hot points and visualize the research orientation and frontier. The results indicate that the field of information communication in disasters has received growing attention from various disciplines. Results of institutions and regional cooperation show that worldwide cooperation is still lacking and needs to be strengthened in future studies in this field. The key findings are five main research orientations in this field based on keyword co-occurrence, which are public information coordination research, public information behavior and perception research, health information communication research, risk communication and social media research and information technology in emergency management. The findings of this paper can be helpful for academics and emergency managers in disaster information management and risk communication by giving them a comprehensive understanding of people's information communication, seeking and sharing.

Analysis on alteration of road traffic casualties in western China from multi-department data in recent decade

Background

Road traffic safety has considerably improved in China. However, the changes may differ in the economically backward and altitude higher western region. This study aims to investigate changes in the occurrence and severity of traffic casualties in western China and illuminate several key causal factors.

Materials and methods

Traffic accident data from the Annual Traffic Accident Statistics Report combined with population and vehicle data from the China Statistics Bureau between 2009 and 2019, were retrospectively analyzed. Traffic accident numbers, fatalities, human injury (HI), case fatality rates (CFR), mortality per 100,000 population (MRP), and mortality per 10,000 vehicles (MRV) were compared between the western and eastern regions. The HI, CFR, MRV, and MRP between the four groups based on the altitude of cities, below 500 meters, 500 to 1,500 meters, 1,500 to 3,000 meters, and over 3,000 meters, were compared using one-way analysis of variance. One hundred and seventy-eight cases of extremely serious traffic accidents were further analyzed in terms of accident occurrence time, vehicle type, road grade, road shape, accident pattern, and accident reason. The differences of accident characteristics between the eastern and western regions were compared using the chi-square test.

Results

The number of traffic accidents and fatalities decreased in low-altitude areas in western China. However, there was a significant increasing trend in the high altitude area. The HI, CFR, MRV, and MRP were higher in the western region than that in the eastern and national. Those accident indicators tended to increase with increasing altitude. And there were statistically significant differences (p < 0.05) among groups from different altitudes. Chi-square test results show that there are statistically significant differences (p < 0.05) in term of road grade, road shape, accident pattern between eastern and western. Low-grade roads, combined curved and sloping roads, and rollover were significant features associated with traffic accidents in the western region. Bad roads were the main cause of rollover accidents in western China, which will lead to more serious casualties. Over speeding, overloading, bad weather, vehicle failure, and driver error were the top five accident causes.

Conclusion

Traffic accidents are increasing in high-altitude areas of western China, and they lead to more severe casualties. The characteristics of serious traffic accidents in this part of the country differ from those of the eastern regions. Improving road safety facilities, restrictions of speed, and improving medical treatment at accident scenes may be effective measures to reduce traffic accidents related injuries in the western region.

Identification and Improvement of Hazard Scenarios in Non-Motorized Transportation Using Multiple Deep Learning and Street View Images

In the prioritized vehicle traffic environment, motorized transportation has been obtaining more spatial and economic resources, posing potential threats to the travel quality and life safety of non-motorized transportation participants. It is becoming urgent to improve the safety situation of non-motorized transportation participants. Most previous studies have focused on the psychological aspects of pedestrians and cyclists exposed to the actual road environment rather than quantifying the objective safety hazards, which has led to a non-rigorous evaluation of their basic safety situation. An integrated processing approach is proposed to comprehensively and objectively evaluate the overall safety level of non-motorized transportation participants on each road segment. Our main contributions include (1) the universal approach is established to automatically identify hazard scenarios related to non-motorized transportation and their direct causing factors from street view images based on multiple deep learning models; (2) a seed points spreading algorithm is designed to convert semantic images into target detection results with detail contour, which breaks the functional limitation of these two types of methods to a certain extent; (3) The safety situation of non-motorized transportation on various road sections in Gulou District, Nanjing, China has been evaluated and based on this, a series of suggestions have been put forward to guide the better adaptation among multiple transportation participants.

Forty Years of Risk Analysis: A Scientometric Overview

Risk Analysis was first published in 1981, established with a vision to provide a platform for inquiry into fundamental risk-related concepts and theories, and to disseminate new knowledge about methods and approaches for identifying, analyzing, evaluating, managing, and communicating risk. The journal has also contributed significantly to a scientific understanding of specific risks related to human health and safety, engineering, ecological, and social systems. Published on behalf of the Society for Risk Analysis, the journal has become a leading platform over its 40-year history. Complementing recent celebratory overviews and perspectives on the evolution, achievements, and future challenges for Risk Analysis, this article presents a scientometric overview of the journal between 1981 and 2020. The study presents high-level insights in the journal publication trends and structure and trends in the leading countries/regions, institutions, and authors, in relation to their respective collaboration networks. Furthermore, the structure and evolution of research focus issues is analyzed, and highly cited publications are identified. The findings are primarily intended to provide high-level insights, which may be useful for early career academics and risk practitioners to understand the structure and development of the research domain, and its main contributors and topics, and for experienced researchers to reflect on the achievements and future developments.

Macro-level literature analysis on pedestrian safety: Bibliometric overview, conceptual frames, and trends

Due to the high volume of documents in the pedestrian safety field, the current study conducts a systematic bibliometric analysis on the researches published before October 3, 2021, based on the science-mapping approach. Science mapping enables us to present a broad picture and comprehensive review of a significant number of documents using co-citation, bibliographic coupling, collaboration, and co-word analysis. To this end, a dataset of 6311 pedestrian safety papers was collected from the Web of Science Core Collection database. First, a descriptive analysis was carried out, covering whole yearly publications, most-cited papers, and most-productive authors, as well as sources, affiliations, and countries. In the next steps, science mapping was implemented to clarify the social, intellectual, and conceptual structures of pedestrian-safety research using the VOSviewer and Bibliometrix R-package tools. Remarkably, based on intellectual structure, pedestrian safety demonstrated an association with seven research areas: "Pedestrian crash frequency models", "Pedestrian injury severity crash models", "Traffic engineering measures in pedestrians' safety", "Global reports around pedestrian accident epidemiology", "Effect of age and gender on pedestrians' behavior", "Distraction of pedestrians", and "Pedestrian crowd dynamics and evacuation". Moreover, according to conceptual structure, five major research fronts were found to be relevant, namely "Collision avoidance and intelligent transportation systems (ITS)", "Epidemiological studies of pedestrian injury and prevention", "Pedestrian road crossing and behavioral factors", "Pedestrian flow simulation", and "Walkable environment and pedestrian safety". Finally, "autonomous vehicle", "pedestrian detection", and "collision avoidance" themes were identified as having the greatest centrality and development degrees in recent years.

Mapping pedestrian safety studies between 2010 and 2021: A scientometric analysis

Pedestrian deaths constitute 23% of road traffic deaths globally. Although several research papers have contributed to pedestrian safety analysis, they did not provide a comprehensive overview of the progress in the research domain and publication trends. This makes it difficult to identify trends and insights into the pedestrian research domain in light of the voluminous number of papers. This study fills this gap with a scientometric analysis of research on pedestrian safety analysis indexed in the Web of Science. The scope covers 2594 papers published between 2010 and 2021 in English. This study analyzed the annual publications and citation trends, top ten most cited papers, influential papers in their first three years after publication, contributing authors, funding agencies, and contributing journals. The regional gaps between the proportion of pedestrian deaths and research were also analyzed. The results showed low research productivity from low and middle-income countries although they have a high incidence of pedestrian deaths. Subsequently, the main keyword clusters or frontier topics were identified and topic analysis was employed to identify the evolution of studies. Four keyword clusters were identified, i.e., "vehicle-to-pedestrian crash and injury severity analysis", "pedestrian movement and decision simulation experiments", "improving the vehicle system towards reducing body region impact injuries", "pedestrian behavior in crosswalks and signalized intersections". This study contributes an integrated knowledge map of pedestrian safety analysis, publication trends, the evolution of studies, and under-researched topics to guide future research work in pedestrian safety analysis.

Accident Prevention Analysis: Exploring the Intellectual Structure of a Research Field

Accident prevention is of great significance in avoiding or reducing all kinds of casualties and economic losses, and is one of the main challenges for social sustainable development. Hence, it has been an active research field for many decades around the world. To master the research status of accident prevention, and explore the knowledge base and hot trends, 1294 papers from the WOS retrieval platform SCIE and SSCI databases from 1990 to 2021 were selected as data samples. Co-occurrence analysis, co-citation analysis, co-authorship analysis, and keyword analysis were performed on the literature on accident prevention research with bibliometric analysis methods. The study showed that the United States ranked first in the number of publications of any country/region and Georgia Inst Technol ranked first in the number of institutional publications. System analysis and accident model establishment, analysis of construction accidents, road accident prevention, and safety culture and safety climate are the knowledge base in the accident prevention studies and the core journals in this field are Safety Science, Accident Analysis and Prevention, Pediatrics, and Reliability Engineering & System Safety. There are four major research hotspots in accident prevention studies: routine accident prevention, model-based research, systems analysis and accident prediction, and occupational safety and public health research. At present, the basic theory and structural system of accident prevention research have been basically established, with many research directions and a wide range of frontier branches. Safety management, public safety, Bayesian networks, and simulation are the research frontiers of accident prevention.

Road safety under the environment of intelligent connected vehicles

The emergence of Intelligent Connected Vehicles (ICVs) is expected to drastically change various fields in the transportation system-especially traffic safety of road users. Therefore, this special issue aims to facilitate a forum for transportation researchers fostering an exchange of research ideas and experience in traffic safety with a specific focus on operations, planning and management of ICVs. The issue contains thirty-six papers from seven different countries. Topics are classified into seven categories: (1) Driving behavior in the ICV environment; (2) Safety evaluation of ICVs; (3) ICV driving/management strategies; (4) New framework for ICV safety analysis; (5) ICV safety for vulnerable road users; (6) Perception towards ICVs; and (7) Security issues relating to ICVs. The papers are concisely introduced in this editorial. All the papers were invited to present at the International Symposium on Accident Analysis & Prevention in 2021 (ISAAP 2021) and the symposium was successfully held. The research conducted in these articles reveal challenges and future directions in the area of ICVs that include further developing novel methodologies and algorithms for collision-free trajectories of ICVs, testing diverse scenarios in complex environments with mixed traffic, and addressing inherent safety risks of specific vulnerable road users (e.g., older road users, bicyclists, riders of micro-mobility vehicles).

Highway Planning Trends: A Bibliometric Analysis

An efficient highway infrastructure network is a determining factor in promoting the socio-economic development of countries and regions. Highway planning activities are highly important because their results ensure that projects are delivered within budget, schedule, and scope. Therefore, academics and professionals have promoted various methodological and technological advances focused on improving highway planning processes, evidenced by many scientific documents that address the topic. Despite progress, few studies have focused on analyzing the state of the knowledge structure and the technological and methodological trends of the highway planning topic. Considering this gap, this study presents trends in highway planning and its knowledge structure based on a bibliometric analysis from January 2015 to September 2021. The research method is based on a bibliometric analysis composed of five main stages: (1) scope definition, (2) selection of bibliometric analysis techniques, (3) data collection, (4) bibliometric analysis execution, and (5) evidence analysis and synthesis. Information from 1703 journal papers was collected and analyzed. The findings show that the main trends of highway planning focus on life cycle analysis, computational tools, smart cities, sustainability issues, construction processes, new equipment and materials, and multi-objective optimization, among others. Thus, the findings of this study allow the reader to identify the methodological and technological trends in highway planning and their knowledge gaps to guide future studies in the field.

Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years

Machine learning and semantic analysis are computer-based methods to evaluate complex relationships and predict future perspectives. We used these technologies to define recent, current and future topics in pancreatic cancer research. Publications indexed under the Medical Subject Headings (MeSH) term 'Pancreatic Neoplasms' from January 1996 to October 2021 were downloaded from PubMed. Using the statistical computing language R and the interpreted, high-level, general-purpose programming language Python, we extracted publication dates, geographic information, and abstracts from each publication's metadata for bibliometric analyses. The generative statistical algorithm "latent Dirichlet allocation" (LDA) was applied to identify specific research topics and trends. The unsupervised "Louvain algorithm" was used to establish a network to identify relationships between single topics. A total of 60,296 publications were identified and analyzed. The publications were derived from 133 countries, mostly from the Northern Hemisphere. For the term "pancreatic cancer research", 12,058 MeSH terms appeared 1,395,060 times. Among them, we identified the four main topics "Clinical Manifestation and Diagnosis", "Review and Management", "Treatment Studies", and "Basic Research". The number of publications has increased rapidly during the past 25 years. Based on the number of publications, the algorithm predicted that "Immunotherapy", Prognostic research", "Protein expression", "Case reports", "Gemcitabine and mechanism", "Clinical study of gemcitabine", "Operation and postoperation", "Chemotherapy and resection", and "Review and management" as current research topics. To our knowledge, this is the first study on this subject of pancreatic cancer research, which has become possible due to the improvement of algorithms and hardware.

Impact of COVID-19 spread on road safety indices of Turkey

Little is known about the effect of the COVID-19 on road safety indicators (RSIs) in developing countries, and conducted studies provide limited information regarding this impact. These prompted the author to evaluate the impact of COVID-19 on RSIs in Turkey. RSIs and related indices of Turkey between 2016 and 2020 were collected. For evaluating the impact, RSIs whose 2020 measures differed significantly from the pre-COVID era were identified using the outlier detection technique and Regression analysis. K-means clustering was used to group RSIs according to their variation patterns in the study period. Results show that COVID-19 led to significant decreases in 26 RSIs, especially ones related to non-fatal road traffic injuries. COVID-19 resulted in a significant drop in road traffic crashes and related indices. Also, considerable changes in monthly and daily fatalities and injuries in 2020 were observed. Clustering results revealed that COVID-19 significantly impacts variation patterns of studied RSIs, especially ones related to non-fatal injuries. Clustering aided in identifying affected RSIs by COVID-19, which other used methods were unable to detect. COVID-19 led to significant changes in road safety indices in Turkey. Road authorities and researchers should be aware of these significant fluctuations in road safety data.

A Comprehensive Review on the Behaviour of Motorcyclists: Motivations, Issues, Challenges, Substantial Analysis and Recommendations

With the continuous emergence of new technologies and the adaptation of smart systems in transportation, motorcyclist driving behaviour plays an important role in the transition towards intelligent transportation systems (ITS). Studying motorcyclist driving behaviour requires accurate models with accurate and complete datasets for better road safety and traffic management. As accuracy is needed in modelling, motorcyclist driving behaviour analyses can be performed using sensors that collect driving behaviour characteristics during real-time experiments. This review article systematically investigates the literature on motorcyclist driving behaviour to present many findings related to the issues, problems, challenges, and research gaps that have existed over the last 10 years (2011–2021). A number of digital databases (i.e., IEEE Xplore^®, ScienceDirect, Scopus, and Web of Science) were searched and explored to collect reliable peer-reviewed articles. Out of the 2214 collected articles, only 174 articles formed the final set of articles used in the analysis of the motorcyclist research area. The filtration process consisted of two stages that were implemented on the collected articles. Inclusion criteria were the core of the first stage of the filtration process keeping articles only if they were a study or review written in English or were articles that mainly incorporated the driving style of motorcyclists. The second phase of the filtration process is based on more rules for article inclusion. The criteria of inclusion for the second phase of filtration examined the deployment of motorcyclist driver behaviour characterisation procedures using a real-time-based data acquisition system (DAS) or a questionnaire. The final number of articles was divided into three main groups: reviews (7/174), experimental studies (41/174), and social studies-based articles (126/174). This taxonomy of the literature was developed to group the literature into articles with similar types of experimental conditions. Recommendation topics are also presented to enable and enhance the pace of the development in this research area. Research gaps are presented by implementing a substantial analysis of the previously proposed methodologies. The analysis mainly identified the gaps in the development of data acquisition systems, model accuracy, and data types incorporated in the proposed models. Finally, research directions towards ITS are provided by exploring key topics necessary in the advancement of this research area.

Impact of Helmet-Wearing Policy on E-Bike Safety Riding Behavior: A Bivariate Ordered Probit Analysis in Ningbo, China

At present, Chinese authorities are launching a campaign to convince riders of electric bicycles (e-bikes) and scooters to wear helmets. To explore the effectiveness of this new helmet policy on e-bike cycling behavior and improve existing e-bike management, this study investigates the related statistical distribution characteristics, such as demographic information, travel information, cycling behavior information and riders’ subjective attitude information. The behavioral data of 1048 e-bike riders related to helmet policy were collected by a questionnaire survey in Ningbo, China. A bivariate ordered probit (BOP) model was employed to account for the unobserved heterogeneity. The marginal effects of contributory factors were calculated to quantify their impacts, and the results show that the BOP model can explain the common unobserved features in the helmet policy and cycling behavior of e-bike riders, and that good safety habits stem from long-term safety education and training. The BOP model results show that whether wearing a helmet, using an e-bike after 19:00, and sunny days are factors that affect the helmet wearing rate. Helmet wearing, evenings during rush hour, and picking up children are some of the factors that affect e-bike accident rates. Furthermore, there is a remarkable negative correlation between the helmet wearing rate and e-bike accident rate. Based on these results, some interventions are discussed to increase the helmet usage of e-bike riders in Ningbo, China.

The Public Health Governance of the COVID-19 Pandemic: A Bibliometric Analysis

The 2019 global outbreak of COVID-19 has had a huge impact on public health governance systems around the world. In response, numerous scholars have conducted research on public health governance in the context of the COVID-19 pandemic. This paper provides a bibliometric analysis of 1437 documents retrieved from the Web of Science (WoS) core collection database, with 49,695 references. It analyses the research directions, countries of publications, core journals, leading authors and institutions and important publications. The paper also summarises research trends by analysing the co-occurrence of keywords, frequently cited documents and co-cited references. It summarises the global responses to COVID-19, including public health interventions and a range of supporting policies based on the features and impacts of the COVID-19 pandemic. The paper provides comprehensive literary support and clear lines of research for future studies on the governance or regulation of public health emergencies.

Mapping the knowledge domain of the evolution of emergy theory: a bibliometric approach

Bibliometric and visualization tools are effective for uncovering information contained in vast amounts of literature data. We retrieved publications from the Web of Science (2008-2020) and used the Statistical Analysis Toolkit for Informetrics (SATI) and CiteSpace to conduct a bibliometric analysis of emergy studies, review the focus of the studies, summarize current research, and identify academic trends. The SATI-based charts provided an overall description of yearly published articles, literature sources, high-frequency keywords, authors, institutions, and highly cited papers. CiteSpace was applied to determine the frontiers and hotspots of emergy theory. A dual-map analysis was used to reveal the connection between multiple disciplines. Keyword clustering analysis was performed to evaluate research hotspots. Moreover, burst analysis and timeline maps presented a holistic picture of cutting-edge issues and the evolutionary trajectory of emergy studies in recent years. An alluvial flow map analysis of the landmark literature depicted the transmission of knowledge in the emergy field. In addition, this article also evaluated the current status of collaborative research in emergy-related fields. The results can provide a reference for researchers to explore hot issues or target a specific field of study.