A perspective way for judging tunnel approach zones by cognitive-behavioral chains and predictive processing model

Abstract

In tunnel approach zones (TAZs), drivers must complete a sequence of tasks, including detecting the tunnel, identifying speed limits, and decelerating to enter safely. However, current standards mandate only stopping sight distance (SSD) compliance of TAZs, which may not suffice for all of these complex driving tasks. In this study, we investigated (1) whether SSDs are sufficient for driving tasks in TAZs, (2) the impacts of restricted visibility conditions on cognitive-behavioral processes, and (3) the appropriate visibility condition of TAZs. We selected tunnels with three visibility conditions to conduct both subjective tests of perception and experiments with real vehicles. We propose a research framework called the task analysis of driving scenarios modified predictive processing model (TADS-MPPM). We then construct a multidimensional framework that includes sequences of behaviors and cognitive tasks (with 4 driving behavior nodes and 4 cognitive nodes) for spatiotemporal profiling, as well as active deceleration coefficients (safety and efficacy coefficients) and cognitive-behavioral workload (measured using the extended Jaccard coefficient). Then, we use an MPPM to visualize the evolution of driving predictions, driving behaviors, and sensory inputs during the approach to the tunnel. Finally, we explore the risk mechanisms of TAZs. The results show that SSD designs (1) delay tunnel detection, speed-limit recognition, and deceleration initiation, as well as compressing behavioral-cognitive chains, and (2) degrade safety and compliance due to overloaded operations and cognition. Conversely, ensuring that critical tunnel information is discernible at a longer decision sight distance provides the necessary margin of safety on the road. This creates adequate space and time to perform progressive deceleration to eliminate task compression and restore composed and smooth driving maneuvers.

Keywords

Road traffic safety / Tunnel approach zones / Driving behavior / Sight distance / Human factors

References

[1] American Association of State Highway and Transportation Officials (2018). A policy on geometric design of highways and streets (7th edition). Washington: IHS Markit.

[2] Amundsen, F. H., & Ranes, G. (2000). Studies on traffic accidents in Norwegian road tunnels. Tunnelling and Underground Space Technology, 15(1), 3-11.

[3] Bei, R., Du, Z., Huang, T., Mei, J., He, S., & Zhang, X. (2024a). Analysis and regulation of driving behavior in the entrance zone of freeway tunnels: Implementation of visual guidance systems in China. Accident Analysis & Prevention, 202, 107600.

[4] Bei, R., Du, Z., Lyu, N., Yu, L., & Yang, Y. (2025). Exploring the mechanism for increased risk in freeway tunnel approach zones: A perspective on temporal-spatial evolution of driving predictions, tasks, and behaviors. Accident Analysis & Prevention, 211.

[5] Bei, R., Wan, H., Du, Z., Huang, T., Han, L., & Mei, J. (2024b). Analysis and adjustment of vehicle trajectories in the entrance area of freeway tunnels: From the perspective of visual guiding system. Promet-Traffic & Transportation, 36(4), 639-653.

[6] Calvi, A., De Blasiis, M. R., & Guattari, C. (2012). An Empirical Study of the Effects of Road Tunnel on Driving Performance. Siiv-5th Internationsl Congress - Sustainbility of Road Infrastructures.

[7] Dou, S., Shen, Y., & Zhu, H. (2023). Fuzzy-based multi-criteria humanistic assessment system for city tunnels: From methodology to application. Tunnelling and Underground Space Technology (pp. 134).

[8] Du, Z., Zheng, Z., Zheng, M., Ran, B., & Zhao, X. (2014). Drivers’ visual comfort at highway tunnel portals: A quantitative analysis based on visual oscillation. Transportation Research Part D: Transport and Environment, 31, 37-47.

[9] Fang, Y., Zhou, J., Hu, H., Hao, Y., Xiao, D., & Li, S. (2022). Combination layout of traffic signs and markings of expressway tunnel entrance sections: A driving simulator study. Sustainability (Switzerland), 14(6).

[10] Feng, S., Kan, D., Zhou, L., Liu, X., Du, C., & Mao, W. (2025). Experimental study on effect of pavement background on obstacle visibility in LED lighting environment of road tunnel. Underground Space, 22, 124-136.

[11] Han, L., & Du, Z. (2025). Unraveling the complex interplay between curved tunnels and drivers’ physiological responses: An HRV perspective. Transportation Research Part F-Traffic Psychology and Behaviour, 108, 39-53.

[12] Han, L., Du, Z., He, S., & Wang, S. (2024a). An empirical investigation of driver’s eye-catching effect in the entrance zone of freeway tunnels: A naturalistic driving experiment. Transportation Research Part F: Traffic Psychology and Behaviour, 101, 92-110.

[13] Han, L., Du, Z., & Ma, A. (2024b). Evaluation of traffic signs information volume at highway tunnel entrance zone based on the visual sample entropy of novice and experienced drivers. Traffic Injury Prevention.

[14] He, S., Du, Z., Mei, J., & Han, L. (2025). Driving behavior inertia in urban tunnel diverging areas: New findings based on task-switching perspective. Transportation Research Part F-Traffic Psychology and Behaviour, 109, 1007-1023.

[15] He, S., Liang, B., Pan, G., Wang, F., & Cui, L. (2017). Influence of dynamic highway tunnel lighting environment on driving safety based on eye movement parameters of the driver. Tunnelling and Underground Space Technology, 67, 52-60.

[16] Hu, J., Sun, S., & Wang, R. (2022). Research on the influence of light source characteristics on traffic visual distance in foggy areas at night. Building and Environment, 212, 108818.

[17] Hu, J. B., Zhang, X. Q., & Guo, D. (2016). Research on the tunnel entrance night light environment of highway tunnel based on visual safety. Transaction of Beijing Institute of Technology, 36(5), 487-490, 497 (in Chinese).

[18] Hu, Y., Liu, H., & Zhu, T. (2019). Influence of spatial visual conditions in tunnel on driver behavior: Considering the route familiarity of drivers. Advances in Mechanical Engineering, 11(5).

[19] Ju, Y., Ye, S., Chen, T., Xing, G., & Chen, F. (2025). How do drivers manage speed at tunnel entrances? Insights from uncorrelated grouped random parameters duration models for model invalidation and performance recovery times. Analytic Methods in Accident Research, 45.

[20] Koustanaï, A., Boloix, E., Van Elslande, P., & Bastien, C. (2008). Formation of expectations while driving: Influence of the possibility and the necessity to anticipate on the ability to identify danger. Transportation Research Part F: Traffic Psychology and Behaviour, 11(2), 147-157.

[21] Ling, J., Li, X., Shen, Y., Chen, C., Yan, Z., Zhu, H., & Li, H. (2025). Human centric VR system development supporting fire emergency evacuation: A novel knowledge-data dual driven approach. Expert Systems with Applications, 273.

[22] Liu, H., Ding, G., Zhao, W., Wang, H., Liu, K., & Shi, L. (2011). Variation of drivers’ visual features in long-tunnel entrance section on expressway. Journal of Transportation Safety and Security, 3(1), 27-37.

[23] Liu, T., Zhu, H., Shen, Y., Xu, L., & Feng, S. (2025). Impact of tunnel lighting on driver perception and safety in foggy conditions. Building and Environment, 285, 113540.

[24] Ma, Z., Chien, S. I. J., Dong, C., Hu, D., & Xu, T. (2016). Exploring factors affecting injury severity of crashes in freeway tunnels. Tunnelling and Underground Space Technology, 59, 100-104.

[25] Ministry of Transport of the People’s Republic of China . (2014). Guidelines for Design of Lighting of Highway Tunnels (JTG/T D70/2-02-2014). Beijing: China Communications Press (in Chinese).

[26] Ministry of Transport of the People’s Republic of China . (2017). Design Specification for Highway Alignment (JTG D20-2017). Beijing, China: China Communications Press (in Chinese).

[27] Ministry of Transport of the People’s Republic of China . (2025). Traffic signs and markings-Part 3: Traffic markings (GB 5768.3-2025). Beijing: China Communications Press (in Chinese).

[28] Muhrer, E., & Vollrath, M . (2010). Expectations while car following-the consequences for driving behaviour in a simulated driving task. Accident Analysis and Prevention, 42(6), 2158-2164.

[29] Pervez, A., Huang, H., Han, C., Wang, J., & Li, Y. (2020). Revisiting freeway single tunnel crash characteristics analysis: A six-zone analytic approach. Accident Analysis and Prevention, 142.

[30] Pervez, A., Lee, J., & Huang, H. (2022). Exploring factors affecting the injury severity of freeway tunnel crashes: A random parameters approach with heterogeneity in means and variances. Accident Analysis and Prevention, 178.

[31] Saifizul, A. A., Yamanaka, H., & Karim, M. R. (2011). Empirical analysis of gross vehicle weight and free flow speed and consideration on its relation with differential speed limit. Accident Analysis and Prevention, 43(3), 1068-1073.

[32] Shao, X., Chen, F., Ma, X., & Pan, X. (2022). The impact of lighting and longitudinal slope on driver behaviour in underwater tunnels: A simulator study. Tunnelling and Underground Space Technology, 122.

[33] Wang, X., Azati, Y., Quddus, M., Cai, B., & Zhang, X. (2024). Statistical Analysis of Traffic Crashes on Mountainous Freeway Tunnel Sections. Transportation Research Record, 2678(2), 1-10.

[34] Wang, X., Liu, S., Cai, B., Hurwitz, D., Guo, Q., & Wang, X. (2023). Sample size study of driving simulator experiment for freeway design safety evaluations. Transportation Research Record, 2677(6), 73-92.

[35] Wang, X., Zhu, M., & Chen, M. (2016a). Impacts of situational urgency on drivers’ collision avoidance behaviors. Journal of Tongji University (Natural Science Edition), 44(6), 0876-0883.

[36] Wang, Y. G., Wang, L. J., Wang, C., & Zhao, Y. D. (2016b). How eye movement and driving performance vary before, during, and after entering a long expressway tunnel: considering the differences of novice and experienced drivers under daytime and nighttime conditions. SpringerPlus (pp. 5).

[37] Wickens, C. D. (2008). Multiple resources and mental workload. Human Factors, 50(3), 449-455.

[38] Xu, L., Zhu, H., Shen, Y., Ling, J., & Feng, S. (2025a). Lighting environment design evaluation with digital workflow: A life cycle framework for integrating carbon and cost reduction strategies in tunnels. Tunnelling and Underground Space Technology, 157, 106363.

[39] Xu, L., Zhu, H., Shen, Y., Liu, T., Ling, J., & Feng, S. (2025b). VR-based evaluation of fog-adaptive tunnel lighting and navigation aids on collision risk mitigation. Advanced Engineering Informatics, 68, 103777.

[40] Yan, G., Wang, M., Qin, P., Yan, T., Bao, Y., & Wang, X. (2022). Comparative study on drivers’ eye movement characteristics and psycho-physiological reactions at tunnel entrances in plain and high-altitude areas: A pilot study. Tunnelling and Underground Space Technology, 122.

[41] Yang, Y., Du, Z., Alonso, F., Faus, M., & He, S. (2024). Why frequent traffic accidents at highway tunnel exit? - An experimental analysis of the slack effect. Tunnelling and Underground Space Technology, 152.

[42] Yang, Y., Du, Z., Alonso, F., Faus, M., & Wan, H. (2025). Traffic safety improvement method for highway tunnel entrances based on linear guiding - An engineering practice from China. Tunnelling and Underground Space Technology, 156.

[43] Zhang, J., Zhao, X., Li, H., Qi, J., & Xing, G. (2023). Evaluation of the Connected Vehicle Environment Effectiveness at Tunnel Entrance. IEEE Transactions on Intelligent Transportation Systems, 24(11), 11699-11709.

[44] Zhang, Y., Yan, Z., Zhu, H., Shen, Y., Guo, Q., & Guo, Q. (2019). Experimental investigation of pedestrian evacuation using an extra-long steep-slope evacuation path in a high altitude tunnel fire. Sustainable Cities and Society, 46.

[45] Zhu, T., Wang, C., Yang, C., & Zhao, R. (2020). Evaluation of effectiveness of speed reduction markings on driving speed in highway tunnel entrance and exit areas. Promet - Traffic and Transportation, 32(1), 141-152.