Sage Journals: Discover world-class research

Abstract

In order to trace the research trends and hotspots related to the field of driver behavior research both in China and worldwide, a bibliometric analysis was performed to systematically analyze 7208 scientific papers about driver behavior in the Web of Science database from 1957 to 2018, and the results were discussed from the 6 perspectives of growth trends of publications, geographical distribution, subject categories, institutions, authors and keywords distribution. The results show that research on driver behavior has been increasing rapidly since the 1990 s, but research in Europe and the United States started more than 20 years earlier than in China. Currently, transportation, engineering, computer science and behavioral science have replaced the traditional subject categories, becoming the most important domains in this field. Research on driver behavior focuses on safety and systems using simulation models which is interacted with almost everything concerning traffic operation and safety. The current trend is that more researchers are working on understand the driver-vehicle-system interactions under the environment of connected and autonomous vehicle. Further research is needed to focus on unsafe driving behavior based on driver-vehicle-system interactions. This study will provide scholars in related academia and industry with panoramic knowledge of driver behavior research, as well as research hotspots and future research directions.

Keywords

Driver behavior research trends driving safety knowledge graph visualization

Get full access to this article

View all access options for this article.

References

International Transport Forum. Road Safety Annual Report 2017. OECD Publishing, Paris, France, 2017.

National Bureau of Statistics of China. China Statistical Yearbook 2016[DB/OL]. www.stats.gov.cn/tjsj/ndsj/2016/indexch.htm, 2016.

National Bureau of Statistics of China. China Statistical Yearbook 2017[DB/OL]. www.stats.gov.cn/tjsj/ndsj/2017/indexch.htm, 2017.

Zou ,

W. L.

Yue and

H.L.

Vu , Visualization and Analysis of Mapping Knowledge Domain of Road Safety Studies, Accident Analysis & Prevention 118 (2018), 131–145.

Graham-Rowe ,

Skippon ,

Gardner and

Abraham , Can we reduce car use and, if so, how? A review of available evidence, Transportation Research Part A 45 (2011), 401–418.

Engelbrecht ,

M.J.

Booysen ,

G.J.

Van Rooyen and

F.J.

Bruwer , Survey of smartphone-based sensing in vehicles for intelligent transportation system applications, IET Intelligent Transport Systems 9(10) (2015), 924–935.

J.L.

Bell ,

M.A.

Taylor ,

X.G.

Chen ,

R.D.

Kirk and

E.R.

Leatherman , Evaluation of an in-vehicle monitoring system (IVMS) to reduce risky driving behaviors in commercial drivers: Comparison of in-cab warning lights and supervisory coaching with videos of driving behavior, Journal of Safety Research 60(2017), 125–136.

M.S.

Young ,

S.A.

Birrell and

N.A.

Stanton , Safe driving in a green world: A review of driver performance benchmarks and technologies to support ‘smart’ driving, Applied Ergonomics 42 (2011), 533.

Markkula ,

Benderius ,

Wolff and

Wahde , A review of near-collision driver behavior models, Human Factors 54 (2012), 1117–1143.

10.

H.W.

Heinrich ,

Petersen and

N.R.

Roos , Industrial accident prevention: A safety management approach, 1980.

11.

Standardization Administration of the People’s Republic of China. The classification for casualty accidents of enterprise staff and workers, GB/T 6441-1986, 1986.

12.

Reason , Understanding adverse events: Human factors, Quality in Health Care Qhc 4 (1995), 80.

13.

Wang ,

P.L.

Rau and

Salvendy , Road safety research in China: Review and appraisal, Traffic Injury Prevention 11 (2010), 425.

14.

Li ,

Reniers ,

Cozzani and

Khan , A bibliometric analysis of peer-reviewed publications on domino effects in the process industry, Journal of Loss Prevention in the Process Industries 49 (2017), 103–110.

15.

Liu ,

Z.H.

Yu ,

Chen ,

Hong ,

Jin and

Yang , Visualization and Bibliometric Analysis of Research Trends on Human Fatigue Assessment, Journal of Medical Systems 42(10) (2018), 179.

16.

Blanco-Mesaa ,

J.M.

Merigo and

A.M.

Gil-Lafuente , Fuzzy decision making: A bibliometric-based review, Journal of Intelligent & Fuzzy Systems 32 (2017), 2033–2050.

17.

M.J.

Cobo , F.Chiclana,

Collop ,

J.D.

Ona and

Herrera-Viedma , A Bibliometric Analysis of the Intelligent Transportation Systems Research Based on Science Mapping, IEEE Transactions on Intelligent Transportation Systems 15(2) (2014), 901–908.

18.

S.A.

Kaye ,

Lewis and

Freeman , Comparison of self-report and objective measures of driving behavior and road safety: A systematic review, Journal of Safety Research 65 (2018), 141–151

19.

N.L.

Bragazzi ,

Dini ,

Toletone ,

Rahmani ,

Montecucco ,

Massa ,

Manca ,

Guglielmi ,

Garbarino ,

Debarbieri and

Durando , Patterns of harmful alcohol consumption among truck drivers: Implications for occupational health and work safety from a systematic review and meta-analysis, International Journal of Environmental Research and Public Health 15(6) (2018), 1121.

20.

R.M.

Shiffrin ,

Börner , Mapping knowledge domains, Proc. Natl. Acad. Sci 101(suppl. 1) (2004), 5183–5185.

21.

N.J.

van Eck ,

Waltman , Software survey: VOSviewer, a computer program for bibliometric mapping, Scientometrics 84(2) (2010), 523–538.

22.

C.M.

Chen , CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature, Journal of the Association for Information Science and Technology 57 (2014), 359–377.

23.

H.J.

Stack , Improving the behavior of drivers, American Journal of Public Health & the Nations Health 47 (1957), 446–451.

24.

Anjani

Kushwaha

, Prof. Deepak

Agrawal

and Prof. Akshat

Khaskalam

, A Survey Paper on Cloud Security Issues, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) V-6, I-9, September, 2018

25.

S.G.

Benson and

S.P.

Dundis , Understanding and motivating health care employees: Integrating Maslow’s hierarchy of needs, training and technology, Journal of Nursing Management 11 (2003), 315–320.

26.

H.Q.

Jin ,

Araki ,

X.K.

Wu ,

Y.W.

Zhang and

L.L.

Duan , Neurobehavioral study on accident-prone motor-vehicle drivers in China, Industrial Health 29 (1991), 23–26.

27.

Wang , A study of biorhythms and motor vehicle accidents on road, Journal of Chongqing Jiaotong University (Natural Science Edition) 2 (1983), 77–85.

28.

A.W.

Wolfe , Social Network Analysis: Methods and Applications, by Stanley Wasserman; Katherine Faust, Contemporary Sociology 91 (1994), 219–220.

29.

D.A.

Johnson and

M.M.

Trivedi , Driving style recognition using a smartphone as a sensor platform. In: International IEEE Conference on Intelligent Transportation Systems, IEEE, pp. (2011), 1609–1615.

30.

Orosz ,

R.E.

Wilson and

Stépán . Introduction: Traffic jams: Dynamics and control, Philosophical Transactions of the Royal Society of London 368 (2010), 4455–4479.

31.

Merat ,

A.H.

Jamson ,

F.C.H.

Lai ,

Daly and

O.M.J.

Carsten , Transition to manual: Driver behaviour when resuming control from a highly automated vehicle, Transportation Research Part F Traffic Psychology & Behaviour 27 (2014), 274–282.

32.

A.H.

Jamson ,

Merat ,

O.M.J.

Carsten and

F.C.H.

Lai , Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions, Transportation Research Part C 30 (2013), 116–125.

33.

Wang ,

Mehler ,

Reimer ,

Lammers ,

D’Ambrosio and

J.F.

Coughlin , The validity of driving simulation for assessing differences between in-vehicle informational interfaces: A comparison with field testing, Ergonomics 53 (2010), 404–420.

34.

Wang ,

Reimer ,

Dobres and

Mehler , The sensitivity of different methodologies for characterizing drivers’ gaze concentration under increased cognitive demand, Transportation Research Part F Psychology & Behaviour 26 (2014), 227–237.

35.

Boldi ,

Santini and

Vigna , PageRank: Functional dependencies, Acm Transactions on Information Systems 27 (2009), 1–23.

36.

Chitra

Kalpesh Vasvani

and Snehal

Kolte

, Secure Sharing Hospital Data Using Visual Cryptography, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) V-6, I-11, November, 2018.

37.

J.D.

Lee , Fifty years of driving safety research, Human Factors 50 (2008), 521.

38.

B.G.

Simons-Morton ,

Guo ,

S.G.

Klauer ,

J.P.

Ehsani and

A.K.

Pradhan , Keep Your Eyes on the Road: Young Driver Crash Risk Increases According to Duration of Distraction, Journal of Adolescent Health 54 (2014), S61–S67.

39.

X.S.

Wang ,

X.W.

Wu ,

Tremont and

Abdel-Aty , Investigation of Road Network Features and Safety Performance, Accident Analysis and Prevention 56 (2013), 22–31.

40.

X.S.

Wang ,

Chen ,

M.X.

Zhu and

Tremont , Development of a Kinematic based Forward Collision Warning Algorithm Using an Advanced Driving Simulator, IEEE Transactions on Intelligent Transportation Systems 17 (2016), 2583–2591.

41.

K.J.

Anstey ,

J.S.

Wood and

J.G.

Walker , Cognitive sensory and physical factors enabling driving safety in older adults, Clinical Psychology Review 25 (2005), 45–65.

42.

Almahasneh ,

W.-T.

Chooi ,

Kamel and

A.S.

Malik , Deep in thought while driving: An EEG study on drivers’ cognitive distraction, Transp. Res. Part. F: Traffic Psychol. Behav 26 (2014), 218–226.

43.

Kalaiselvi and

Shiddharthy , A Review on Plants and Food Grains Disease Detection and Quality Checking using Image Mining Techniques, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) V-6, I-10, October, 2018.

44.

Zeng ,

H.Y.

Wen and

H.L.

Huang , The interactive effect on injury severity of driver-vehicle units in two-vehicle crashes, Journal of Safety Research 59 (2016), 105–111.

45.

M.M.

Porter ,

G.A.

Smith ,

A.W.

Cull ,

A.M.

Myers ,

Bédard ,

Gélinas and

B.H.

Vrkljan , Older driver estimates of driving exposure compared to in-vehicle data in the Candrive II Study, Traffic Injury Prevention 16(1) (2015), 24–27.

46.

J.A.

Grunbaum ,

Kann ,

Kinchen ,

Ross and

Hawkins , Youth risk behavior surveillance–United States, 2003, Mmwr Surveill Summ 53 (2004), 1–96.

47.

Hatfielda ,

Williamsona ,

E.J.

Kehoeb ,

Lemona ,

Arguela ,

Prabhakharana and

R.F.S.

Job , The effects of training impulse control on simulated driving, Accident Analysis and Prevention 119 (2018), 1–15

48.

Zheng ,

Ran and

H.L.

Huang , Safety evaluation for driving behaviors under bidirectional looking context, Journal of Intelligent Transportation 21(4) (2017), 255–270.

49.

S.K.

Lal and

Craig , A critical review of the psychophysiology of driver fatigue. Biological Psychology 55(3) (2001), 173.

50.

R.B.

Wang ,

K.Y.

Guo ,

J.W.

Chu and

X. M.

Chu , Study on the Eye Location Method in Driver Fatigue State Surveillance, Journal of Highway and Transportation Research and Development 20 (2003), 111–114.

51.

R.J.

Yu ,

Abdel-Aty and

Ahmed , Bayesian Random Effect Models Incorporating Real-time Weather and Traffic Data to Investigate Mountainous Freeway Hazardous Factors, Accident Analysis & Prevention 50 (2013), 371–376.

52.

Chai and

Y.D.

Wong , Micro-simulation of vehicle conflicts involving right-turn vehicles at signalised junctions based on Cellular Automata, Accidents Analysis & Prevention 64 (2014), 94–103.

53.

Paul ,

Daniel ,

Ahmad and

Rho , “Cooperative cognitive intelligence for internet of vehicles”, IEEE Systems Journal 11(3) (2017), 1249–1258.

54.

X.S.

Wang ,

Tarko and

Tremont , The influence of combined alignments on lateral acceleration on mountainous freeways: A driving simulator study, Accident Analysis & Prevention 76 (2015), 110–117.

55.

Alfred Daniel , Anand

Paul

and Awais

Ahmad

, “Cooperative Intelligence of Vehicles for Intelligent Transportation Systems (ITS)”, Wireless Personal Communications 87(2) (2016), 461–484.

Knowledge graph analysis and visualization of research trends on driver behavior

Abstract

Keywords

Get full access to this article

References