Calibrating Car-Following Models on Urban Streets Using Naturalistic Driving Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 4
Abstract
Car-following models serve an important role in gaining a thorough understanding of traffic flow and driving behavior characteristics. By analyzing these characteristics, the models are critical to microscopic traffc simulation, and consequently, to traffic safety. However, lack of reliable traffic data in China has, until recently, limited the use of car-following models. As the Shanghai Naturalistic Driving Study (SH-NDS) has now made such data accessible, car-following models have been built for freeways and urban expressways, but none have yet been developed for urban streets. To compare car following for the three road types and to determine the best model for urban streets, five commonly used car-following models were calibrated and validated with 5,500 urban street-level car-following events extracted from the 161,055 km of data collected in the SH-NDS. The models were evaluated based on their parameter estimates and root mean square percentage errors (RMSPE). Results show that (1) the intelligent driver model (IDM), with a calibration error of 24% and a validation error of 28%, performed best in modeling drivers’ car-following behavior on urban Shanghai streets; and (2) in comparison to previous car-following research on Chinese freeways and urban expressways, drivers on urban streets tend to assume a relatively lower car-following speed, and maintain slightly larger time headway and maximum acceleration. Because the IDM demonstrated great performance on expressways, freeways, and urban streets in China, it is reasonable to assume the model may show similar performance when used to analyze car following in other countries.
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Data Availability Statement
Some of the data, models, and code generated and used during the study are proprietary and confidential in nature and may only be provided with restrictions. Informed consent forms (ICFs) were signed with experiment participants, clearly stating that the data of the participants collected in the experiment was only used within the research team to protect the participants’ privacy.
Acknowledgments
This study was jointly sponsored by the Chinese National Science Foundation (51878498) and the Science and Technology Commission of Shanghai Municipality (18DZ1200200). The authors confirm contributions to the paper as follows: study conception and design, data collection, analysis and interpretation of results, draft manuscript preparation by Linjia He and Xuesong Wang. Both authors reviewed the results and approved the final version of the manuscript.
References
Bando, M., K. Hasebe, A. Nakayama, A. Shibata, and Y. Sugiyama. 1995. “Dynamical model of traffic congestion and numerical simulation.” Phys. Rev. E: Stat. Phys. Plasmas Fluids Relat. Interdip. Top. 51 (2): 1035–1042. https://doi.org/10.1103/physreve.51.1035.
Brackstone, M., and M. McDonald. 1999. “Car-following: A historical review.” Transp. Res. Part F: Traffic Psychol. Behav. 2 (4): 181–196. https://doi.org/10.1016/S1369-8478(00)00005-X.
Brockfeld, E., R. D. Kuhne, and P. Wagner. 2005. “Calibration and validation of microscopic models of traffic flow.” Transp. Res. Rec.: J. Transp. Res. Board 1934 (1): 179–187. https://doi.org/10.1177/0361198105193400119.
Chao, Q., Z. Deng, and X. Jin. 2015. “Vehicle-pedestrian interaction for mixed traffic simulation.” Comput. Anim. Virtual Worlds 26 (3–4): 405–412. https://doi.org/10.1002/cav.1654.
Cheng, B., T. Taniguchi, T. Hatano, and K. Matsushima. 2005. “Characteristics of driver behavior in car-following.” Rev. Automot. Eng. 26 (2): 191–199.
Fitch, G. M., and R. J. Hanowski. 2012. “Using naturalistic driving research to design, test and evaluate driver assistance systems.” Handbook Intell. Vehicles 2012 (1): 559–580.
Gazis, D., R. Herman, and R. W. Rothery. 1961. “Nonlinear follow-the-leader models of traffic flow.” Oper. Res. 9 (4): 545–567. https://doi.org/10.1287/opre.9.4.545.
Gipps, P. G. 1981. “A behavioural car-following model for computer simulation.” Transp. Res. Part B 15 (2): 105–111. https://doi.org/10.1016/0191-2615(81)90037-0.
Higgs, B., and M. Abbas. 2013. “A two-step segmentation algorithm for behavioral clustering of naturalistic driving styles.” In Proc., 16th Int. IEEE Conf. on Intelligent Transportation Systems, 857–862. New York: IEEE. https://doi.org/10.1109/ITSC.2013.6728339.
Jiang, R., Q. Wu, and Z. Zhu. 2001. “Full velocity difference model for a car-following theory.” Phys. Rev. E 64 (1): 17101. https://doi.org/10.1103/PhysRevE.64.017101.
Kesting, A., and M. Treiber. 2008. “Calibrating car-following models by using trajectory data: A methodological study.” Transp. Res. Rec.: J. Transp. Res. Board 2088 (1): 148–156. https://doi.org/10.3141/2088-16.
Krajewski, R., J. Bock, L. Kloeker, and L. Eckstein. 2018. “The HighD dataset: A drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems.” In Proc., 21st Int. Conf. on Intelligent Transport Systems (ITSC). New York: IEEE. https://doi.org/10.1109/ITSC.2018.8569552.
Lu, Z., T. Fu, L. Fu, S. Shiravi, and C. Jiang. 2016. “A video-based approach to calibrating car-following parameters in VISSIM for urban traffic.” Int. J. Transp. Sci. Technol. 5 (1): 1–9. https://doi.org/10.1016/j.ijtst.2016.06.001.
May, A. D. 1990. Traffic flow fundamentals. Hoboken, NJ: Prentice Hall.
Ministry of Public Security. 2015. “Statistics of motor vehicles and drivers in China, 2014.” Accessed December 11, 2016. https://www.mps.gov.cn/n2255040/n4908728/c4929117/content.html.
Panwai, S., and H. Dia. 2005. “Comparative evaluation of microscopic car-following behavior.” IEEE Trans. Intell. Transp. Syst. 6 (3): 314–325. https://doi.org/10.1109/TITS.2005.853705.
Papadimitriou, S., and C. F. Choudhury. 2017. “Transferability of car-following models between driving simulator and field traffic.” In Proc., 96th Transportation Research Board Annual Meeting, 60–72. Washington, DC: Transportation Research Board.
Piao, J., and M. McDonald. 2003. “Low speed car following behaviour from floating vehicle data.” In Proc., IEEE IV2003 Intelligent Vehicles Symp., 462–467. New York: IEEE.
Pipes, L. A. 1953. “An operational analysis of traffic dynamics.” J. Appl. Phys. 24 (3): 274–281. https://doi.org/10.1063/1.1721265.
Punzo, V., M. T. Borzacchiello, and B. Ciuffo. 2011. “On the assessment of vehicle trajectory data accuracy and application to the next generation simulation (NGSIM) program data.” Transp. Res. Part C: Emerging Technol. 19 (Mar): 1243–1262. https://doi.org/10.1016/j.trc.2010.12.007.
Rahman, M. 2013. Application of parameter estimation and calibration method for car-following models. Clemson, SC: Clemson Univ.
Rakha, H., and B. Crowther. 2002. “Comparison of greenshields, pipes, and Van Aerde car-following and traffic stream models.” Transp. Res. Rec.: J. Transp. Res. Board 1802 (1): 28. https://doi.org/10.3141/1802-28.
Saifuzzaman, M., and Z. D. Zheng. 2014. “Incorporating human-factors in car-following models: A review of recent developments and research needs.” Transp. Res. Part C: Emerging Technol. 48 (Sep): 379–403. https://doi.org/10.1016/j.trc.2014.09.008.
Sangster, J., H. Rakha, and J. Du. 2013. “Application of naturalistic driving data to modeling of driver car-following behavior.” Transp. Res. Rec.: J. Transp. Res. Board 2390 (1): 20–33. https://doi.org/10.3141/2390-03.
Shi, X., D. Zhao, H. Yao, X. Li, D. K. Hale, and A. Ghiasi. 2021. “Video-based trajectory extraction with deep learning for high-granularity highway simulation (HIGH-SIM).” Commun. Transp. Res. 1 (Dec): 100014. https://doi.org/10.1016/j.commtr.2021.100014.
Sun, P., X. Wang, and M. Zhu. 2021. “Modeling car-following behavior on freeways considering driving style.” J. Transp. Eng. Part A: Syst. 12 (5): 147. https://doi.org/10.1061/JTEPBS.0000584.
Tang, T., H. Huang, S. Huang, and R. Jiang. 2009. “A new car-following model with consideration of the traffic interruption probability.” Chin. Phys. B 18 (3): 975–983. https://doi.org/10.1088/1674-1056/18/3/022.
Tang, T., Y. Wang, X. Yang, and Y. Wu. 2012. “A new car-following model accounting for varying road condition.” Nonlinear Dyn. 70 (2): 1397–1405. https://doi.org/10.1007/s11071-012-0542-8.
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.
Treiber, M., and A. Kesting. 2013. “Microscopic calibration and validation of car-following models—A systematic approach.” Proc. Soc. Behav. Sci. 80 (5): 922–939. https://doi.org/10.1016/j.sbspro.2013.05.050.
Wiedemann, R. 1974. Simulation Des Strassenverkehrsflusses. Washington, DC: Transportation Research Board.
Zhou, J., and H. Peng. 2005. “Range policy of adaptive cruise control vehicles for improved flow stability and string stability.” IEEE Trans. Intell. Transp. Syst. 6 (2): 229–237. https://doi.org/10.1109/TITS.2005.848359.
Zhu, M., X. Wang, A. Tarko, and S. Fang. 2018. “Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study.” Transp. Res. Part C: Emerging Technol. 93 (10): 425–445. https://doi.org/10.1016/j.trc.2018.06.009.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 4 • April 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 10, 2021
Accepted: Jun 9, 2022
Published online: Jan 24, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 24, 2023
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