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A graph method of description of driving behaviour characteristics under the guidance of navigation prompt message

Published online by Cambridge University Press:  15 August 2022

Liping Yang ,
Yang Bian ,
Xiaohua Zhao Open the ORCID record for Xiaohua Zhao [Opens in a new window] ,
Yiping Wu ,
Hao Liu  and
Xiaoming Liu
Liping Yang
Affiliation:
Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing, China School of information and electrical engineering, Zhejiang University City College, Hangzhou, China
Yang Bian
Affiliation:
Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing, China
Xiaohua Zhao*
Affiliation:
Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing, China State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China
Yiping Wu
Affiliation:
Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing, China
Hao Liu
Affiliation:
Beijing Transportation Information Center, Beijing Municipal Commission of Transportation, Beijing, China
Xiaoming Liu
Affiliation:
Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

To verify whether a graph is suitable for describing driver behaviour performance under the effects of navigation information, this study applies two types of prompt messages: simple and detailed. The simple messages contain only direction instructions, while the detailed messages contain distance, direction, road and lane instructions. A driving simulation experiment was designed to collect the empirical data. Two vehicle operating indicators (velocity and lateral offset), and two driver manoeuvre indicators (accelerator power and steering wheel angle) were selected, and T-test was used to compare the differences of behavioural performance. Driving behaviour graphs were constructed for the two message conditions; their characteristics and similarities were further analysed. Finally, the results of T-test of behavioural performance and similarity results of the driving behaviour graphs were compared. Results indicated that the two different types of prompt messages were associated with significant differences in driving behaviours, which implies that it is feasible to describe the characteristics of driving behaviours guided by navigation information using such graphs. This study provides a new method for systematically exploring the mechanisms affecting drivers’ response to navigation information, and presents a new perspective for the optimisation of navigation information.

Keywords

navigation prompt messagedriving behaviour graphdriving simulator technology
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 5 , September 2022 , pp. 1167 - 1189
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

AQSIQ and SA. 2009. The State Standard of the P.R. China: Road Signs and Markings (GB5768-2009). Administration of Quality Supervision, Inspection and Quarantine and Standardization Administration. Available at: http://www.doc88.com/p-3701701707998.html [Accessed 26 Sep. 2020].Google Scholar
Allert, K., Van, N. N., Michiel, C., Marjan, H. and Karel, B. (2016). The use of navigation systems in naturalistic driving. Traffic Injury Prevention, 17(3), 264–270.Google Scholar
BigData-Research (2019). China Mobile Map Market Research Report in the third quarter of 2019. Available at: https://zhuanlan.zhihu.com/p/95683092. [Accessed 28 July 2022].Google Scholar
Brun, L., Cappellania, B., Saggese, A. and Vento, M. (2014). Detection of Anomalous Driving Behaviours by Unsupervised Learning of Graphs. The 11th IEEE International Conference on Advanced Video and Signal-Based Surveillance, South Korea.Google Scholar
Chandra, R., Bhattacharya, U., Mittal, T., Li, X., Bera, A. and Manocha, D. (2019). GraphRQI: Classifying Driver Behaviours Using Graph Spectrums, The 2020 International Conference on Robotics and Automation, Paris, France.CrossRefGoogle Scholar
Chen, C. (2019). Mining, identification and prediction of individual driver's behaviour risk features based on naturalistic driving data. Ph. D. Thesis, Beijing University of Technology, Beijing, China.Google Scholar
Chen, C. F. and Chen, P. C. (2011). Applying the TAM to travelers’ usage intentions of GPS devices. Expert Systems with Applications, 38(5), 6217–6221.CrossRefGoogle Scholar
Chen, S. W., Fang, C. Y. and Tien, C. T. (2013). Driving behaviour modelling system based on graph construction. Transportation Research Part C: Emerging Technologies, 26(1), 314–330.CrossRefGoogle Scholar
Chen, C., Zhao, X., Zhang, Y., Rong, J. and Liu, X. (2019). A graphical modeling method for individual driving behaviour and its application in driving safety analysis using GPS data. Transportation Research Part F: Traffic Psychology and Behaviour, 63, 118–134.CrossRefGoogle Scholar
Dalton, P., Agarwal, P., Fraenkel, N., Baichoo, J. and Masry, A. (2013). Driving with navigational instructions: Investigating user behaviour and performance. Accident Analysis and Prevention, 50, 298–303.CrossRefGoogle ScholarPubMed
Ding, H., Zhao, X., Rong, J. and Ma, J. (2013). Experimental research on the effectiveness of speed reduction markings based on driving simulation. A case study. Accident Analysis and Prevention, 60, 211–218.CrossRefGoogle ScholarPubMed
Emmerson, C., Guo, W., Blythe, P., Namdeo, A. and Edwards, S. (2013). Fork in the road: In-vehicle navigation systems and older drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 21, 173–180.CrossRefGoogle Scholar
Gong, X., Pei, T., Sun, J. and Luo, M. (2011). Review of the research progresses in trajectory clustering methods. Progress in Geography, 30(5), 522–534.Google Scholar
Hu, L. (2017). Research on the influence of navigation function of smart phone on driving performance. Master Thesis, Chang'an University, Xi'an, China.Google Scholar
Jensen, B. S., Skov, M. B. and Thiruravichandran, N. (2010). Studying Driver Attention and Behaviour for Three Configurations of GPS Navigation in Real Traffic Driving. Proceedings of the 28th International Conference on Human Factors in Computing Systems, New York, USA.CrossRefGoogle Scholar
Large, D. R. and Burnett, G. E. (2014). The effect of different navigation voices on trust and attention while using in-vehicle navigation systems. Journal of Safety Research, 49(7), 69–75.CrossRefGoogle ScholarPubMed
Lavie, T., Oron-Gilad, T. and Meyer, J. (2011). Aesthetics and usability of in-vehicle navigation displays. International Journal of Human-Computer Studies, 69(1–2), 80–99.CrossRefGoogle Scholar
Li, R. and Liu, Y. (2013). A discussion about the application and problems of voice prompt in digital map for car navigation. Geomatics World, 20(5), 79–83.Google Scholar
Li, L. and Yuan, M. (2011). Influential factors analysis of drivers’ mental workload with the use of vehicle navigation system. Journal of Safety and Environment, 11(6), 202–204.Google Scholar
Lin, C. T., Wu, H. C. and Chien, T. Y. (2010). Effects of e-map format and sub-windows on driving performance and glance behaviour when using an in-vehicle navigation system. International Journal of Industrial Ergonomics, 40, 330–336.CrossRefGoogle Scholar
Liu, Y. C. and Wen, M. H. (2004). Comparison of head-up display (HUD) versus head-down display (HDD): Driving performance of commercial vehicle operators in Taiwan. International Journal of Human-Computer Studies, 61, 679–697.CrossRefGoogle Scholar
Liu, C., Qi, H. and Chen, C. (2019). Graph expression method based on risk characteristics of safe driving behaviour. Traffic Engineering, 19(6), 13–18.Google Scholar
Park, E. and Kim, K. J. (2014). Driver acceptance of car navigation systems: integration of locational accuracy, processing speed, and service and display quality with technology acceptance model. Personal & Ubiquitous Computing, 18, 503–513.CrossRefGoogle Scholar
Qi, H., Liu, C., Wu, Y. and Zhao, X. (2019). A graph based security description method of driving behaviour characteristics. Traffic Engineering, 19(6), 1–7.Google Scholar
Tversky, A. and Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5, 207–232.CrossRefGoogle Scholar
Uang, S. T. and Hwang, S. L. (2003). Effects on driving behaviour of congestion information and of scale of in-vehicle navigation systems. Transportation Research Part C: Emerging Technologies, 11, 423–438.CrossRefGoogle Scholar
Walker, G. H. and Stanton, N. A. (2001). Where is computing driving cars? International Journal of Human–Computer Interaction, 13(2), 203–229.CrossRefGoogle Scholar
Wu, Y. (2017). Research on eco-driving behaviour characteristics identification and feedback optimization method. Ph.D. Thesis, Beijing University of Technology, Beijing, China.Google Scholar
Wu, Y. and Zhao, X. (2018). A graph based method to describe individual driving behaviour. Traffic Engineering, 18(1), 13–17.Google Scholar
Wu, C. F., Huang, W. F. and Wu, T. C. (2009). A Study on the Design of Voice Navigation of Car Navigation System. Proceedings of the 13th International Conference on Human-Computer Interaction, San Diego, California, USA.CrossRefGoogle Scholar
Yuan, W., Guo, Y., Fu, R. and Chen, Y. (2014). Influence of urban road section types on drivers’ workload. Journal of Chang'an University (Natural Science Edition), 34(5), 95–100.Google Scholar
Yun, M., Zhao, J., Zhao, J., Weng, X. and Yang, X. (2017). Impact of in-vehicle navigation information on lane-change behaviour in urban expressway diverge segments. Accident Analysis & Prevention, 106, 53–66.CrossRefGoogle ScholarPubMed
Zhao, X., Zhang, X., Rong, J. and Ma, J. (2011). Identifying method of drunk driving based on driving behaviour. International Journal of Computational Intelligence Systems, 4(3), 361–369.Google Scholar
Zheng, Y. (2019). 44.7% of people have multiple navigation applications. How do you use navigation when driving?. Available at: https://www.youcheyihou.com/news/213181. [Accessed 28 July 2022].Google Scholar