Headway Distribution Considering Vehicle Type Combinations
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 3
Abstract
To achieve more-accurate traffic simulation and autonomous vehicle control, increasing attention recently has been devoted to the analysis of car-following behavior considering leader–follower vehicle types. Firstly, on the basis of a vehicle trajectory data set, distance headway (DHW) and time headway (THW) distributions of leader–follower vehicle type combinations, including car following car (CC), car following truck (CT), truck following car (TC), and truck following truck (TT), were compared to verify their significant differences. Then best-fit distribution models of four combinations were identified in each scenario. Next, characteristics of DHW and THW of four combinations in different speed ranges were determined according to their best-fit distribution models. Finally, the transferability of the results was verified. Results show that DHW and THW of CC and CT can be modeled as log-logistic distributions, and DHW and THW best-fit models of TC and TT are gamma distributions. CF behavior depends more on the following vehicle type when the speed is less than , and the impact of the leading vehicle gradually occurs when the speed is higher than .
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This study was sponsored by the Shanghai Science and Technology Committee (STCSM) (Grant No. 18DZ1200200).
References
Aghabayk, K., M. Sarvi, N. Forouzideh, and W. Young. 2015. “Modelling heavy vehicle car-following behaviour in congested traffic conditions.” J. Adv. Transp. 48 (8): 1017–1029. https://doi.org/10.1002/atr.1242.
Brackstone, M., B. Waterson, and M. McDonald. 2009. “Determinants of following headway in congested traffic.” Transp. Res. Part F: Traffic Psychol. Behav. 12 (2): 131–142. https://doi.org/10.1016/j.trf.2008.09.003.
Chen, X. M., L. Li, and Q. Shi. 2015. “A Markov model based on headway/spacing distributions.” In Stochastic evolutions of dynamic traffic flow, 49–79. New York: Springer.
Choi, K., J. S. Jung, S. Shim, and S. Lee. 2005. “Optimal VMS spacing strategy with considering traffic information update interval.” In Proc., World Congress on Intelligent Transport Systems. Brussels, Belgium: ERTICO.
Hammit, B. E., A. Ghasemzadeh, R. M. James, M. M. Ahmed, and R. K. Young. 2018. “Evaluation of weather-related freeway car-following behavior using the SHRP2 naturalistic driving study database.” Transp. Res. Part F: Traffic Psychol. Behav. 59 (Nov): 244–259. https://doi.org/10.1016/j.trf.2018.08.023.
HCM (Highway Capacity Manual). 2010. Highway capacity manual 2010. Washington, DC: Transportation Research Board.
Jiang, J., J. Lu, and Z. Lv. 2011. “Car-following behavior of different vehicles on ordinary highways.” In Proc., 11th Int. Conf. of Chinese Transportation Professionals (ICCTP). Reston, VA: ASCE.
Kong, D., and X. Guo. 2016. “Analysis of vehicle headway distribution on multi-lane freeway considering car–truck interaction.” Adv. Mech. Eng. 8 (4): 168781401664667. https://doi.org/10.1177/1687814016646673.
Kong, D., L. Sun, J. Li, and Y. Xu. 2021. “Modeling cars and trucks in the heterogeneous traffic based on car–truck combination effect using cellular automata.” Physica A 562 (Jan): 125329. https://doi.org/10.1016/j.physa.2020.125329.
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 Transportation Systems. New York: IEEE.
Kühne, R. 2008. “Foundation of traffic flow theory I: Greenshields legacy highway traffic.” In Proc., TRB Traffic Flow Theory and Characteristics Committee Summer Meeting and Symp. on the Fundamental Diagram: 75 years (“Greenshields75" Symp.). Washington, DC: Federal Highway Administration.
Leblanc, D. J., S. Bao, J. R. Sayer, and S. Bogard. 2013. “Longitudinal driving behavior with integrated crash-warning system: Evaluation from naturalistic driving data.” Transp. Res. Rec. 2365 (1): 17–21. https://doi.org/10.3141/2365-03.
Liu, T., and R. Fu. 2018. “The relationship between different safety indicators in car-following situations.” In Proc., IEEE Intelligent Vehicles Symp. (IV). New York: IEEE.
Liu, T., and Selpi. 2019. “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.
Maurya, A. K., S. Dey, and S. Das. 2015. “Speed and time headway distribution under mixed traffic condition.” J. East. Asia Soc. Transp. Stud. 11: 1774–1792. https://doi.org/10.11175/easts.11.1774.
Messaoud, K., I. Yahiaoui, A. Verroust-Blondet, and F. Nashashibi. 2019. “Non-local social pooling for vehicle trajectory prediction.” In Proc., IEEE Intelligent Vehicles Symp. (IV). New York: IEEE.
Osorio, C., and V. Punzo. 2019. “Efficient calibration of microscopic car-following models for large-scale stochastic network simulators.” Transp. Res. Part B: Methodol. 119 (Jan): 156–173. https://doi.org/10.1016/j.trb.2018.09.005.
Parker, M. T. 1996. “The effect of heavy goods vehicles and following behavior on capacity at motorway road work sites.” Traffic Eng. Control 39 (9): 524–531.
Sipahi, R., F. M. Atay, and S.-I. Niculescu. 2015. “Stability analysis of a constant time-headway driving strategy with driver memory effects modeled with distributed delays.” IFAC PapersOnLine 48 (12): 376–381. https://doi.org/10.1016/j.ifacol.2015.09.407.
Swaroop, D., J. K. Hedrick, C. C. Chen, and P. Ioannou. 1994. “A comparision of spacing and headway control laws for automatically controlled vehicles.” Veh. Syst. Dyn. 23 (1): 597–625. https://doi.org/10.1080/00423119408969077.
Vivek, S., B. Varsha, S. T. Vignesh, K. Anirudh, and K. P. Peeyush. 2020. “Design of control systems for ABC and speed control for autonomous vehicle.” J. Phys. Conf. Ser. 1706 (1): 012090. https://doi.org/10.1088/1742-6596/1706/1/012090.
Vogel, K. 2002. “What characterizes a ‘free vehicle’ in an urban area?” Transp. Res. Part F: Traffic Psychol. Behav. 5 (1): 15–29. https://doi.org/10.1016/S1369-8478(02)00003-7.
Weng, J., Q. Meng, and T. F. Fwa. 2014. “Vehicle headway distribution in work zones.” Transportmetrica A: Transp. Sci. 10 (4): 285–303. https://doi.org/10.1080/23249935.2012.762564.
Yang, L., J. Mao, K. Liu, J. Du, and J. Liu. 2020. “An adaptive cruise control method based on improved variable time headway strategy and particle swarm optimization algorithm.” IEEE Access 8 (Sep): 168333–168343. https://doi.org/10.1109/ACCESS.2020.3023179.
Ye, F., and Y. Zhang. 2009. “Vehicle type–specific headway analysis using freeway traffic data.” Transp. Res. Rec. 2124 (1): 222–230. https://doi.org/10.3141/2124-22.
Zheng, L., C. Zhu, Z. He, T. He, and S. Liu. 2018. “Empirical validation of vehicle type-dependent car-following heterogeneity from micro- and macro-viewpoints.” Transportmetrica B: Transp. Dyn. 7 (Dec): 765–787. https://doi.org/10.1080/21680566.2018.1517057.
Zhu, M., X. Wang, and X. Wang. 2016. “Car-following headways in different driving situations: A naturalistic driving study.” In Proc., Cota Int. Conf. of Transportation Professionals. Reston, VA: ASCE.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 3 • March 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 7, 2021
Accepted: Aug 19, 2021
Published online: Dec 23, 2021
Published in print: Mar 1, 2022
Discussion open until: May 23, 2022
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