Driver’s Shy Away Effect in Urban Extra-Long Underwater Tunnel


  • Ying Chen School of Transportation and Logistics Engineering, Wuhan University of Technology
  • Zhigang Du School of Transportation and Logistics Engineering, Wuhan University of Technology
  • Zehao Jiang School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology
  • Congjian Liu School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology
  • Xuefeng Chen Guizhou Highway Engineering Group Company Limited



traffic safety, urban extra-long underwater tunnel, shy away effect, driving behavior, driving characteristics


For urban extra-long underwater tunnels, the obstacle space formed by the tunnel walls on both sides has an impact on the driver's driving. The aim of this study is to investigate the shy away characteristics of drivers in urban extra-long underwater tunnels. Using trajectory offset and speed data obtained from real vehicle tests, the driving behaviour at different lanes of an urban extra-long underwater tunnel was investigated, and a theory of shy away effects and indicators of sidewall shy away deviation for quantitative analysis were proposed. The results show that the left-hand lane has the largest offset and driving speed from the sidewall compared to the other two lanes. In the centre lane there is a large fluctuation in the amount of deflection per 50 seconds of driving, increasing the risk of two-lane collisions. When the lateral clearances are increased from 0.5 m to 2.19 m on the left and 1.29 m on the right, the safety needs of drivers can be better met. The results of this study have implications for improving traffic safety in urban extra-long underwater tunnels and for the improvement of tunnel traffic safety facilities.


Jiao FT, et al. Research on drivers’ visual characteristics in different curvatures and turning conditions of the extra-long urban underwater tunnels. Tunnelling and Underground Space Technology. 2020;99:103360. DOI: 10.1016/j.tust.2020.103360.

Wang J, et al. Crash analysis of Chinese freeway tunnel groups using a five-zone analytic approach. Tunnelling and Underground Space Technology. 2018;82:358–365. DOI: 10.1016/j.tust.2018.08.037.

Caliendo C, Guglielmo MLD, Guida M. A crash-prediction model for road tunnels. Accident Analysis and Prevention. 2013;55:107–115. DOI: 10.1016/j.aap.2013.02.024.

Bassan S. Overview of traffic safety aspects and design in road tunnels. IATSS Research. 2016;40(1):35–46. DOI: 10.1016/j.iatssr.2016.02.002.

Calvi A, de Blasiis MR, Guattari C. An empirical study of the effects of road tunnel on driving performance. Procedia Social Behavioral Sciences. 2012;53:1098–1108. DOI: 10.1016/j.sbspro.2012.09.959.

Ye F, Su CH. Safety analysis and countermeasures for highway tunnel operation. Modern Tunnelling Technology. 2003;01:31–33.

Song ZX, et al. Study on driving safety evaluation based on tunnel sidewall effect. Highway Engineering. 2010;35(5):10–13+18.

Hu HY, et al. Cost-sensitive semi-supervised deep learning to assess driving risk by application of naturalistic vehicle trajectories. Expert Systems with Applications. 2022;178:115041. DOI: 10.1016/j.eswa.2021.115041.

Shangguan QQ, et al. A proactive lane-changing risk prediction framework considering driving intention recognition and different lane-changing patterns. Accident Analysis and Prevention. 2022;164:106500. DOI: 10.1016/j.aap.2021.106500.

Anik D, Mohamed MA. Exploring the effect of fog on lane-changing characteristics utilizing the SHRP2 naturalistic driving study data. Journal of Transportation Safety & Security. 2019;13(5):477–502. DOI: 10.1080/19439962.2019.1645777.

Chen KJ, Liu JQ. Study on plane alignment coordination of highway tunnel entrance based on deviation limit of driving track. Highway Traffic Technology (Application Technology Edition). 2017;13(03):188–189.

Ouyang PY, et al. Traffic safety analysis of inter-tunnel weaving section with conflict prediction models. Journal of Transportation Safety & Security. 2022;14(4):630–654. DOI: 10.1080/19439962.2020.1801924.

Qin XC, et al. How does tunnel interior color environment influence driving behaviour? Quantitative analysis and assessment experiment. Tunnelling and Underground Space Technology. 2020;98:103320. DOI: 10.1016/j.tust.2020.103320.

Zhao XH, et al. Evaluation of the effect of decorated sidewall in tunnels based on driving behaviour characteristics. Tunnelling and Underground Space Technology. 2022;127:104591. DOI: 10.1016/j.tust.2022.104591.

Shah D, Lee C. Analysis of effects of driver’s evasive action time on rear-end collision risk using a driving simulator. Journal of Safety Research. 2021;78:242–250. DOI: 10.1016/j.jsr.2021.06.001.

Roque C, et al. Topic analysis of road safety inspections using latent dirichlet allocation: A case study of roadside safety in Irish main roads. Accident Analysis and Prevention. 2019;131:336–349. DOI: 10.1016/j.aap.2019.07.021.

Dai ZH, et al. Research on Vehicle trajectory deviation characteristics on freeways using natural driving trajectory data. International Journal of Environmental Research and Public Health. 2022;19:14695. DOI: 10.3390/ijerph192214695.

Zhuang JF, et al. Vehicles trajectory oscillation characteristics and passenger cars' lane width for freeways. Journal of Transportation Systems Engineering and Information Technology. 2023;23(1):324–336.

Shang T, Bai JR. Study on the influence of longitudinal visual illusion deceleration markings on ramp sections of urban roads in mountainous areas. Journal of China and Foreign Highway. 2021;41(2):338–343. DOI: 10.14048/j.issn.1671-2579.2021.02.067.

Jiang SP, et al. Specifications for design of highway tunnels. China: Ministry of Transport of the People's Republic of China; 2018.

Lee SH, Lee SB. Test evaluation method for lane keeping assistance system using dual cameras. Machines. 2021;9(12):310–310. DOI: 10.3390/machines9120310.

Calvi A, Benedetto A, Blasiis MRD. A driving simulator study of driver performance on deceleration lanes. Accident Analysis and Prevention. 2012;45(1):195–203. DOI: 10.1016/j.aap.2011.

Wang X, et al. The influence of combined alignments on lateral acceleration on mountainous freeways: A driving simulator study. Accident Analysis and Prevention. 2015;76:110–117. DOI: 10.1016/j.aap.2015.01.003.

Lyu NC, et al. The effect of gender, occupation and experience on behaviour while driving on a freeway deceleration lane based on field operational test data. Accident Analysis and Prevention. 2018;121:82–93. DOI: 10.1016/j.aap.2018.07.034.

Pervez A, et al. Revisiting freeway single tunnel crash characteristics analysis: A six-zone analytic approach. Accident Analysis and Prevention. 2020;142:105542. DOI: 10.1016/j.aap.2020.105542.

Hu SW. Research on active lane change system based on driving intention recognition. Master Thesis. Tsinghua University; 2019.

Wang SS, et al. Drivers’ visual characteristics in small-radius optically long tunnels on rural roads. Tunnelling and Underground Space Technology. 2021;113:103969. DOI: 10.1016/j.tust.2021.103969.

Du ZG, et al. Research on light environment improvement framework of highway tunnel based on visual guidance. China Journal of Highway and Transport. 2018;31(4):122–129.

Du ZG, et al. Experimental study on the efficacy of retroreflective rings in the curved freeways tunnels. Tunnelling and Underground Space Technology. 2021;110:103813. DOI: 10.1016/j.tust.2021.103813.

Li YA. The operating speed prediction and coordination evaluation of extra-long underwater tunnels based on VISSIM. Master's Thesis. Huazhong University of Science and Technology; 2019.

Ma ZY, Fang SE, Pan HZ. Analysis on vehicle speed distribution and control effectiveness of extralong tunnel. Chinese Journal of Underground Space and Engineering. 2021;217(S2):650–655+676.

Eigentler K. Experiences with LED-based visual guidance systems in tunnels. Tunnelling and Underground Space Technology. 2006;21(3–4):325. DOI: 10.1016/j.tust.2005.12.039.

Patten C, Mårdh S. Interior tunnel design and traffic safety aspects. Road safety on four continents: 16th International Conference, 15–17 May 2013, Beijing, China. 2013. p. 1–13.

Schroer J, et al. A policy on geometric design of highways and streets. American Association of State Washington: Highway and Transportation Officials; 2018.

Yamada N, Ota Y. Safety systems for the Trans-Tokyo Bay Highway Tunnel project. Tunnelling and Underground Space Technology. 1999;14(1):3–12. DOI: 10.1016/S0886-7798(99)00008-5.

Yu MJ, et al. Code for design of urban underground road engineering. China: Ministry of Housing and Urban Rural Development of the People's Republic of China; 2015.

Liu G. Research on speed increasing technology of expressway tunnel based on lateral width. Master Thesis. Chang’an University; 2021.

Wang XS, Wang T, Chen YX. Influence of lane width on driving behaviour on freeway tunnel: Driving simulator study. China Safety Science Journal. 2016;26(6):36–41.

Jiao FT, et al. Influence of different visual guiding facilities in urban road tunnel on driver’s spatial right-of-way perception. Accident Analysis and Prevention. 2022;172:106688. DOI: 10.1016/j.aap.2022.106688.




How to Cite

Chen, Y., Du, Z., Jiang, Z., Liu, C., & Chen, X. (2023). Driver’s Shy Away Effect in Urban Extra-Long Underwater Tunnel. Promet - Traffic&Transportation, 35(4), 552–566.