Consistency Analysis of Drivers’ Car-Following Behaviors
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 4
Abstract
Car-following models are calibrated to account for various driver behaviors such as speed and space headway. Because drivers do not all drive the same way, they are typically classified based on their level, or profile, of aggressiveness. This approach to model calibration assumes that a single set of car-following parameters applies to an individual driver consistently, that is, during the entire driving period a study considers. The purpose of this research is to challenge that assumption by analyzing the heterogeneity and inconsistency of driver behaviors in different traffic conditions. A total of 3,262 urban expressway car-following periods from 51 drivers were extracted from the Shanghai Naturalistic Driving Study database. The intelligent driver model (IDM) was selected as this study’s car-following model for its favorable performance in highly dynamic situations; its parameters, that is, desired speed, desired time headway, maximum acceleration, comfortable deceleration, acceleration exponent, and minimum spacing, were calibrated. To reflect the effect of traffic conditions, the car-following periods were categorized into three regimes from low-speed to high-speed. Two-way analysis of variance was used to evaluate heterogeneity and inconsistency in the car-following parameters. The findings of this study show that (1) the main significant variable that distinguished driver profile was desired time headway, while the comfortable acceleration was commonly affected by both driver profile and speed regime; (2) variability of individual driver parameter estimates greatly depended on the traffic regime (low-, medium-, and high-speed), showing that driver behavior was highly influenced by traffic conditions; and (3) speed information was demonstrated to be a useful tool by which to divide car-following periods for modeling, as the car-following parameters exhibited distinct differences among the regimes in the varied traffic environment.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
Acknowledgments
This study was sponsored by the Chinese National Science Foundation (51878498). The authors are grateful to Barbara Rau Kyle for her helpful edit.
References
Bando, M., K. Hasebe, A. Nakayama, A. Shibata, and Y. Sugiyama. 1995. “Dynamical model of traffic congestion and numerical simulation.” Phys. Rev. E 51 (2): 1035–1042. https://doi.org/10.1103/PhysRevE.51.1035.
Berthaume, A. L., R. M. James, B. E. Hammit, C. Foreman, and C. L. Melson. 2018. “Variations in driver behavior: An analysis of car-following behavior heterogeneity as a function of road type and traffic condition.” Transp. Res. Rec. 2672 (37): 31–44. https://doi.org/10.1177/0361198118798713.
Brockfeld, E., R. D. Kühne, and P. Wagner. 2005. “Calibration and validation of microscopic models of traffic flow.” Transp. Res. Rec. 1934 (1): 179–187. https://doi.org/10.1177/0361198105193400119.
Chen, D., J. Laval, Z. Zheng, and S. Ahn. 2012. “A behavioral car-following model that captures traffic oscillations.” Transp. Res. Part B Methodol. 46 (6): 744–761. https://doi.org/10.1016/j.trb.2012.01.009.
Higgs, B., and M. M. Abbas. 2014. “Multi-resolution comparison of car-following models using naturalistic data.” In Proc., Transportation Research Board 93rd Annual Meeting. Washington, DC: Transportation Research Board.
Hollander, Y., and R. Liu. 2008. “The principles of calibrating traffic microsimulation models.” Transportation 35 (3): 347–362. https://doi.org/10.1007/s11116-007-9156-2.
Huang, Y. X., R. Jiang, H. M. Zhang, M. B. Hu, J. F. Tian, B. Jia, and Z. Y. Gao. 2018. “Experimental study and modeling of car-following behavior under high speed situation.” Transp. Res. Part C Emerging Technol. 97 (Dec): 194–215. https://doi.org/10.1016/j.trc.2018.10.022.
Kesting, A., and M. Treiber. 2008. “Calibrating car-following models by using trajectory data: Methodological study.” Transp. Res. Rec. 2088 (1): 148–156. https://doi.org/10.3141/2088-16.
Kim, I., T. Kim, and K. Sohn. 2013. “Identifying driver heterogeneity in car-following based on a random coefficient model.” Transp. Res. Part C Emerging Technol. 36 (Nov): 35–44. https://doi.org/10.1016/j.trc.2013.08.003.
Lindorfer, M., C. F. Mecklenbräuker, and G. Ostermayer. 2018. “Modeling the imperfect driver: Incorporating human factors in a microscopic traffic model.” IEEE Trans. Intell. Transp. Syst. 19 (9): 2856–2870. https://doi.org/10.1109/TITS.2017.2765694.
Liu, T., and Selpi. 2020. “Comparison of car-following behavior in terms of safety indicators between China and Sweden.” IEEE Trans. Intell. Transp. Syst. 21 (9): 3696–3705. https://doi.org/10.1109/TITS.2019.2931797.
MathWorks. 2022. “Genetic algorithm options.” Accessed March 14, 2022. https://www.mathworks.com/help/gads/genetic-algorithm-options.html.
Montgomery, D. C. 2012. Design and analysis of experiments, 187–206. Hoboken, NJ: Wiley.
Murphey, Y. L., R. Milton, and L. Kiliaris. 2009. “Driver’s style classification using jerk analysis.” In Proc., IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, 23–28. New York: IEEE.
Ossen, S., and S. P. Hoogendoorn. 2011. “Heterogeneity in car-following behavior: Theory and empirics.” Transp. Res. Part C Emerging Technol. 19 (2): 182–195. https://doi.org/10.1016/j.trc.2010.05.006.
Ossen, S., S. P. Hoogendoorn, and B. G. Gorte. 2006. “Interdriver differences in car-following: A vehicle trajectory-based study.” Transp. Res. Rec. 1965 (1): 121–129. https://doi.org/10.1177/0361198106196500113.
Pariota, L., F. Galante, and G. N. Bifulco. 2016. “Heterogeneity of driving behaviors in different car-following conditions.” Period. Polytech. Transp. Eng. 44 (2): 105–114. https://doi.org/10.3311/PPtr.8609.
Park, M., Y. Kim, and H. Yeo. 2020. “Development of an asymmetric car-following model and simulation validation.” IEEE Trans. Intell. Transp. Syst. 21 (8): 3513–3524. https://doi.org/10.1109/TITS.2019.2930320.
Paschalidis, E., C. F. Choudhury, and S. Hess. 2019. “Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour.” Anal. Methods Accid. Res. 22 (Jun): 100089. https://doi.org/10.1016/j.amar.2019.02.001.
Pourabdollah, M., E. Bjärkvik, F. Fürer, B. Lindenberg, and K. Burgdorf. 2017. “Calibration and evaluation of car following models using real-world driving data.” In Proc., 2017 IEEE 20th Int. Conf. on Intelligent Transportation Systems (ITSC), 1–6. New York: IEEE.
Punzo, V., M. Montanino, and B. Ciuffo. 2015. “Do we really need to calibrate all the parameters? Variance-based sensitivity analysis to simplify microscopic traffic flow models.” IEEE Trans. Intell. Transp. Syst. 16 (1): 184–193. https://doi.org/10.1109/TITS.2014.2331453.
Punzo, V., and F. Simonelli. 2005. “Analysis and comparison of microscopic traffic flow models with real traffic microscopic data.” Transp. Res. Rec. 1934 (1): 53–63. https://doi.org/10.1177/0361198105193400106.
Saifuzzaman, M., Z. Zheng, M. Mazharul Haque, and S. Washington. 2015. “Revisiting the task–capability interface model for incorporating human factors into car-following models.” Transp. Res. Part B Methodol. 82 (Dec): 1–19. https://doi.org/10.1016/j.trb.2015.09.011.
Sarwar, M. T., P. C. Anastasopoulos, N. Golshanic, and K. F. Hulme. 2017. “Grouped random parameters bivariate probit analysis of perceived and observed aggressive driving behavior: A driving simulation study.” Anal. Methods Accid. Res. 13 (Mar): 52–64. https://doi.org/10.1016/j.amar.2016.12.001.
Soria, I., L. Elefteriadou, and A. Kondyli. 2014. “Assessment of car-following models by driver type and under different traffic, weather conditions using data from an instrumented vehicle.” Simul. Modell. Pract. Theory 40 (Jan): 208–220. https://doi.org/10.1016/j.simpat.2013.10.002.
Sun, P., X. Wang, and M. Zhu. 2021. “Modeling car-following behavior on freeways considering driving style.” J. Transp. Eng. Part A Syst. 147 (12): 04021083. https://doi.org/10.1061/JTEPBS.0000584.
Tang, T. Q., Y. Gui, and J. Zhang. 2022. “ATAC-based car-following model for level 3 autonomous driving considering driver’s acceptance.” IEEE Trans. Intell. Transp. Syst. 23 (8): 10309–10321. https://doi.org/10.1109/TITS.2021.3090974.
Tang, T. Q., Y. Gui, J. Zhang, and T. Wang. 2020. “Car-following model based on deep learning and Markov theory.” J. Transp. Eng. Part A Syst. 146 (9): 04020104. https://doi.org/10.1061/JTEPBS.0000430.
Taylor, J., X. Zhou, N. M. Rouphail, and R. J. Porter. 2015. “Method for investigating intradriver heterogeneity using vehicle trajectory data: A dynamic time warping approach.” Transp. Res. Part B Methodol. 73 (Mar): 59–80. https://doi.org/10.1016/j.trb.2014.12.009.
Tibshirani, R., G. Walther, and T. Hastie. 2001. “Estimating the number of clusters in a data set via the gap statistic.” J. R. Stat. Soc. B 63 (2): 411–423. https://doi.org/10.1111/1467-9868.00293.
Treiber, M., A. Hennecke, and D. Helbing. 2000. “Congested traffic states in empirical observations and microscopic simulations.” Phys. Rev. E 62 (2): 1805–1824. https://doi.org/10.1103/PhysRevE.62.1805.
van Hinsbergen, C. P. I. J., W. J. Schakel, V. L. Knoop, J. W. C. van Lint, and S. P. Hoogendoorn. 2015. “A general framework for calibrating and comparing car-following models.” Transportmetrica A: Transp. Sci. 11 (5): 420–440. https://doi.org/10.1080/23249935.2015.1006157.
Wagner, P. 2012. “Analyzing fluctuations in car-following.” Transp. Res. Part B Methodol. 46 (10): 1384–1392. https://doi.org/10.1016/j.trb.2012.06.007.
Wang, H., W. Wang, J. Chen, and M. Jing. 2010. “Using trajectory data to analyze intradriver heterogeneity in car-following.” Transp. Res. Rec. 2188 (1): 85–95. https://doi.org/10.3141/2188-10.
Wang, Y., J. Zhang, and G. Lu. 2019. “Influence of driving behaviors on the stability in car following.” IEEE Trans. Intell. Transp. Syst. 20 (3): 1081–1098. https://doi.org/10.1109/TITS.2018.2837740.
Zhang, J., T. Q. Tang, and T. Wang. 2019. “Some features of car-following behaviour in the vicinity of signalised intersection and how to model them.” IET Intel. Transport Syst. 13 (11): 1686–1693. https://doi.org/10.1049/iet-its.2018.5510.
Zheng, Y., S. He, R. Yi, F. Ding, B. Ran, P. Wang, and Y. Lin. 2020. “Categorizing car-following behaviors: Wavelet-based time series clustering approach.” J. Transp. Eng. Part A Syst. 146 (8): e04020072. https://doi.org/10.1061/jtepbs.0000402.
Zhou, M., X. Qu, and X. Li. 2017. “A recurrent neural network based microscopic car following model to predict traffic oscillation.” Transp. Res. Part C Emerging Technol. 84 (Nov): 245–264. https://doi.org/10.1016/j.trc.2017.08.027.
Zhou, Y., J. C. Medina, J. Taylor, and X. Cathy Liu. 2022. “Empirical verification of car-following parameters using naturalistic driving data on freeway segments.” J. Transp. Eng. Part A Syst. 148 (2): 04021108. https://doi.org/10.1061/JTEPBS.0000629.
Zhu, M., X. Wang, and J. Hu. 2020. “Impact on car following behavior of a forward collision warning system with headway monitoring.” Transp. Res. Part C Emerging Technol. 111 (Feb): 226–244. https://doi.org/10.1016/j.trc.2019.12.015.
Zhu, M., X. Wang, A. Tarko, and S. Fang. 2018a. “Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study.” Transp. Res. Part C Emerging Technol. 93 (Aug): 425–445. https://doi.org/10.1016/j.trc.2018.06.009.
Zhu, M., X. Wang, and Y. Wang. 2018b. “Human-like autonomous car-following model with deep reinforcement learning.” Transp. Res. Part C Emerging Technol. 97 (Dec): 348–368. https://doi.org/10.1016/j.trc.2018.10.024.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: May 4, 2022
Accepted: Nov 18, 2022
Published online: Jan 23, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 23, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Jun Bai, Jaeyoung Lee, Suyi Mao, Effects of Adaptive Cruise Control System on Traffic Flow and Safety Considering Various Combinations of Front Truck and Rear Passenger Car Situations, Transportation Research Record: Journal of the Transportation Research Board, 10.1177/03611981231223982, (2024).