Incorporating Behavioral Adaptation of Human Drivers in  Predicting Traffic Efficiency of Mixed Traffic: A Case Study of Priority T-Intersections

Nagarjun Reddy; Narayana Raju; Haneen Farah; Serge Hoogendoorn

doi:10.59490/ejtir.2025.25.2.7557

Incorporating Behavioral Adaptation of Human Drivers in Predicting Traffic Efficiency of Mixed Traffic

A Case Study of Priority T-Intersections

Authors

Nagarjun Reddy Department of Transport and Planning, Delft University of Technology, Netherlands https://orcid.org/0000-0002-9523-4453
Narayana Raju Department of Transport and Planning, Delft University of Technology, Netherlands https://orcid.org/0000-0002-3561-5676
Haneen Farah Department of Transport and Planning, Delft University of Technology, Netherlands https://orcid.org/0000-0002-2919-0253
Serge Hoogendoorn Department of Transport and Planning, Delft University of Technology, Netherlands https://orcid.org/0000-0002-1579-1939

DOI:

https://doi.org/10.59490/ejtir.2025.25.2.7557

Keywords:

Behavioral adaptation, Mixed traffic, Automated Vehicles, Gap acceptance, Microscopic traffic simulation, Traffic efficiency

Abstract

As automated vehicles (AVs) are increasingly being deployed on our road network, it becomes important to understand how human drivers interact with them in mixed traffic. This research investigates how mixed traffic factors affect human-driven vehicles (HDV) gap acceptance behavior at a priority T-intersection. Using a driving simulator, four scenarios were tested by varying AV driving style (less defensive, more defensive, and HDV-like) and AV recognizability (distinguishable or not from HDVs). Gap acceptance models were estimated based on the collected trajectory data. These models were then implemented in SUMO, a microscopic traffic simulation platform, where a T-intersection network was set up. Simulation runs varied based on AV driving style, recognizability, penetration rate of AVs (0-75% in 25% increments), and whether HDV behavioral adaptation was considered. The minor road had only traffic composed of HDVs, while the major road had both AVs and HDVs (i.e., mixed traffic). Vehicle delay and queue length on the minor road were used as performance indicators to evaluate the effect of mixed traffic, on the travel efficiency of minor road vehicles. The results indicate that minor road vehicle delays increase with higher AV penetration rates in traffic on the major road. Recognizable and less defensive AVs causes more delays for minor road vehicles compared to other scenarios. Ignoring behavioral adaptation led to a delay underestimation of up to 75% for minor road vehicles. In conclusion, there is a behavioral adaptation in gap acceptance of HDVs in mixed traffic environments. This is affected by AVs recognizability, the driving style of AVs, and their penetration rate. Taking into account the behavioral adaptation is essential for accurately assessing traffic efficiency in mixed traffic conditions. This will help to direct policy decisions related to AVs recognizability and driving style and about infrastructural interventions.

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References

Arvin, R., Khattak, A. J., Kamrani, M., & Rio-Torres, J. (2020). Safety evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections. Journal of Intelligent Transportation Systems, 25(2), 170–187. https://doi.org/10.1080/15472450.2020.1834392

Beanland, V., Lenné, M. G., Candappa, N., & Corben, B. (2013a). Gap acceptance at stop-controlled T-intersections in a simulated rural environment. Transportation Research Part F: Traffic Psychology and Behaviour, 20, 80–89. https://doi.org/10.1016/J.TRF.2013.05.006

Beanland, V., Lenné, M. G., Candappa, N., & Corben, B. (2013b). Gap acceptance at stop-controlled T-intersections in a simulated rural environment. Transportation Research Part F: Traffic Psychology and Behaviour, 20, 80–89. https://doi.org/10.1016/J.TRF.2013.05.006

Calvert, S. C., Schakel, W. J., & van Lint, J. W. C. (2017). Will automated vehicles negatively impact traffic flow? Journal of Advanced Transportation, 2017. https://doi.org/10.1155/2017/3082781

Chunxi, H., Xiao, W., Dengbo, H., & Sisi, J. (2022). Sharing the road: how human drivers interact with autonomous vehicles on highways. Proceedings of the Human Factors and Ergonomic Society Annual Meeting, 66(1), 1437–1441. https://doi.org/10.1177/1071181322661165

Dutta, M., & Ahmed, M. A. (2018). Gap acceptance behavior of drivers at uncontrolled T-intersections under mixed traffic conditions. Journal of Modern Transportation, 26(2), 119–132. https://doi.org/10.1007/S40534-017-0151-9/FIGURES/10

Fuest, T., Feierle, A., Schmidt, E., & Bengler, K. (2020). Effects of Marking Automated Vehicles on Human Drivers on Highways. Information 2020, Vol. 11, Page 286, 11(6), 286. https://doi.org/10.3390/INFO11060286

Gouy, M., Wiedemann, K., Stevens, A., Brunett, G., & Reed, N. (2014). Driving next to automated vehicle platoons: How do short time headways influence non-platoon drivers’ longitudinal control? Transportation Research Part F: Traffic Psychology and Behaviour, 27(PB), 264–273. https://doi.org/10.1016/J.TRF.2014.03.003

Greenblatt, J. B., & Shaheen, S. (2015). Automated Vehicles, On-Demand Mobility, and Environmental Impacts. Current Sustainable/Renewable Energy Reports 2015, 2(3), 74–81. https://doi.org/10.1007/S40518-015-0038-5

Hensch, A. C., Beggiato, M., & Krems, J. F. (2023). Drivers’ gap acceptance during parking maneuvers as a basis for initiating driving actions in automated vehicles. Transportation research part F: traffic psychology and behaviour, 92, 133-142.

Kulmala, R., & Rama, P. (2013). Definition of Behavioural Adaptation. In Behavioural adaptation and road safety: Theory, evidence and action.

Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flotterod, Y. P., Hilbrich, R., Lucken, L., Rummel, J., Wagner, P., & Wiebner, E. (2018). Microscopic Traffic Simulation using SUMO. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018-November, 2575–2582. https://doi.org/10.1109/ITSC.2018.8569938

Ma, Z., & Zhang, Y. (2022). Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers’ Driving Styles. Human Factors. https://doi.org/10.1177/00187208221088358/ASSET/IMAGES/LARGE/10.1177_00187208221088358-FIG6.JPEG

Mahdinia, I., Mohammadnazar, A., Arvin, R., & Khattak, A. J. (2021). Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles. Accident Analysis & Prevention, 152, 106006. https://doi.org/10.1016/J.AAP.2021.106006

Makridis, M., Mattas, K., Anesiadou, A., & Ciuffo, B. (2021). OpenACC. An open database of car-following experiments to study the properties of commercial ACC systems. Transportation Research Part C: Emerging Technologies, 125, 103047. https://doi.org/10.1016/J.TRC.2021.103047

Nyholm, S., & Smids, J. (2020). Automated cars meet human drivers: responsible human-robot coordination and the ethics of mixed traffic. Ethics and Information Technology, 22(4), 335–344. https://doi.org/10.1007/S10676-018-9445-9/METRICS

Olia, A., Razavi, S., Abdulhai, B., & Abdelgawad, H. (2017). Traffic capacity implications of automated vehicles mixed with regular vehicles. Https://Doi-Org.Tudelft.Idm.Oclc.Org/10.1080/15472450.2017.1404680, 22(3), 244–262. https://doi.org/10.1080/15472450.2017.1404680

Papadoulis, A., Quddus, M., & Imprialou, M. (2019). Evaluating the safety impact of connected and autonomous vehicles on motorways. Accident Analysis and Prevention, 124, 12–22. https://doi.org/10.1016/J.AAP.2018.12.019

Piao, J., McDonald, M., Hounsell, N., Graindorge, M., Graindorge, T., & Malhene, N. (2016). Public Views towards Implementation of Automated Vehicles in Urban Areas. Transportation Research Procedia, 14, 2168–2177. https://doi.org/10.1016/J.TRPRO.2016.05.232

Rahmati, Y., Khajeh Hosseini, M., Talebpour, A., Swain, B., & Nelson, C. (2019). Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers. Transportation Research Record. https://doi.org/10.1177/0361198119862628

Raju, N., Schakel, W., Reddy, N., Dong, Y., & Farah, H. (2022). Car-Following Properties of a Commercial Adaptive Cruise Control System: A Pilot Field Test. Transportation Research Record, 2676(7), 128–143. https://doi.org/10.1177/03611981221077085

Razmi Rad, S., Farah, H., Taale, H., van Arem, B., & Hoogendoorn, S. P. (2021). The impact of a dedicated lane for connected and automated vehicles on the behaviour of drivers of manual vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 82, 141–153. https://doi.org/10.1016/J.TRF.2021.08.010

Reddy, N., Hoogendoorn, S. P., & Farah, H. (2022). How do the recognizability and driving styles of automated vehicles affect human drivers’ gap acceptance at T- Intersections? Transportation Research Part F: Traffic Psychology and Behaviour, 90, 451–465. https://doi.org/10.1016/J.TRF.2022.09.018

RStudio Team. (2022). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/

Schakel, W. J., Van Arem, B., & Netten, B. D. (2010). Effects of cooperative adaptive cruise control on traffic flow stability. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 759–764. https://doi.org/10.1109/ITSC.2010.5625133

Schoenmakers, M., Yang, D., & Farah, H. (2021). Car-following behavioural adaptation when driving next to automated vehicles on a dedicated lane on motorways: A driving simulator study in the Netherlands. Transportation Research Part F: Traffic Psychology and Behaviour, 78, 119–129. https://doi.org/10.1016/J.TRF.2021.01.010

Schumann, J. F., Kober, J., & Zgonnikov, A. (2023). Benchmarking behavior prediction models in gap acceptance scenarios. IEEE Transactions on Intelligent Vehicles, 8(3), 2580-2591.

Soni, S., Reddy, N., Tsapi, A., van Arem, B., & Farah, H. (2022). Behavioral adaptations of human drivers interacting with automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 86, 48–64. https://doi.org/10.1016/J.TRF.2022.02.002

Stange, V., Kühn, M., & Vollrath, M. (2022). Manual drivers’ experience and driving behavior in repeated interactions with automated Level 3 vehicles in mixed traffic on the highway. Transportation Research Part F: Traffic Psychology and Behaviour, 87, 426–443. https://doi.org/10.1016/J.TRF.2022.04.019

Taieb-Maimon, M., & Shinar, D. (2016). Minimum and Comfortable Driving Headways: Reality versus Perception: Http://Dx.Doi.Org/10.1518/001872001775992543, 43(1), 159–172. https://doi.org/10.1518/001872001775992543

Taubman-Ben-Ari, O., Mikulincer, M., & Gillath, O. (2004). The multidimensional driving style inventory - Scale construct and validation. Accident Analysis and Prevention, 36(3), 323–332. https://doi.org/10.1016/S0001-4575(03)00010-1

Treiber, M., Hennecke, A., & Helbing, D. (2000). Congested traffic states in empirical observations and microscopic simulations. Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 62(2), 1805–1824. https://doi.org/10.1103/PhysRevE.62.1805

Trende, A., Unni, A., Weber, L., Rieger, J. W., & Luedtke, A. (2019). An investigation into human-autonomous vs. Human-human vehicle interaction in time-critical situations. ACM International Conference Proceeding Series, 303–304. https://doi.org/10.1145/3316782.3321544

Wang, Y., Farah, H., Yu, R., Qiu, S., & Arem, B. van. (2023). Characterizing Behavioral Differences of Autonomous Vehicles and Human-Driven Vehicles at Signalized Intersections Based on Waymo Open Dataset. Https://Doi-Org.Tudelft.Idm.Oclc.Org/10.1177/03611981231165783, 036119812311657. https://doi.org/10.1177/03611981231165783

Wen, X., Cui, Z., & Jian, S. (2022). Characterizing car-following behaviors of human drivers when following automated vehicles using the real-world dataset. Accident Analysis & Prevention, 172, 106689. https://doi.org/10.1016/J.AAP.2022.106689

Winkelbauer, M., Donabauer, M., Pommer, A., & Jansen, R. (2019). Naturalistic data on time headway behind motorcycles and other vehicles. Safety Science, 119, 162–173. https://doi.org/10.1016/J.SSCI.2019.01.020

Ye, L., & Yamamoto, T. (2018). Modeling connected and autonomous vehicles in heterogeneous traffic flow. Physica A: Statistical Mechanics and Its Applications, 490, 269–277. https://doi.org/10.1016/J.PHYSA.2017.08.015

Zhao, X., Wang, Z., Xu, Z., Wang, Y., Li, X., & Qu, X. (2020). Field experiments on longitudinal characteristics of human driver behavior following an autonomous vehicle. Transportation Research Part C: Emerging Technologies, 114, 205–224. https://doi.org/10.1016/j.trc.2020.02.018

Zohdy, I., Sadek, S., & Rakha, H. A. (2010). Empirical analysis of effects of wait time and rain intensity on driver left-turn gap acceptance behavior. Transportation Research Record, 2173, 1–10. https://doi.org/10.3141/2173-01