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Research Article

Identifying drivers’ perception-reaction time (PRT) in car-following processes via two different methods using vehicle trajectory data

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Received 16 May 2023, Accepted 04 Jun 2024, Published online: 04 Jul 2024

References

  • Alagumani, S., & Natarajan, U. M. (2024). Q-learning and fuzzy logic multi-tier multi-access edge clustering for 5g v2x communication. Network (Bristol, England), 1–24. https://doi.org/10.1080/0954898X.2024.2309947
  • Broen, N. L., & Chiang, D. P. (1996). Braking response times for 100 drivers in the avoidance of an unexpected obstacle as measured in a driving simulator. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 40(18), 900–904. https://doi.org/10.1177/154193129604001807
  • Chandler, R. E., Herman, R., & Montroll, E. W. (1958). Traffic dynamics: Studies in car following. Operations Research, 6(2), 165–184. https://doi.org/10.1287/opre.6.2.165
  • Chen, B., Zhao, X., Li, Y., & Liu, X. (2024). Exploring the associations of demographics and scale measures with cognitive driving behavior among older drivers in China. Accident; Analysis and Prevention, 200, 107542. https://doi.org/10.1016/j.aap.2024.107542
  • D'Addario, P., & Donmez, B. (2019). The effect of cognitive distraction on perception-response time to unexpected abrupt and gradually onset roadway hazards. Accident; Analysis and Prevention, 127, 177–185. https://doi.org/10.1016/j.aap.2019.03.003
  • Durrani, U., Lee, C., & Shah, D. (2021). Predicting driver reaction time and deceleration: Comparison of perception-reaction thresholds and evidence accumulation framework. Accident; Analysis and Prevention, 149, 105889. https://doi.org/10.1016/j.aap.2020.105889
  • Elhenawy, M., El-Shawarby, I., & Rakha, H. (2017). Modeling the perception reaction time and deceleration level for different surface conditions using machine learning techniques. Advances in Applied Digital Human Modeling and Simulation, 481, 131–142. https://doi.org/10.1007/978-3-319-41627-4
  • Feng, W., Zhu, Q., Zhuang, J., & Yu, S. (2019). An expert recommendation algorithm based on Pearson correlation coefficient and FP-growth. Cluster Computing, 22(S3), 7401–7412. https://doi.org/10.1007/s10586-017-1576-y
  • FHWA. (2006). Next generation simulation (NGSIM). https://ops.fhwa. dot.gov/trafficanalysistools/ngsim.htm
  • Guo, H., Liu, J., Dai, Q., Chen, H., Wang, Y., & Zhao, W. (2020). A distributed adaptive triple-step nonlinear control for a connected automated vehicle platoon with dynamic uncertainty. IEEE Internet of Things Journal, 7(5), 3861–3871. https://doi.org/10.1109/JIOT.2020.2973977
  • Hu, M., Hui, F., Zhao, X., Wu, X., & Guo, J. (2020). The effect of connected automated vehicle platoon on mixed traffic flow. Transportation Evolution Impacting Future Mobility, 2020, 4660–4671.
  • Huang, L., Yao, J., Wu, W., & Yang, X. (2013). Feasibility analysis of vehicle-to-vehicle communication on suburban road. PROMET - Traffic&Transportation, 25(5), 483–493. https://doi.org/10.7307/ptt.v25i5.446
  • Huang, S., Chen, H., Wen, X., & Zhang, H. (2024). Predicting highway risk event with trajectory data: A joint approach of traffic flow and vehicle kinematics. Electronics, 13(3), 625. https://doi.org/10.3390/electronics13030625
  • Jiménez-Espada, M., García, F. M. M., & González-Escobar, R. (2023). Citizen perception and ex ante acceptance of a low-emission zone implementation in a medium-sized Spanish city. Buildings, 13(1), 249. https://doi.org/10.3390/buildings13010249
  • Jeong, J., Namdoo, K., Karbowski, D., & Rousseau, A. (2019). Implementation of model predictive control into closed-loop micro-traffic simulation for connected automated vehicle. IFAC-PapersOnLine, 52(5), 224–230. https://doi.org/10.1016/j.ifacol.2019.09.036
  • Kim, T. H., Won, J. M., Bae, G. M., & Kim, K. D. (2006). The development of pedestrian signal timing models considering pedestrian behavior and location. KSCE Journal of Civil Engineering, 10(2), 131–136. https://doi.org/10.1007/BF02823931
  • Kryszkiewicz, P., Sroka, P., Sybis, M., & Kliks, A. (2023). PATH loss and shadowing modeling for vehicle-to-vehicle communications in terrestrial TV band. IEEE Transactions on Antennas and Propagation, 71(1), 984–998. https://doi.org/10.1109/TAP.2022.3216472
  • Lan, T. T., Kanitpong, K., Tomiyama, K., Kawamura, A., & Nakatsuji, T. (2019). Effectiveness of retro-reflective tape at the rear of heavy trucks to increase visibility and reduce rear-end collisions. IATSS Research, 43(3), 176–184. https://doi.org/10.1016/j.iatssr.2019.01.002
  • Lerner, N. D. (1993). Brake perception-reaction times of older and younger drivers. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 37(2), 206–210. https://doi.org/10.1177/154193129303700211
  • Li, Q., Wang, Y., Zhang, L., Shang, Y., & Jia, Z. (2024). System design for maximizing rate in vehicle networking with reconfigurable intelligent surface (RIS) assistance. Wireless Personal Communications, 134(1), 25–41. https://doi.org/10.1007/s11277-024-10864-3
  • Li, Y., & Chen, Y. (2017). Driver vision based perception-response time prediction and assistance model on mountain highway curve. International Journal of Environmental Research and Public Health, 14(1), 31. https://doi.org/10.3390/ijerph14010031
  • Li, Y., Chen, Z., Yin, Y., & Peeta, S. (2020). Deployment of roadside units to overcome connectivity gap in transportation networks with mixed traffic. Transportation Research Part C: Emerging Technologies, 111, 496–512. https://doi.org/10.1016/j.trc.2020.01.001
  • Li, Y., Ma, Y., & Chen, Z. (2022). How can connected and automated vehicles improve merging efficiency at freeway on-ramps? Transportmetrica A: Transport Science, 20(2), 2149286. https://doi.org/10.1080/23249935.2022.2149286
  • Li, Y., Wu, D., Chen, Q., Lee, J., & Long, K. (2021). Exploring transition durations of rear-end collisions based on vehicle trajectory data: A survival modeling approach. Accident; Analysis and Prevention, 159, 106271. https://doi.org/10.1016/j.aap.2021.106271
  • Li, Y., Wu, D., Lee, J., Yang, M., & Shi, Y. (2020). Analysis of the transition condition of rear-end collisions using time-to-collision index and vehicle trajectory data. Accident Analysis & Prevention, 144, 105676. https://doi.org/10.1016/j.aap.2020.105676
  • Li, Z., Zhang, J., Rong, J., Ma, J., & Guo, Z. (2014). Measurement and comparative analysis of driver’s perception–reaction time to green phase at the intersections with and without a countdown timer. Transportation Research Part F: Traffic Psychology and Behaviour, 22, 50–62. https://doi.org/10.1016/j.trf.2013.10.012
  • Ma, X., & Andréasson, I. (2006). Driver reaction delay estimation from real data and its application in gm-type model evaluation. Transportation Research Record: Journal of the Transportation Research Board, 1965(1), 130–141. (1965), https://doi.org/10.1177/0361198106196500114
  • Moinuddin, M., Proffer, L., Vechione, M., & Khanal, A. (2024). Application of machine learning models to predict driver left turn destination lane choice behavior at urban intersections. International Journal of Transportation Science and Technology, 13, 155–170. https://doi.org/10.1016/j.ijtst.2023.12.005
  • Olson, P. L., & Sivak, M. (1986). Perception-response time to unexpected roadway hazards. Human Factors, 28(1), 91–96. https://doi.org/10.1177/001872088602800110
  • Orosz, G. (2019). Connected automated vehicle design among human-driven vehicles. IFAC-PapersOnLine, 51(34), 403–406. https://doi.org/10.1016/j.ifacol.2019.01.005
  • Pan, T. H. K., Lam, W., Sumalee, A., & Zhong, R. (2019). Multiclass multilane model for freeway traffic mixed with connected automated vehicles and regular human-piloted vehicles. Transportmetrica A: Transport Science, 17(1), 5–33. https://doi.org/10.1080/23249935.2019.1573858
  • Pan, S., & Zhang, X. M. (2023). Cooperative gigabit content distribution with network coding for mmwave vehicular networks. IEEE Transactions on Mobile Computing, 23(2), 1863–1877. https://doi.org/10.1109/TMC.2023.3241074
  • Pan, Y., Wu, Y., Xu, L., Xia, C., & Olson, D. L. (2024). The impacts of connected autonomous vehicles on mixed traffic flow: A comprehensive review. Physica A: Statistical Mechanics and Its Applications, 635, 129454. https://doi.org/10.1016/j.physa.2023.129454
  • Picallo, I., Aguirre, E., Lopez-Iturri, P., Guembe, J., Olariaga, E., Klaina, H., Marcotegui, J. A., & Falcone, F. (2022). Design, assessment and deployment of an efficient golf game dynamics management system based on flexible wireless technologies. Sensors, 23(1), 47. https://doi.org/10.3390/s23010047
  • Riexinger, L. E., & Fortenbaugh, D. M. (2021). A methodology for assessing driver perception-response time during unanticipated cross-centerline events. Traffic Injury Prevention, 22(sup1), S161–S163. https://doi.org/10.1080/15389588.2021.1982609
  • Sharma, A., Zheng, Z., Kim, J., Bhaskar, A., & Haque, M. M. (2019). Estimating and comparing response times in traditional and connected environments. Transportation Research Record: Journal of the Transportation Research Board, 2673(4), 674–684. https://doi.org/10.1177/0361198119837964
  • Shoman, M. M., Imine, H., Acerra, E. M., & Lantieri, C. (2023). Evaluation of cycling safety and comfort in bad weather and surface conditions using an instrumented bicycle. IEEE Access, 11, 15096–15108. https://doi.org/10.1109/ACCESS.2023.3242583
  • Tasseron, G., Martens, K., & van der Heijden, R. (2016). The potential impact of vehicle-to-vehicle communication on on-street parking under heterogeneous conditions. IEEE Intelligent Transportation Systems Magazine, 8(2), 33–42. https://doi.org/10.1109/MITS.2015.2506761
  • 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 Pt A), 1805–1824. https://doi.org/10.1103/physreve.62.1805
  • Wang, Y., Lin, C., Zhao, B., Gong, B., & Liu, H. (2024). Trajectory-based vehicle emission evaluation for signalized intersection using roadside LiDAR data. Journal of Cleaner Production, 440, 140971. https://doi.org/10.1016/j.jclepro.2024.140971
  • Wang, Y., Masoud, N., & Khojandi, A. (2021). Real-time sensor anomaly detection and recovery in connected automated vehicle. IEEE Transactions on Intelligent Transportation Systems, 22(3), 1411–1421. https://doi.org/10.1109/TITS.2020.2970295
  • Warshawsky-Livne, L., & Shinar, D. (2002). Effects of uncertainty, transmission type, driver age and gender on brake reaction and movement time. Journal of Safety Research, 33(1), 117–128. https://doi.org/10.1016/s0022-4375(02)00006-3
  • Xing, L., Wu, D., Tang, Y. Y., & Li, Y. (2023). Optimizing longitudinal control model parameters of connected and automated vehicles using empirical trajectory data of human drivers in risky car-following scenarios. Journal of Central South University, 30(8), 2790–2802. https://doi.org/10.1007/s11771-023-5413-6
  • Xu, H., & Deng, Y. (2018). Dependent evidence combination based on shearman coefficient and Pearson coefficient. IEEE Access, 6, 11634–11640. https://doi.org/10.1109/ACCESS.2017.2783320
  • Yao, H., & Li, X. (2020). Decentralized control of connected automated vehicle trajectories in mixed traffic at an isolated signalized intersection. Transportation Research Part C: Emerging Technologies, 121, 102846. https://doi.org/10.1016/j.trc.2020.102846
  • You, L., Xu, J., Alexandropoulos, G. C., Wang, J., Wang, W., & Gao, X. (2023). Energy efficiency maximization of massive MIMO communications with dynamic metasurface antennas. IEEE Transactions on Wireless Communications, 22(1), 393–407. https://doi.org/10.1109/TWC.2022.3194070
  • Yu, B., Yu, Z., Xin, L., & Li, X. (2019). Cooperative weaving for connected and automated vehicles to reduce traffic oscillation. Transportmetrica A: Transport.
  • Zeng, T., Ferdowsi, A., Semiari, O., Saad, W., & Hong, C. S. (2024). Convergence of communications, control, and machine learning for secure and autonomous vehicle navigation. IEEE Wireless Communications, 1–7. https://doi.org/10.1109/MWC.005.2300030
  • Zheng, J., Suzuki, K., & Fujita, M. (2013). Car-following behavior with instantaneous driver–vehicle reaction delay: A neural-network-based methodology. Transportation Research Part C: Emerging Technologies, 36, 339–351. https://doi.org/10.1016/j.trc.2013.09.010
  • Zhou, M., Qu, X., & Li, X. (2017). A recurrent neural network based microscopic car following model to predict traffic oscillation. Transportation Research Part C: Emerging Technologies, 84, 245–264. https://doi.org/10.1016/j.trc.2017.08.027
  • Zhu, M., Wang, X., & Hu, J. (2020). Impact on car following behavior of a forward collision warning system with headway monitoring. Transportation Research Part C: Emerging Technologies, 111, 226–244. https://doi.org/10.1016/j.trc.2019.12.015

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