Knowledge Mapping with CiteSpace, VOSviewer, and SciMAT on Intelligent Connected Vehicles: Road Safety Issue

Abstract

The rapid development of the Intelligent connected vehicle (ICV) industry has stimulated technological innovation in energy and communication while also highlighting the need for effective policies and road safety measures. Understanding and addressing road safety issues in the context of ICVs can contribute to ICV development and safe driving. This paper employs a knowledge mapping approach to scientifically and intuitively demonstrate research on the road safety issues of ICV over the last decade. By utilizing bibliometric tools such as CiteSpace, VOSviewer, and SciMAT, a total of 3661 original articles from the Web of Science are examined to explore three aspects. Firstly, the study investigates the collaborative relationships among authors and institutions within the industry. Secondly, it summarizes major research topics by analyzing and clustering keywords. Lastly, the paper identifies research hotspots and predicts future research directions. The findings reveal a dynamic field characterized by close collaboration among diverse institutions, with China and the United States emerging as the most active countries and mathematics and computer science journals becoming mainstream. According to three bibliometric tools, the research topics primarily revolve around three areas: Vehicular ad hoc Networks (VANET), intelligent transportation systems (ITS), and network security. Machine learning and V2X communication are predicted to be essential research topics in the next stage. Research on traffic accidents still has potential as the number of ICVs increases.

1. Introduction

With the advancement of science and technology, transportation tools have become integral to people’s daily lives. While vehicles have brought great convenience, the issue of traffic safety in modern society is garnering increasing attention. Road traffic crashes cause nearly 1.3 million preventable deaths and an estimated 50 million injuries each year worldwide, making them the world’s leading killer of children and young people. As predicted, traffic crashes will cause an additional 13 million deaths and 500 million injuries during the next decade and encumber the sustainable development of low- and middle-income countries [1]. These alarming statistics emphasize the need to find suitable methods to ensure road safety [2]. In recent years, the rapid development of technologies in intelligent connected vehicles (ICVs) [3] has led to a surge in the demand for autonomous driving in various industries. ICVs are expected to revolutionize transportation by enhancing safety, reducing pollution, and providing a more enjoyable travel experience [2]. At present, in order to improve the safety and comfort of intelligent vehicles, ICVs not only need to directly sense the environment and make decisions but also be able to cooperate and respond effectively, showcasing the advantages of multi-vehicle intelligence through vehicle-to-vehicle communication and coordination. However, compared with traditional cars, intelligent vehicles present numerous security challenges, such as network communication security [4], data security [5], driving safety [6], legal issues [7], etc. Consequently, an increasing number of scholars have been focusing on the road safety issues associated with ICVs. Their studies span a significant timeframe and encompass a wide range of topics, indicating the interdisciplinary nature of this field’s development.

This paper aims to conduct a visual bibliometric analysis of studies on the road safety issue of ICVs from 2013 to 2022. By collecting and analyzing existing research, the goal is to explore the current state of the art, research hotspots, and future research directions in this area, thus helping subsequent researchers to check existing results and fill vacancies.

To sort out a systematic and comprehensive understanding of the subject matter in the jumble of existing research, traditional review methods are no longer sufficient. Traditional reviews heavily rely on qualitative research conducted by experts in the field based on their knowledge and understanding of the discipline, which can lead to omissions and subjective biases. Also, these review articles often follow a single thread and are limited in scope. For new researchers entering the discipline, it is difficult to accurately identify key documents amidst the extensive literature, leading to time-consuming and difficult reading, especially when trying to keep up with new research. Knowledge mapping, a bibliometric approach, on the other hand, uses technological means like data mining, which can effectively avoid the aforementioned problems and provide a more objective and accurate reflection of the history of the subject [8]. With the use of different software, knowledge mapping allows for comprehensive coverage of research on the related topic within a short timeframe, offering interdisciplinary and intuitive results. Some software even indicates research landmarks and predicts research trends. CiteSpace, developed in JAVA, is the most widely used visual analysis tool in bibliometrics. It offers a stable running environment and precise computational results. It incorporates various well-established algorithms that can perform cluster analysis and attribute computation, while its powerful visual editing functions enable researchers to customize map layouts, backgrounds, font colors, and scales as needed. Another useful tool is VOSviewer, which provides a specific demonstration of fixed data input patterns, including grid charts and density maps. The software requires minimal adjustments, primarily involving setting a threshold. The third visual analysis tool is SciMAT, which excels at mapping strategic coordinate diagrams. This tool analyzes thematic evolution over different periods, assigning density and betweenness centrality values to theme clusters. Strategic coordinate diagrams reflect the research focus of related fields and trends in research directions.

Each bibliometric tool has its strengths. With the integration of three bibliometric tools, researchers can analyze and organize the knowledge structure of road safety issues related to ICVs from different perspectives, understand the basic information, identify the research focus, excavate the research hotspots, analyze the venation of thematic evolution, and predict the potential research direction so that they can provide a reference for follow-up research of this kind.

2. Materials and Methods

2.1. Data Resource and Search Strategy

When compared to other databases such as Science Direct and Google Scholar, the Web of Science Core Collection (WoSCC) stands out as one of the most widely used databases, offering a broader range of data. Besides, the data format obtained from WoSCC can be directly read and analyzed by multiple bibliometric tools. Therefore, after conducting a comparative analysis of results obtained from various databases, WoSCC was selected as the data source for this paper.

The searching keywords are: intelligent connected vehicle, safety, road accident, and extension words include: intelligent connected vehicle $, intelligent and connected vehicle $, intelligent connected car $, intelligent networked automobile $, intelligent networked vehicle $, safe *, secur *, traffic accident $, traffic accident $, traffic crashes $, and Highway accident $. The symbol “*” represents the omission of one or more letters, while “$” represents no omission or the omission of one letter. The search was conducted within the timeframe of 2013 to 2022, resulting in the initial retrieval of n = 4079 articles. After excluding letters, conference abstracts, and less relevant papers and adding the restrictive conditions of the paper, review paper, and proceeding paper, the final number of retrieved articles amounted to n = 3661. The search strategy is TS = ((intelligent connected vehicle $ OR intelligent and connected vehicle $ OR intelligent connected car $ OR intelligent networked automobile $ OR intelligent networked vehicle $) AND ((safe * OR secur *) OR (traffic accident $ OR road accident $ OR traffic crashes OR highway accident $))). All the information from the 3661 articles, including titles, authors, abstracts, contents, references, etc., is derived in plain text and generated into the datasets used for the study.

The detailed process of this study is shown in Figure 1.

2.2. Bibliometric Analysis

2.2.1. Document Characteristic Analysis

This section encompasses two primary components. Firstly, it includes a descriptive analysis of the retrieved literature, including the number of publications per year, name of authors, name of institution, name of the journal, etc. Secondly, the collaborative network analysis, an in-depth discussion of complex interpersonal relationships in authors’ information using anthropological and sociological methods based on the theory of social network analysis, is conducted herein to present the collaborative relationships of 3661 documents. When literature is jointly published by multiple authors, institutions, and countries, cooperative relationships exist at three levels. The cross-over and overlap of multiple cooperative relationships will form a cooperative network. By utilizing VOSviewer to extract literature data and conduct the analysis, the authors’ network, institutions’ network, and countries’ network can be visually represented. These visualizations serve as intuitive reflections of the collaborative relationships in terms of intensity and extent.

2.2.2. Co-Word Analysis

Co-word analysis focuses on identifying recurring words or phrases across different articles. The research aims to identify different research themes in the field of ICV road safety through co-word analysis and generate a co-word network according to the frequency of the words. The study will provide a direct basis for researchers to understand the research situation of different subjects and topics in this field. By employing VOSviewer, visualizations of keyword networks and keyword density can be obtained in this section.

2.2.3. Co-Citation and Cluster Analysis

CiteSpace is used to perform cluster analysis on the data, grouping literature with similar keywords into the same cluster. Subsequently, the literature within each cluster is further analyzed to obtain sets of cited literature and citing literature. Cited literature refers to the existing research that is cited by other articles, while citing literature refers to new articles that cite references within the cluster. The cited literature represents the research base within the cluster, while the citing literature reflects the research frontiers. Unlike citation counts found on various websites, which may not be relevant, the citing and cited reference counts calculated by the software indicate the correlation of articles inside the cluster. When two or more references are cited in the same paper, it is called “co-cited.” The combination of a timeline with co-citation clusters can reflect the evolution of different themes in this field.

The G-index algorithm of CiteSpace 5.8. R1 can calculate the betweenness centrality (SIGMA value) of the literature, which represents the importance of the literature in the knowledge structure in the related field. A higher betweenness centrality value indicates a higher frequency of citations (both cited and citing), positioning the literature closer to the central position of a knowledge structure and suggesting its influence as a significant document. Also, the addition of a timeline can reflect the hotspot in different periods. The identification of high betweenness centrality values during different periods can be achieved through the burst result query module of CiteSpace.

2.2.4. Theme Evolution Analysis

The theme evolution analysis serves two main purposes. Firstly, it aims to obtain research themes and changes in the field of ICV road safety for different periods from 2013 to 2022. Additionally, a theme network, also known as the thematic evolution map, will be depicted based on keyword density and betweenness centrality. Secondly, it aims to figure out the hot topics and analyze the emerging research themes with development potential in different periods. The final representation is the strategy diagram. In the diagram, large nodes represent themes with high density and high betweenness centrality, which also imply research hotspots at that period. To achieve their goals, the researchers carried out cluster analysis on the co-occurrence of keywords in the literature using SciMAT (v1.1.04).

Based on the number of publications each year, the researchers divided the time into four intervals: 2013–2014, 2015–2017, 2018–2020, and 2021–2022. Although the number of published papers in 2013 and 2014 was relatively small, it still exceeded 100. The number of publications per year approximately doubled from 2015 to 2017. There was a sudden increase in the number of publications in 2018, and this trend continued until 2020. Another sudden increase in publications occurred in 2021. The interval partitions help to explore the reasons for the change in the number of publications. The other parameters are set as follows: words as the unit of analysis (author and source); 1, 1, 1 as the threshold for data frequency reduction in each period; the co-occurrence is in matrix form; 1, 1, 1 as the threshold for data network reduction in each period; association strength is used as a similarity measure to normalize the network; the simple centrality algorithm is used as the clustering algorithm. The bibliometric measures are selected for document counts, which are computed using the core document mapper. Jaccard’s Index and Inclusion Index are chosen as measures for longitudinal maps.

3. Results

3.1. Documents Characteristic Analysis

3.1.1. Number of Publications

From 2013 to 2022, a total of 3661 papers related to ICV road safety have been published, including more than 1300 papers presented at various international conferences. The total number of papers is annually growing, reaching a peak in 2021 with 726 papers; the number of articles published in 2018 is 444, which is a leap from 2017; the tendency indicates the number of publications in 2022 (the whole year) is expected to exceed that in 2021, which reflects the rapid development and excellent trend of scientific research in this field in recent years (Figure 2).

3.1.2. Networks

From 2013 to 2022, the country with the largest number of publications in the field of ICV road safety is China (1113), followed by the United States (547), India (497), and Canada (228). The cooperation among countries is extensive and close, showing nice development and good prospects. Therein, the cooperation between China and the United States is the most prominent (Figure 3).

According to the institution network visualization (Figure 4), institutions with the largest number of publications are mainly universities. In terms of the number of publications, Tsinghua Univ, Beijing Univ Posts & Telecommun, Univ Waterloo, Univ Ottawa, Xidian Univ, Beijing Jiaotong Univ, and Tongji Univ are the top seven, with Tsinghua University posting the most (61 papers). In terms of reference frequency, Univ Waterloo, Chinese Acad Sci, Univ Berkeley, Beijing Univ Posts & Telecommun, and Univ Oslo are the top five. From the perspective of Total Link Strength, Chinese Acad Sci, Univ Waterloo, Dalian Univ Technol, King Saud Univ, Xidian Univ and Tsinghua Univ perform well. It is worth noting that the University of Waterloo performs well in all three aspects, indicating the relevant research from this university is likely to be in a leading position in this field. In addition, Tsinghua Univ, Beijing Univ Posts & Telecommun, Chinese Acad Sci, and Xidian Univ are at the top in two of the three aspects, which suggests these institutions are the key ones to conduct ICV accident and safety research. The atlas shows that inter-institutional cooperation is extensive and close, and the intensity distribution of inter-institutional cooperation is uniform, which indicates that inter-institutional cooperation in this field is developing well (Figure 4).

The authors’ network visualization (Figure 5) reflects Azzedine Boukerche, Neeraj Kumar, Wei Wang, Joel J. P. C. Rodrigues, Muhammad Awais Javed, and Sudeep Tanwar as the six authors that have published more than fourteen articles in the past ten years, which ranked tops in the number of publications. Among them, Azzedine Boukerche has published thirty articles and ranks first, and Neeraj Kumar ranks second with 26 articles. In terms of citation, Yan Zhang, Neeraj Kumar, Mario Gerla, Sherali Zeadally, and Kan Zheng are cited more than 500 times each, among which Yan Zhang is cited 787 times, ranking at the top, and Neeraj Kumar ranks second with 728 times. In terms of Total Link Strength, Neeraj Kumar, Alexandre Santos, Fabio Goncalves, and Joaquim Macedo perform excellently. The figure suggests that the overall cooperative relationship among authors is good but not good enough to form a closed circle, and the local cooperation of each node is close, showing multiple scholastic groups (Figure 5).

3.1.3. Dual-Map Overlay of Journals

The dual-map overlay of journals is used to determine representative journals in the field by calculating the data relationship between cited and citing articles. On the one hand, it reflects the main research in the field. On the other hand, it serves as a guide for future researchers to contribute their results. In the dual-map overlay (Figure 6), the left-hand side represents cited journals, and the right-hand side represents citing journals. Journals of different disciplines are connected by paths in different colors, i.e., articles in one field on the right side are cited by journals in different fields on the left side. Red represents the field of mathematics and systems; purple represents the field of physics, materials, and chemistry; and blue represents the fields of psychology, education, and health. According to the map, the fields of mathematics and systems and systems and computing are connected by a thick red line, which shows prominent citation relationships, indicating computer programming and mathematics are well-developed disciplines and are the focuses of the current study. Meanwhile, the names of the journals are shown in the corresponding colors on a green background. As the map shows, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Vehicular Technology, and IET Intelligent Transport Systems are the representative citing journals, while IEEE Communications Magazine, IEEE Communications Surveys and Tutorials, AD HOC Networks, and IEEE Wireless Communications are the representative cited journals. The purple and green paths reflect physics, materials, chemistry, and health and safety, which are also vital disciplines in this field. Sensors, IEEE Sensors Journal, and Accident Analysis and Prevention are the representatives of citing journals in these directions, and Sensors-Basel, IEEE Transactions on Vehicular Technology, and Transportation Research part C—Emerging Technologies are the representatives of the cited journals. Submission of subsequent study results in this area can be aimed at the aforementioned journals (Figure 6).

3.2. Co-Word Analysis

Two graphs, a density map and a network map, are used for co-word analysis. The principle is based on the computation of co-words in literature. In the density map (Figure 7), the more frequently a keyword appears, the higher the density, which is shown in red. On the contrary, keywords with low density are shown in light green or light blue in the figure. The density map indicates that keywords in ICV road safety are concentrated, and the prominent keywords include Security (413 times), VANET (325 times), Intelligent Transportation Systems (279 times), and the Internet (221 times). Based on the density analysis, establishing the correlations between keywords produces a keyword network map (Figure 8). The network map reflects a clear keyword network, clear emphases, and close and stable correlations among keywords in the field. There are five major classes of keywords: Security, Vehicular ad hoc Networks (VANETs), Intelligent Transportation Systems, Internet of Things, and Cloud Computing (Figure 7 and Figure 8).

3.3. Co-Citation and Cluster Analysis

3.3.1. Citing Articles and Cited References

CiteSpace identifies the major clusters of research in the field by analyzing the literature citation data. The analysis obtained more than 30 clusters, including 15 main clusters and 779 inter-cluster nodes. The modularity of the clusters, represented by Q = 0.6825, indicates an outstanding inter-cluster network structure as Q > 0.3. The silhouette of all clusters, with a value of S = 0.8541, falls within the range of 0.5 < S < 1, indicating a reasonable intra-cluster similarity. These values demonstrate excellent clustering that is capable of analysis. For each cluster, major words from the titles of the cited literature are extracted as labels. For example, in

Table 1. Cluster analysis.

Cluster-ID	Size	Silhouette	Mean (Year)	Label (LLR)
0	114	0.785	2015	vehicular cloud computing; using VANET; data management perspective
1	67	0.905	2020	trust management; using blockchain; security privacy
2	66	0.853	2020	learning-based resource allocation; machine learning
3	65	0.792	2018	intrusion detection system; using fog computing; routing protocol
4	62	0.932	2013	VANET system; back-off mechanism; vehicular networking perspective
5	60	0.863	2018	key agreement protocol; efficient conditional privacy-preserving authentication scheme
6	54	0.804	2019	v2i communication; architectural design
7	50	0.858	2019	controller area network; analyzing cyberattack effect
8	44	0.874	2019	convolutional neural network; vehicular network
9	32	0.907	2018	popular smart vehicle; cpss-based approach; vehicular social network
10	28	0.935	2016	automated vehicle
11	22	0.959	2014	taxonomy challenge; low-overhead link quality assessment
12	20	0.92	2017	security qo; multihop data dissemination
13	18	0.991	2021	reconfigurable intelligent surface; irs-assisted secure uav transmission
14	15	0.985	2016	adversary-oriented overview; collaborative multi-hop vehicular communication

, the most cited literature in Cluster 0 contains VEHICULAR CLOUD COMPUTING, USING VANET, etc. All 15 clusters have good structure and high similarity values. Cluster 0 (VEHICULAR CLOUD COMPUTING) is the largest; Cluster 1 (TRUST MANAGEMENT), Cluster 2 (LEARNING-BASED RESOURCE ALLOCATION), and Cluster 3 (INTRUSION DETECTION SYSTEM) are relatively large clusters in the co-citation analysis and are the main clusters in the field of study (Table 1 and Figure 9).

The timelines of co-citation clusters (Figure 10) depict each cluster as a line, with the size of the cluster during the corresponding period indicated by the size of the dots. Cluster 0 (VEHICULAR CLOUD COMPUTING) emerges earliest, around 2014–2015, as a large cluster, indicating the early research on ICV road safety focused on vehicular cloud computing and related themes. Cluster 1 (TRUST MANAGEMENT), Cluster 2 (LEARNING-BASED RESOURCE ALLOCATION), Cluster 6 (V2I COMMUNICATION), Cluster 7 (CONTROLLER AREA NETWORK), Cluster 8 (CONVOLUTIONAL NEURAL NETWORK), and Cluster 13 (RECONFIGURABLE INTELLIGENT SURFACE) emerged in later periods. Since 2019, the main research in this field has revolved around automotive information communication and intelligent control technology. Cluster 2 (LEARNING-BASED RESOURCE ALLOCATION), Cluster 6 (V2I COMMUNICATION), and Cluster 7 (CONTROLLER AREA NETWORK) are the representatives. In addition, the information security of smart vehicle users is attracting increasing attention as the techniques improve. Cluster 1 (TRUST MANAGEMENT) and Cluster 5 (KEY AGREEMENT PROTOCOL) are the representatives (Figure 10).

The top five clusters will be discussed in this study. For each of them, major cited and citing literature will be analyzed to determine the research base and research frontier, and the top five cited and citing literature will be listed in this section.

• Cluster#0 VEHICULAR CLOUD COMPUTING

Cluster 0 contains wording like VEHICULAR CLOUD COMPUTING, USING VANET, DATA MANAGEMENT PERSPECTIVE and so on.

The most frequently cited work in cluster 0 is by Al-sultan S., et al. (2014) [9], which overwhelmingly dominates the field of research. Other cited literature has a frequency of no more than half. Whaiduzzaman M. (2014) [10] is cited 40 times and ranked second. Mejri M. (2014) [11] and Zeadally S. (2012) [12] are the third, and each has been cited more than 30 times. Engoulou R. (2014) [13] is ranked fifth. The top five citing authors are ILARRI, S. (2015) [14], SAINI, M. (2015) [15], ZEKRI, A. (2018) [16], ZHENG, K. (2015) [17], and MACHARDY, Z. (2018) [18], of whom the top two cover 26% and 24% of the literature in this cluster, respectively (

Table 2. Cited references and citing articles from Cluster #0.

Cited References		Citing Articles
Cites	Author, Year, Journal, Volume, Page	Coverage%	Author (Year) Title
80	Al-sultan S, 2014, J NETW COMPUT APPL, 37, 380 [9]	26	ILARRI, S (2015.0) A data management perspective on vehicular networks. IEEE Communications Surveys and Tutorials, V17, P41 DOI 10.1109/COMST.2015.2472395 [19]
40	Whaiduzzaman M, 2014, J NETW COMPUT APPL, 40, 325 [10]	24	SAINI, M (2015.0) How close are we to realizing a pragmatic VANET solution? a meta-survey. ACM Computing Surveys, V48, P40 DOI 10.1145/2817552 [15]
31	Mejri M, 2014, VEH COMMUN, 1, 53 [11]	18	ZEKRI, A (2018.0) Heterogeneous vehicular communications: a comprehensive study. AD HOC Networks, V75-76, P28 DOI 10.1016/j.adhoc.2018.03.010 [16]
31	Zeadally S, 2012, TELECOMMUN SYST, 50, 217 [12]	15	ZHENG, K (2015.0) Heterogeneous vehicular networking: a survey on architecture, challenges, and solutions. IEEE Communications Surveys and Tutorials, V17, P20 DOI 10.1109/COMST.2015.2440103 [17]
28	Engoulou R, 2014, COMPUT COMMUN, 44, 1 [13]	14	MACHARDY, Z (2018.0) V2x access technologies: regulation, research, and remaining challenges. IEEE Communications Surveys and Tutorials, V20, P20 DOI 10.1109/COMST.2018.2808444 [18]

).

• Cluster#1 TRUST MANAGEMENT

The cited literature in Cluster 1 contains TRUST MANAGEMENT, USING BLOCKCHAIN, and SECURITY PRIVACY in their titles.

Yang Z. (2019) [20] is the top-cited article in this cluster and has been cited 40 times. Dorri A. (2017) [21] has been cited 33 times and ranked second. The top 3 to 5 are equally cited. They are Kang J. (2019) [22], Kang J. (2019) [23], and Lu Z. (2019) [24]. As for citing articles, MIKAVICA, B. (2021) [25] covers 28% of related literature. The other four top citing literature each cover approximately 20% of the references in cluster 1. They are ABBAS, S. (2021) [26], JABBAR, R. (2022) [27], JAN, S. (2021) [28], and SILVA, L. (2021) [29] (

Table 3. Cited references and citing articles from Cluster#1.

Cited References		Citing Articles
Cites	Author, Year, Journal, Volume, Page	Coverage%	Author (Year) Title
40	Yang Z, 2019, IEEE Internet Things, 6, 1495 [20]	28	MIKAVICA, B (2021.0) Blockchain-based solutions for security, privacy, and trust management in vehicular networks: a survey. Journal of Supercomputing, V77, P56 DOI 10.1007/s11227-021-03659-x [25]
33	Dorri A, 2017, IEEE COMMUN MAG, 55, 119 [21]	23	ABBAS, S (2021.0) Blockchain-based authentication in the internet of vehicles: a survey. SENSORS, V21, P42 DOI 10.3390/s21237927 [26]
27	Kang J, 2019, IEEE Internet Things, 6, 4660 [22]	22	JABBAR, R (2022.0) Blockchain technology for intelligent transportation systems: a systematic literature review. IEEE Access, V10, P37 DOI 10.1109/ACCESS.2022.3149958 [27]
27	Kang J, 2019, IEEE T VEH TECHNOL, 68, 2906 [23]	21	JAN, S (2021.0) A survey on privacy-preserving authentication schemes in VANETs: attacks, challenges and open issues. IEEE Access DOI 10.1109/ACCESS.2021.3125521 [28]
27	Lu Z, 2018, IEEE Access, 6, 0 [24]	21	SILVA, L (2021.0) Computing paradigms in emerging vehicular environments: a review. IEEE-CAA Journal of Automatica Sinica DOI 10.1109/JAS.2021.1003862 [29]

).

• Cluster#2 LEARNING-BASED RESOURCE ALLOCATION

Cluster 2 contains wordings such as LEARNING-BASED RESOURCE ALLOCATION and MACHINE LEARNING.

Tang F. (2020) [30] is the most cited article in the cluster, having been cited 42 times. The second most frequently cited article is He Y. (2018) [31], with 25 citations. The other three top literature cited more than 20 times are Machardy Z. (2018) [18], Molina-masegosa R. (2017) [32], and Naik G. (2019) [33]. The top two citing articles are LAMSSAGGAD, A. (2021) [34] and TANG, F. (2021) [35]. The former cites 23% of the cluster, and the latter cites 19% of the cluster. Other top citing articles that cover less than 15% are SILVA, L. (2021) [29], BANGUI, H. (2021) [36], and YANG, Y. (2019) [20] (

Table 4. Cited references and citing articles from Cluster#2.

Cited References		Citing Articles
Cites	Author, Year, Journal, Volume, Page	Coverage%	Author (Year) Title
42	Tang F, 2020, P IEEE, 108, 292 [30]	23	LAMSSAGGAD, A (2021.0) A survey on the current security landscape of intelligent transportation systems. IEEE Access DOI 10.1109/ACCESS.2021.3050038 [34]
25	He Y, 2018, IEEE T VEH TECHNOL, 67, 44 [31]	19	TANG, F (2021.0) Comprehensive survey on machine learning in the vehicular network: technology, applications and challenges. IEEE Communications Surveys and Tutorials, V23, P31 DOI 10.1109/COMST.2021.3089688 [35]
23	Machardy Z, 2018, IEEE COMMUN SURV TUT, 20, 1858 [18]	14	SILVA, L (2021.0) Computing paradigms in emerging vehicular environments: a review. IEEE-CAA Journal of Automatica Sinica DOI 10.1109/JAS.2021.1003862 [29]
23	Molina-masegosa R, 2017, IEEE VEH TECHNOL MAG, 12, 30 [32]	12	BANGUI, H (2021.0) A hybrid machine learning model for intrusion detection in VANET. Computing, V104, P29 DOI 10.1007/s00607-021-01001-0 [36]
22	Naik G, 2019, IEEE Access, 7, 70169 [33]	12	YANG, Y (2019.0) Emerging technologies for 5 g-enabled vehicular networks. IEEE Access DOI 10.1109/ACCESS.2019.2954466 [20]

).

• Cluster#3 INTRUSION DETECTION SYSTEM

References in cluster 3 have wordings such as INTRUSION DETECTION SYSTEM, USING FOG COMPUTING, ROUTING PROTOCOL and so on in their titles.

In this cluster, Hasrouny H. (2017) [37] has been cited 69 times and ranked number one. The second-ranked cited reference is Sakiz F. (2017) [38], with 50 citations. Other top-cited literature (less than 35 times) is by Cunha F. (2016) [39], Manvi S. (2017) [40], and Li W. (2016) [41]. The top three citing articles are WANG, X. (2019) [42], SHARMA, S. (2019) [43], and LAMSSAGGAD, A. (2021) [34], each with coverage of 26%, 21%, and 18%, respectively. Both HUSSAIN, R. (2019) [44] and ZEKRI, A. (2018) [16], with coverage of 13%, are ranked in the top four and five (

Table 5. Cited references and citing articles from Cluster#3.

Cited References		Citing Articles
Cites	Author, Year, Journal, Volume, Page	Coverage%	Author (Year) Title
69	Hasrouny H, 2017, VEH COMMUN, 7, 7 [37]	26	WANG, X (2019.0) Privacy-preserving content dissemination for vehicular social networks: challenges and solutions. IEEE Communications Surveys and Tutorials, V21, P32 DOI 10.1109/COMST.2018.2882064 [42]
50	Sakiz F, 2017, AD HOC NETW, 61, 33 [38]	21	SHARMA, S (2019.0) A survey on internet of vehicles: applications, security issues & solutions. Vehicular Communications, V20, P44 DOI 10.1016/j.vehcom.2019.100182 [43]
32	Cunha F, 2016, AD HOC NETW, 44, 90 [39]	18	LAMSSAGGAD, A (2021.0) A survey on the current security landscape of intelligent transportation systems. IEEE Access DOI 10.1109/ACCESS.2021.3050038 [34]
30	Manvi S, 2017, VEH COMMUN, 9, 19 [40]	13	HUSSAIN, R (2019.0) Integration of VANET and 5 g security: a review of design and implementation issues. Future Generation Computer Systems -the International Journal of Escience, V101, P22 DOI 10.1016/j.future.2019.07.006 [44]
28	Li W, 2016, IEEE T INTELL TRANSP, 17, 960 [41]	13	ZEKRI, A (2018.0) Heterogeneous vehicular communications: a comprehensive study. AD HOC Networks, V75-76, P28 DOI 10.1016/j.adhoc.2018.03.010 [16]

).

• Cluster#4 VANET SYSTEM

Cluster 4 has references entitled with wordings such as VANET SYSTEM, BACK-OFF MECHANISM, and VEHICULAR NETWORKING PERSPECTIVE.

The most frequently cited reference in cluster 4 is Sommer C. (2011) [45], which has been cited 22 times. Other references are cited less than 20 times. Top 2 to top 5 are Behrisch M. (2011) [46], Gozalvez J. (2012) [47], Hartenstein H. (2008) [48], and Tonguz O. (2010) [49]. As for citing articles, ALSABAAN, M. (2013) [50], which is a survey on the environmental vehicular network, is the only one with more than 10% citations in this cluster. Other articles each have less than 10% coverage in citation cluster 4, and the top 2 to top 5 are STANICA, R. (2014) [51], JOERER, S. (2014) [52], SANTA, J. (2013) [53], and GHOSH, A. (2014) [54] (

Table 6. Cited references and citing articles from Cluster#4.

Cited References		Citing Articles
Cites	Author, Year, Journal, Volume, Page	Coverage%	Author (Year) Title
22	Sommer C, 2011, IEEE T MOBILE COMPUT, 10, 3 [45]	11	ALSABAAN, M (2013) Vehicular networks for a greener environment: a survey. IEEE Communications Surveys and Tutorials, V15, P17 DOI 10.1109/SURV.2012.101912.00184 [50]
11	Behrisch M, 2011 [46], P 3 INT C ADV SYST S, 0, 63	8	STANICA, R (2014) Reverse back-off mechanism for safety vehicular ad hoc networks. AD HOC Networks, V16, P15 DOI 10.1016/j.adhoc.2013.12.012 [51]
10	Gozalvez J, 2012, IEEE COMMUN MAG, 50, 176 [47]	7	JOERER, S (2014) A vehicular networking perspective on estimating vehicle collision probability at intersections. IEEE Transactions on Vehicular Technology, V63, P11 DOI 10.1109/TVT.2013.2287343 [52]
10	Hartenstein H, 2008, IEEE COMMUN MAG, 46, 164 [48]	7	SANTA, J (2013) Comprehensive vehicular networking platform for v2i and v2v communications within the walkie-talkie project. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS DOI 10.1155/2013/676850 [53]
9	Tonguz O, 2010, IEEE WIREL COMMUN, 17, 47 [49]	6	GHOSH, A (2014) Exploring efficient seamless handover in VANET systems using network dwell time. EURASIP JOURNAL ON Wireless Communications AND NETWORKING DOI 10.1186/1687-1499-2014-227 [54]

).

3.3.2. Citation Bursts

CiteSpace is capable of identifying bursts of frequent citations within a specific period and assigning burst value to the corresponding literature. A higher burst value indicates a higher citation frequency for that literature. By synthesizing the literature information, such as the title, authors, time of publication, time of burst, and burst duration, researchers can gain insights into the research focus or hotspot at different stages. The results show that the top five bursts are Al-sultan S (2014) [9], Zeadally S (2012) [12], Karagiannis G (2011) [55], Kenney J (2011) [56], and Whaiduzzaman M (2014) [10], which are all original research papers (

Table 7. Ten References with high citation bursts.

Ref.	Title	Burst	Year	Duration
Al-sultan S, 2014 [9]	Al-sultan S, 2014, J NETW COMPUT APPL, V37, P380, DOI 10.1016/j.jnca.2013.02.036, DOI	21.43	2014	2015–2018
Zeadally S, 2012 [12]	Zeadally S, 2012, TELECOMMUN SYST, V50, P217, DOI 10.1007/s11235-010-9400-5, DOI	16.25	2012	2014–2017
Karagiannis G, 2011 [55]	Karagiannis G, 2011, IEEE COMMUN SURV TUT, V13, P584, DOI 10.1109/SURV.2011.061411.00019, DOI	14.81	2011	2014–2016
Kenney J, 2011 [56]	Kenney J, 2011, P IEEE, V99, P1162, DOI 10.1109/JPROC.2011.2132790, DOI	14.24	2011	2014–2016
Whaiduzzaman M, 2014 [10]	Whaiduzzaman M, 2014, J NETW COMPUT APPL, V40, P325, DOI 10.1016/j.jnca.2013.08.004, DOI	12.02	2014	2015–2018
Sommer C, 2011 [45]	Sommer C, 2011, IEEE T MOBILE COMPUT, V10, P3, DOI 10.1109/TMC.2010.133, DOI	11.93	2011	2013–2016
Lu N, 2014 [57]	Lu N, 2014, IEEE Internet Things, V1, P289, DOI 10.1109/JIOT.2014.2327587, DOI	11.08	2014	2018–2019
Sharef B, 2014 [58]	Sharef B, 2014, J NETW COMPUT APPL, V40, P363, DOI 10.1016/j.jnca.2013.09.008, DOI	10.97	2014	2015–2018
He D, 2015 [59]	He D, 2015, IEEE T INF FOREN SEC, V10, P2681, DOI 10.1109/TIFS.2015.2473820, DOI	10.67	2015	2018–2020
Ren S, 2015 [60]	Ren S, 2015, ADV NEUR IN, V28, P0, DOI 10.1109/TPAMI.2016.2577031, DOI	10.29	2015	2019–2020

and Figure 11).

3.3.3. Betweenness Centrality

Literature with a high degree of centrality is frequently cited or co-cited by articles in different fields. It can be the intersection of different knowledge domains or the key node in the temporal evolution of different knowledge domains. The paper with a high betweenness centrality occupies an essential position in the knowledge structure; that is, it plays an important role in connecting other nodes or several different clusters [61]. The results show that among 3661 articles, the top 4 articles with high betweenness centrality are Whaiduzzaman M (0.16), Akhtar N (0.13), Araniti G (0.12), and Naik G (0.1). These papers are significant results of the research on ICV road safety (

Table 8. References with a high betweenness centrality (>0.1).

Centrality	Author	Year	Source	Vol	Page
0.16	Whaiduzzaman M	2014	J NETW COMPUT APPL	40	325
0.13	Akhtar N	2015	IEEE T VEH TECHNOL	64	248
0.12	Araniti G	2013	IEEE COMMUN MAG	51	148
0.1	Naik G	2019	IEEE Access	7	70169

and Figure 12).

3.4. Theme Evolutionary Analysis

3.4.1. Thematic Overlapping

The thematic overlap analysis is conducted at two levels. On the one hand, the change in the total number of thematic words in the past decade is understood to infer trends and rules of development in the field of ICV road safety. The increasing number of thematic words over time reflects positive developments in the field, where research directions and themes are becoming more refined and comprehensive. On the other hand, the increase in new themes, the decrease in ancient themes, and the theme retention rate offer an understanding of the continuity and innovativeness within the field. For this purpose, the study divides the past decade into four stages: 2013–2014, 2015–2017, 2018–2020, and 2021–2022. As shown in Figure 13, there are 143 thematic words in the first stage, 35 of which are lost, and the remaining 108 (76%) remain in the second stage. In the second stage, there are 253 themes: 145 original themes are added; 33 are lost; and 220 (87%) themes enter the third stage. In the third stage, there are 382 thematic words; 162 new words are added; 72 words flow away; and 310 (87%) themes move to the fourth stage. In the fourth stage, there are 357 themes, with 47 new ones added. In each stage, the number of new themes is considerably larger than the number of lost themes, and a large proportion of the themes are retained for the next stage. The total number of themes increases in each stage, indicating a healthy and orderly development with strong continuity and ongoing flourishing (Figure 13).

3.4.2. Thematic Evolution

The thematic evolution map of ICV road safety intuitively illustrates the relationships between specific themes and time, showing the evolutionary paths from old themes to the latest themes. The node size refers to the amount of literature on the related theme, and a larger node typically represents the core theme of the period. The thick and solid connection line means the connected themes have a certain similarity and strong evolutionary relationship, while the dotted line means that the latter theme is an embranchment of the former theme, or the new word derived from that subject, with less similarity. There are isolated nodes at certain epochs where evolutionary sources cannot be found, nor can relations between them and other themes be established. As shown in Figure 14, in the early stage, SYSTEM, WIRELESS-SENSOR-NETWORKS, and INTELLIGENT-TRANSPORTATION-SYSTEM are relatively core themes; in the middle stage, V2X-COMMUNICATION, AUTONOMOUS-VEHICLES, IMPACT, VEHICULAR-NETWORK, VEHICULAR-COMMUNICATION, ATTACKS, and INTELLIGENT-VEHICLES become the core themes; in the latest stage, there are no representative themes and themes are in broad coverage and evenly sized, including ARCHITECTURE, ELECTRIC-VEHICLES, ACCIDENTS, COSTS, WORK-ZONE, TRAFFIC-COLLISIONS, etc (Figure 14).

There are three major evolutionary paths. The path from AD-HOC-NETWORKS/INTELLIGENT-TRANSPORTATION-SYSTEM to VEHICULAR-AD-HOC-NETWORK(VANET) to INTELLIGENT-TRANSPORTATION-SYSTEM to INTELLIGENT-TRANSPORTATION-SYSTEM is clearly defined, and its evolutionary relationship is from vehicular ad hoc network (VANET) to intelligent transportation system (ITS). Another obvious path is SYSTEM to AUTONOMOUS-VEHICLES to C-ITS to V2X-COMMUNICATIONS/CELLULAR-NETWORKS. WIRELESS-SENSOR-NETWORKS to WIRELESS-SENSOR-NETWORKS to INTELLIGENT-TRANSPORTATION-SYSTEM to INTELLIGENT-TRANSPORTATION-SYSTEM is the last well-defined path, which reflects the evolutionary relationship between the wireless sensor network and the intelligent transportation system. In recent studies, the paths of AUTOMOBILE to ACCIDENT, CONNECTED-AND-AUTOMATED-VEHICLE to IMPACT, and ELECTRIC-VEHICLES to TRAFFIC-COLLISION (dotted line) imply studies on road safety are emerging.

3.4.3. Strategic Coordinate Diagram

To gain a deeper understanding of the evolving state of themes in each stage, strategic coordinate diagrams of the themes in the four stages are plotted with SciMAT. The strategic coordinate diagram utilizes a cross-coordinate system, with the horizontal axis representing Centrality and the vertical axis representing Density. The larger the centrality value, the stronger the evolutionary relationship between the theme and other themes, and the larger the density value, the higher the internal connection of the theme. The size of the nodes is proportional to the amount of literature related to each theme. Larger nodes signify themes that have attracted more attention. Last but not least, the diagram divides themes into four quadrants. The first quadrant contains themes with high centrality and density, known as the “engine themes,” which represent the research focuses of the field. The second quadrant includes themes with low centrality and high density, referred to as isolated/professional themes. The third quadrant comprises themes with low centrality and low density, which are emerging or dying themes and are not research foci during that stage. The fourth quadrant consists of themes with high centrality and low density, representing innovation themes with great potential l [62].

The strategic coordinate diagrams indicate that engine themes for 2013–2014 are INTELLIGENT-TRANSPORTATION-SYSTEMS and AS-HOC-NETWORKS, while the innovation theme is SYSTEM; engine themes from 2015 to 2017 are VEHICLES, and AUTONOMOUS-VEHICLES, while innovation themes are NETWORKS, VEHICULAR-NETWORKS, and VEHICULAR-AS-HOC-NETWORK(VANET), the theme shifting from an innovation theme to an engine theme is INTELLIGENT-VEHICLES and the theme changing from isolated theme to engine theme is IMPACT. In the next two stages, a lot of themes prosper in all quadrants, and the size of nodes is quite balanced in the first and fourth quadrants, indicating that research in this field has experienced leapfrog growth since 2018, which is consistent with the growing trend of publications in the previous section. From 2018 to 2020, engine themes include ELECTRIC-VEHICLES, AUTOMOBILES, INTELLIGENT-TRANSPORTATION-SYSTEMS, DRIVER-BEHAVIOR, and UAV, while innovation themes include INTELLIGENT-VEHICLES, NEURAL-NETWORK, PREDICTION, and SENSOR. VEHICULAR-COMMUNICATION and ATTACKS are the two largest nodes moving from the second quadrant to the first quadrant. In 2021–2022, engine themes include AUTOMOBILES, SIMULATION, ANALYTICAL-MODELS, IMPACT, INTELLIGENT-TRANSPORTATION-SYSTEM, and UNMANNED-AERIAL-VEHICLES. ELECTRIC-VEHICLES is moving into the second quadrant. Innovation themes in this stage include ALGORITHM, SMART-CITIES, ARCHITECTURE, and ACCIDENTS. It is worth noting that ACCIDENTS come into sight for the first time in 2021–2022, and IMPACT appears in both stages of 2015–2017 and 2021–2022, indicating research on road safety in the field of ICV is attracting people’s attention (Figure 15).

4. Discussion

4.1. Documents Characteristic Analysis

The field of ICV road safety has experienced significant growth in terms of literature volume since 2013. The number of publications steadily increased year by year and experienced a leap forward in 2018 (Figure 2). The total number of publications has reached 3661 in the past decade. Notably, the growth of the literature volume after 2019 remained unaffected by the global COVID-19 pandemic, highlighting the robust progress of research in this field. The research in the field shows an advancing trend with rapid progress. Another feature is the presence of more than 1300 conference articles, which are evidence of the development of industrial technologies. These articles are produced by various international conferences on traffic safety, ICV, and automobile safety, serving as platforms for idea exchange and technical communication regarding ICV road safety. In terms of the number of publications, the number of publications in China is significantly higher than in the rest of the world and twice that of the United States, the second-ranked country. This may have something to do with the prosperity of the Chinese automobile market in recent years. With the continuous increase in production and sales, China has gradually become the world’s largest automobile producer and major market. Also, Figure 3 reflects close cooperation relationships in the field between different countries, which is a good foundation to promote the industry. Among these, the closest cooperation relationship is between China and the United States, indicating there is sufficient communication and consensus on scientific research. From the perspective of inter-institution collaborations (Figure 4), universities and research institutes perform well in terms of the number of publications, cited frequency, and strength of collaborations. The results suggest the Universities of Waterloo, Tsinghua Univ., Beijing Univ. Posts & Telecommun, Chinese Acad Sci., Xidian Univ., and others are leading institutions in the research field. It is considered that the publications of universities and research institutions are closely related to discipline construction, program requirements, and faculty targets. This study cannot reflect any strong partnerships between schools and enterprises. Further research could strengthen policy guidance and technical exchanges and strive for higher-level integration of industry, education, and research. In terms of inter-author collaborations, Figure 5 shows that in major research, authors are closely connected and linked in a closed-loop pattern, and scholar groups are formed in certain research directions. The overall collaboration is balanced and mature.

The main journals in this field reflected by the dual-map overlay of journals (Figure 6) are IEEE Transactions on Intelligent Transportation Systems and IEEE Transactions on Vehicular Technology, IET Intelligent Transport Systems, IEEE Communications Magazine, IEEE Communications Surveys and Tutorials, AD HOC Networks, IEEE Wireless Communications, Sensors, IEEE Sensors Journal, Accident Analysis and Prevention, Sensors-Basel, Transportation Research part C—Emerging Technologies, etc. Among these journals, IEEE Transactions on Vehicular Technology stands out in multiple disciplines. Future scholars and researchers wishing to understand important research in this field or publish cutting-edge study results can aim for the above journals.

Overall, the flourishing development of the research field of road safety for ICV rides the wave of new transportation modes and reform during the global energy crisis. With the help of emerging technologies such as the interconnection of information transmission and human-computer interaction and new products of the automobile industry such as new energy vehicles and autonomous vehicles, the research field is shining on global intelligent technology development. Its vigorous progress is inevitable, and its long-lasting potential is promising.

4.2. Co-Word Analysis

In the co-word analysis, VOSviewer is utilized to summarize the keywords in the field of ICV road safety. The density map (Figure 7) shows prominent keywords, and, at the same time, the network graph (Figure 8) exhibits clear correlations among the keywords. Comparing the two figures, the core keywords in common are SECURITY, VEHICULAR AS HOC NETWORKS(VANETs), INTELLIGENT TRANSPORTATION SYSTEMS, and the INTERNET OF THINGS. These results indicate the existence of four main research directions in the field, whereas two of the four themes are in the field of computer science and the other two are either in the safety domain or the traffic domain. The density map shade and the node size of the network graph suggest a balanced development among these themes. The presence of stable themes is characteristic of a mature research field.

4.3. Co-Citation and Cluster Analysis

4.3.1. Overview of Clusters

CiteSpace is employed to organize the references of all literature and group them into clusters based on shared keywords in the titles. Literature entitled with the same or similar keywords is assigned to the same cluster, and then the frequency of citations in that cluster as well as the citation coverage of the cluster are counted. In this way, researchers can easily recognize important articles in their field of study and gain a perspective on what other scholars have conducted or are doing. Each cluster contains a cited reference list. The most cited literature represents the research base in that cluster, and citing literature represents the research frontier. The collection of most frequently cited references can be considered a key research base in the field of road safety issues for ICV, whereas the aggregation of citing articles is not sorted by publication year and does not equal the overall research frontier in the field of study.

Taking an overall perspective on the top five co-citation clusters, it is evident that the literature in the field primarily revolves around vehicular network security, machine learning, and computing techniques. The largest cluster is cluster 0, with 114 articles, whereas the other four clusters are about the same size, with no more than 67 articles each. Article types are mainly research papers (research on improving vehicular networking techniques) and surveys. There are also some review articles. From the perspective of publication year, cluster 0 contains articles published in 2014 and is mostly cited by articles published in 2015 and 2018, indicating the topic related to vehicular cloud computing, using VANET, and data management has been developed for a long time, as the citation burst and centrality betweenness verify. Cluster 1 and Cluster 2 are mainly articles from 2018–2019 and are cited by articles published in 2021. The two clusters are emerging, containing topics such as machine learning, blockchain, security privacy, etc., indicating scholars are interested in network security protection for ICVs. Cluster 3 consists mainly of articles published around 2017 and is cited by articles published in 2019 and 2021, which are relatively new. The most cited articles in cluster 4 were published in 2013–2014, and articles cited in this cluster were published in 2011, indicating VANETs back-off mechanism is out of date. Scholars have moved to an in-depth study of vehicular networking.

As of the completion of this paper, the topics of trust management, security privacy, learning-based recourse allocation, and machine learning are the most recent concerns in the study of road safety issues for ICV. Scholars attempt to address road safety problems by improving intelligent technologies on ICVs, even though their keywords do not contain ‘traffic accident’. Such literature could be overlooked when using traditional review methods.

Cluster 0: vehicular cloud computing; using VANET; data management perspective.

• Research Base

On the way to intelligent transportation systems (ITS), vehicular ad hoc networks (VANET), a subclass of the mobile ad hoc network (MANET), caught researchers’ attention [9,11,13,58]. Researchers conducted a lot of surveys on VANETs and the relevant issues from 2010 to 2013. The paper of Al-sultan S. et al., the most cited reference, introduces VANETs from architecture to characteristics, from theory to simulation and application. It is a comprehensive study that helps researchers understand VANETs from the very beginning, including features surrounding VANET, tools to simulate VANET protocols, and applications up to 2013 [9]. As vehicles are expected to carry communication systems, computing systems, infotainment systems, and so on, vehicular networks are facing a lot of challenges [10,11,12,13,55]. To improve ITS on aspects of road safety, traffic efficiency, privacy, and security issues, researchers try to classify the problems cryptographically [11] and present solutions such as Vehicular Cloud Computing (VCC) [10], long-term evolution-wireless (LTE) for vehicular networking [63], as well as dedicated short-range communication (DSRC) technology [56]. The VCC is a hybrid technology that allows the vehicle to make decisions instantly to promote traffic and road safety [10]. Md. Whaiduzzaman et al. also summarize the applicable properties that support VCC [10]. Giuseppe Araniti et al. discuss LTE for vehicular networking critically on merit and demerit [63]. DSRC is a technology for communication between vehicles and the roadside. John B. Kenney (2011) interprets the concept of DSRC and discusses the standards in the United States and whether the technology should be adopted [56]. Sherali Zeadally et al. also conducted a review study of wireless access standards and presented the worldwide concerns of VANET on protocols, technologies, and services [12]. Georgios Karagiannis et al. discuss requirements, challenges, standards, and solutions for vehicular networking protocols in the United States, Japan, and Europe in their survey [55].

In other words, researchers realized the potential of VANET in the early 2010s and conducted surveys on technique issues and ethical issues.

• Research Frontier

In the late 2010s, researchers conducted more surveys on VANET based on a host of data [15,19] to solve technical problems in the application of vehicular networks. Studies in this cluster focus on two main aspects: new vehicular network technology and vehicular networking security.

In the beginning, the idea of the combination of cellular networks and DSRC, the heterogeneous vehicular network (HetVNET), came into view, and Kan Zheng et al. reviewed applications of it. Then, they present a HetVNET framework that uses different wireless networking techniques and illustrates typical scenarios, discussing the challenges and future research directions [17]. Zekri Abdennour and Jia Weijia further discuss the advantages of HetVNET, including high data rates, low latency, a wide communication range, benefits for autonomous cars, and limits such as vehicle connection [16]. Other radio access technologies such as vehicle-to-everything (V2X), visible light communication, mmWave, Cellular-V2X, and 5G have become new research directions [18,64]. Nishu Gupta et al. conducted a tutorial survey of these vehicular communication technologies and discussed future research directions [64].

Another popular topic is security and privacy. Challenges and research on cyber threats are reviewed [24,65,66,67], an efficient medium access control (MAC) protocol that enhances vehicular safety is introduced [66], and the intrusion detection system (IDS) and its limits, along with its potential application in VANET Cloud, are discussed [68].

Cluster 1: trust management; using blockchain; security privacy.

• Research Base

References in this cluster focus on two keywords: security and blockchain. As intelligent transportation systems benefit both consumers and manufacturers, traffic management, road safety, security, and privacy threats become important issues. Researchers find blockchain-based technology to be the potential solution to this problem [21,22,23,69,70,71]. In a decentralized trust management system for vehicular networks based on blockchain, a rating from each neighboring vehicle is calculated and packed into a block, and then the total value of the blocks is evaluated [71]. On the one hand, researchers wish to ensure data sharing security. Jiawen Kang et al. propose an approach of consortium blockchain and smart contract technologies to ensure the security of vehicular edge computing servers [22]. Jiawen Kang and his colleagues also propose a two-stage soft security enhancement solution to improve the existing delegated proof-of-stake consensus scheme, which is used to establish a blockchain-enabled Internet of Vehicles (IoV) [23]. On the other hand, to protect users’ privacy, researchers are making many attempts. Ali Dorri et al. propose a blockchain-based architecture that increases the security of vehicular networks [21]. Zhaojun Lu et al. propose an anonymous reputation system (BARS) that is used to establish a trust model for VANETs [69]. Lun Li et al. propose CreditCoin, an announcement network that allows users to post announcements anonymously to share traffic information [70].

References in this cluster are from literature within the past 5 years, indicating the research direction is relatively novel.

• Research Frontier

The development of the technology of concern seems to have had certain achievements, so the newest articles are mostly literature reviews. Researchers conduct comparative studies on blockchain-based models in security, privacy prevention, and trust management systems [25], authentication schemes in the IoV and VANETs [26], authentication schemes with 5G and 5G-SDN, and blockchain technologies [72]. As can be seen, blockchain-based solutions are favorable in the VANET scenario [73], wireless technologies such as IEEE 802.11p and 5G V2X are adopted [29], and the vehicular network environment is shifting from VANET to the IoV, a new concept that is likely to realize the application of V2X [29,74]. In addition, researchers classify authentication schemes according to security mechanisms, requirements, limitations, attacks, countermeasures, and so on [28], classify related literature according to research directions and blockchain-based IoV implementations [27], and summarize ways of attacking as well as unsolved issues in the field.

Certainly, researchers will not forget the open issues. They propose a consortium blockchain technology, as 5G appears, that enables traceable and anonymous V2V data sharing and privacy prevention in IoV [75]. Moreover, data storage environment and security are studied, and solutions such as practical byzantine fault tolerance (PBFT) are proposed, which allows consortium blockchains to audit publicly, store data sharing, and record consensus processes [76].

Citing articles in this cluster were published in 2021 and 2022, meaning researchers are currently concerned about vehicular trust management, security, and privacy systems.

Cluster 2: learning-based resource allocation; machine learning

• Research Base

As vehicular networking is strongly demanded, machine learning, one branch of artificial intelligence (AI), is expected to achieve intelligent transportation [30,31]. Besides, the development of communication technologies, including cellular networks, LTE, and 5G, has made great progress in vehicular communication [32,33,77]. Therefore, researchers are devoted to building more reliable and convenient vehicular networks. From 2017 to 2020, researchers established a solid research foundation on vehicular networks based on communication technology. Some researchers aim to improve vehicular communication systems by proposing an integrated framework that allows networking, caching, and computing [31]. Some researchers provide a clear overview of the evolution of the long-term evolution vehicle (LTE-V) and its modes 3 and 4, as well as the practice of 5G V2V technology [32], the standardization of radio access technologies such as 802.11bd and NR V2X (supplementary of vehicular sensors) and their prospects [33,78], the standardization of LTE V2X in the third generation partnership project (3GPP) [18,77], etc.

Except for the most cited article by Tang et al., which envisions the next-generation technology, throughout most references in this cluster, the researchers notice the main idea is how different wireless access technologies enable V2X and their features rather than merely about AI. As the cluster emerges, the research base is expected to be extended in the foreseeable future.

• Research Frontier

Nowadays, IoV is in a paramount position in either economic development or international prestige, and V2X communication is a highly expected subject. The United States pays a lot of attention to the development of DSRC, which enables V2V and relative communications [20]. Studies have been conducted to analyze the strengths and limitations of DSRC and cellular network communication technologies, as well as to envision the impact of 5G technology [20]. Beyond this, researchers are eager to fully understand the concept, history, features, and application of VANET, the 5G V2X, and the possible 6G V2X [29,35,79,80]. Researchers also wish to know how machine learning technology helps the development of VANET [35] in either security systems [34] or detection accuracy and efficiency [36]. Moreover, machine learning in 6G V2X and mobile edge computing are reviewed [79,81]. In some review articles, researchers focus on the cellular vehicle-to-everything (C-V2X) in 3GPP, especially the sidelink air interface [82].

The aforementioned studies were conducted around 2021; thus, researchers who are interested in the integration of AI technology and vehicular networking could go through these articles to gain the newest research status.

Cluster 3: intrusion detection system; using fog computing; routing protocol

• Research Base

References with high citation rates were published mainly in 2016 and 2017, and the major research direction is the security challenges of VANETs. In this period, VANETs were one of the popular topics that drew scholars’ attention. Researchers would like to figure out the types of attacks and the corresponding detection mechanisms [37,38], the suitable types of protocols and applications [39], and the feasible authentication scheme [40]. Except for reviewing literature in the field of study, researchers also look for different approaches to improve vehicular trust management. For example, researchers propose an attack-resistant trust management scheme (ART) for VANETs to detect and deal with attacks and evaluate the trustworthiness of data [41]. Researchers also concern themselves with the effects of security attacks on vehicular communication channels and sensor tampering on ICVs and their countermeasures [83]. Besides, some researchers discuss the application of UAVs in the establishment of ITS [84].

References in this cluster seem to be solutions to vehicular security issues other than blockchain.

• Research Frontier

From 2019 to 2021, researchers summarize requirements and solutions for several wireless network communications from the perspective of privacy preservation. Research topics include content dissemination in VSNs [42], traditional cryptographic technologies for IoV and VANETs, authentication protocol for radio-frequency identification devices (RFID) [43], as well as an authentication protocol for both VANET security standards and 5G security features [44]. Comprehensive review articles are also published to present the advantages and disadvantages of existing solutions [34], the characteristics of different VANET wireless network communications (including vertical handover, data dissemination and collection, gateway selection, etc.) [16], and the comparison of different authentication and privacy schemes [85].

There are two refreshing articles. Jitendra et al. mentioned software-defined networking (SDN), which can bring flexibility and programmability to vehicular networks. Features of SDN-based VANETs are summarized, and their architecture modes, protocols, access technologies, and potential technologies are demonstrated [86]. Chowdhury A. et al. embarked on the existing cyberattacks on self-driving cars. They analyze the attacks, describe the countermeasures undertaken by the governments and the manufacturers, and discuss how ICVs guarantee resilient operation under cyberattacks [87].

In other words, researchers are seeking different solutions to ensure vehicular security and privacy.

Cluster 4: VANET system; back-off mechanism; vehicular networking perspective

• Research Base

References in this cluster were mostly published ten years ago (2008–2013), when researchers started to concern themselves with VANETs. There were many challenges on VANET in 2008, and research bloomed in all aspects.

Considering route planning and safety applications, Sommer C. et al. discuss inter-vehicle communication (IVC) protocols and the demand for bidirectional coupling of network simulation and road traffic microsimulation for IVC protocols. They try to improve IVC and protocol evaluation by developing a hybrid simulation framework composed of the network simulation OMNeT++ and the road traffic simulator SUMO, called Veins (vehicles in network simulation) [45]. Tonguz O. et al. study highway traffic regimes and propose a distributed vehicular broadcast (DV-CAST) protocol for VANETs, which relies on local topology information to handle broadcast messages and operate in all kinds of traffic regimes [49]. Manuel Fogue et al. focus on the broadcast storm problem (including redundancy, contention, and packet collisions), and present enhanced message dissemination based on roadmaps (eMDR), which could reduce the notification time [88]. Harri J. (2009) introduces a framework that helps generate the vehicular mobility model and illustrates approaches for the development of those models and their interactions with network simulators [89].

Except for the above studies, researchers are interested in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Results of a field testing campaign confirm the influence of the urban environment, including trees, heavy vehicles, and terrain elevation, on V2I communication and provide consideration for urban RSU deployment [47]. Hartenstein H. et al. conducted a review study of VANETs, including V2V and V2I communication, in terms of applications, resources, and the environment [48]. The IEEE also develops a system architecture that allows wireless access in vehicular environments [48], and Uzcategui R. et al. introduce the associated standards in their tutorial literature [90].

• Research Frontier

Researchers are devoting themselves to improving VANETs during this period.

Around 2013 and 2014, researchers were looking for technologies that satisfied different demands. For example, a comprehensive survey of VANET is conducted to verify its economic and environmental-friendly characteristics [50]. However, most researchers are concerned about road safety problems. Joerer S. et al. aim to propose an evaluation scheme that could predict the possibility of collisions at intersections according to real-time information received from surrounding vehicles. Their study shows the beacon message to be unsatisfactory, while the timeliness of beacon messages is confirmed [52]. Coincidentally, Santa J. et al. evaluate the wireless collision avoidance (CA) system in VANET by generating vehicular mobility traces, deriving the probability of rear-end collisions, and quantifying the probability of vehicles not receiving emergency messages. The results confirm the function of CA systems; however, the relationship between collisions and vehicle density is not linear [91]. Some researchers consider the IEEE 802.11 medium access control protocol and its requirements for vehicular safety and propose a reverse back-off mechanism designated for road safety in high-density scenarios [51]. Beyond that, some researchers consider the integration of V2V and V2I. For instance, José Santa et al. present a communication platform and a network designed for both the vehicle and infrastructure sides and introduce a service access middleware together with onboard application management [53]. Researchers also considered Y-Comm architecture such as network dwell time (NDT), the time before handover (TBH), and exit time (ET) to overcome handover issues [54]. Furthermore, researchers consider standardized facilities such as the cooperative awareness message (CAM) and the decentralized environmental notification message (DENM). In Santa J.’s article, the functions of vehicle tracking and dissemination of traffic accidents using the two facilities are validated, as are impacts from vehicle speed, signal quality, or message dissemination rules [92].

4.3.2. Citation Burst & Centrality Betweenness Analysis

This section provides direct guidance for researchers to quickly grasp the core research and key literature in the field within a short time. The top 5 articles with high burst value (Al-sultan S. (2014), Zeadally S. (2012), Karagiannis G. (2011), Kenney J. (2011), and Whaiduzzaman M. (2014)) can provide objective guidance for relevant researchers to acquire important research results in the corresponding period. Also, the top 4 articles with high centrality between them (Whaiduzzaman M. (0.16), Akhtar N. (0.13), Araniti G. (0.12), and Naik G. (0.1)) represent the core articles of the knowledge framework in ICV road safety.

4.4. Theme Evolutionary Analysis

The thematic overlapping map (Figure 13) illustrates the change in the number of themes. The overall number of themes is increasing, which not only implies an increase in research in the field but also implies an increase in research depth and an extension of research directions. This is consistent with the conclusion of the previous sections. The thematic evolution map (Figure 14) and the strategic coordinate diagrams (Figure 15) mainly reflect the changes in research themes in the field, where the evolution map shows the evolutionary relationships of themes and the strategic coordinate diagrams highlight engine themes and innovation themes in different stages. The former indicates themes such as INTELLIGENT-TRANSPORTATION-SYSTEM, CELLULAR-NETWORKS and V2X-COMMUNICATION have clear evolutionary paths. There has been no specific research focus in recent years. The strategic coordinate diagram of 2021–2022 indicates engine themes including AUTOMOBILES, SIMULATION, ANALYTICAL-MODELS, IMPACT, INTELLIGENT-TRANSPORTATION-SYSTEM, and UNMANNED-AERIAL-VEHICLES, and innovation themes including ALGORITHM, SMART-CITIES, ARCHITECTURE, and ACCIDENTS. By summarizing the results of this section, the key research themes in the recent stage are networks, ITS, IoV, and vehicular communication. In addition, themes with great potential include accidents and vehicular networking. Since cyber-security is one of the most important challenges in ICV systems and may endanger the safety of passengers [4], themes of vehicular network security or vehicular intelligent systems (IMPACT, ANALYTICAL-MODELS, ALGORITHM, etc.) will be long-lasting hotspots. Also, the ITS, as the goal of the development of ICV, will reflect the overall intelligent travel pattern after coupling the construction of vehicular systems and road traffic facilities.

It is believed that SciMAT presents a summary of themes in the current research field based on the contents of existing studies, allowing researchers to grasp the current situation timely and accurately and predict the potential research direction.

4.5. Research Strengths and Limitations

The time span covered in this study is limited to 10 years due to the recent emergence of ICVs. The availability of the data is restricted by the limited market penetration of ICVs, which in turn affects the research data. However, knowledge mapping provides valuable insights into the research landscape and future trends related to the safety issues of ICVs. As the number of ICVs on the road continues to increase over time, the volume of related studies will also grow, and the results depicted by knowledge mapping will evolve accordingly. The results are strongly time-sensitive, which has both advantages and disadvantages for this method. Additionally, although WoS is the largest database, it does not guarantee the comprehensiveness of studies worldwide.

5. Conclusions

When looking at 3661 papers on intelligent connected vehicles related to road safety issues from 2013 to 2022, China contributed the most publications, and the collaboration between China and the United States was the closest. Institutions in the industry cooperated a lot, while scholars formed a few communities. It is recommended that IEEE Transactions on Vehicular Technology and other journals be hot or key journals for different subjects in this field, which will beacon the direction for subsequent scholars to find the industry frontier or publish new achievements. The density map and network map drawn by VOSviewer cover key contents such as Security, VANET, ITS, and the Internet of Things. The co-citation clusters and clusters timelines produced by CiteSpace reflect that VEHICULAR CLOUD COMPUTING, TRUST MANAGEMENT, MACHINE LEARNING, INTRUSION DETECTION SYSTEM, and VANET SYSTEM are the main clusters. The thematic evolution map and the strategic coordinate diagrams produced by SciMAT show that networks, ITS, IoV, and vehicular communication attracting researchers’ attention, AUTOMOBILES, SIMULATION, ANALYTICAL-MODELS, IMPACT, INTELLIGENT-TRANSPORTATION-SYSTEM, and UNMANNED-AERIAL-VEHICLES, are engine themes in the current stage and ALGORITHM, SMART-CITIES, ARCHITECTURE, and ACCIDENTS as innovation themes.

Consistent conclusions are obtained by mining the research on the road safety of ICVs from different perspectives. Results from all three software packages can be summarized as “Vehicular ad hoc Network,” “Intelligent Transportation Systems,” and “Network Security.” Overall, the vehicular Internet, or vehicular network, and ITS are the focal points of the research on road safety in ICV. However, according to the prediction of SciMAT, specific subjects such as machine learning, V2X-Communication, accidents, and architecture may become the key research directions in the next stage. It is believed that with the breakthrough of V2X technology [93], the strengthening of machine learning, and the development of 5G technology, ICVs are expected to shift from single-vehicle control to multi-vehicle control. Any breakthrough in the cutting-edge issues of VANETs or IoVs could lead to a huge leap forward in the development of the ICV industry. Thus, researchers in disciplines related to the field of communication can enhance this focus. ITS may involve more classified areas. It is not only necessary to research and develop interactive systems for vehicles themselves but also to propose demands on road traffic facilities, traffic policies, and regulations, which could provide ideas for follow-up work by governments, traffic management, and other researchers. Network security will go with the process of ICV, and there will be different technical issues at different technical stages so that it can potentially drive the overall development of the industry.

Based on the visualization study of the retrieved literature, current research in the field of ICV road safety focuses on vehicular system design and manufacturing as well as the construction of ITS. The study shows that the field is in a highly developing and innovative phase. Researchers from all sides hope to improve road safety and reduce accident probability through new technologies such as onboard networks and machine learning. Mature ICV transportation will emerge as the problems are solved in each section. Some scholars have made a special study on ICV traffic accidents [91,94,95,96]. It is believed that as the number of ICVs increases, the potential for traffic accident-related research will gradually emerge.