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General Lane-Changing Model MOBIL for Car-Following Models
Abstract
A general model (minimizing overall braking induced by lane change, MOBIL) is proposed to derive lane-changing rules for discretionary and mandatory lane changes for a wide class of car-following models. Both the utility of a given lane and the risk associated with lane changes are determined in terms of longitudinal accelerations calculated with microscopic traffic models. This determination allows for the formulation of compact and general safety and incentive criteria for both symmetric and asymmetric passing rules. Moreover, anticipative elements and the crucial influence of velocity differences of these car-following models are automatically transferred to the lane-changing rules. Although the safety criterion prevents critical lane changes and collisions, the incentive criterion takes into account the advantages and disadvantages of other drivers associated with a lane change via the “politeness factor.” The parameter allows one to vary the motivation for lane changing from purely egoistic to more cooperative driving behavior. This novel feature allows one first to prevent lane changes for a marginal advantage if they obstruct other drivers and second to let an aggressive driver induce the lane change of a slower driver ahead in order to no longer be obstructed. This phenomenon is common for asymmetric passing rules with a dedicated lane for passing. The model is applied to traffic simulations of cars and trucks with the intelligent driver model as the underlying car-following model. An open system with an on-ramp is studied, and the resulting lane-changing rate is investigated as a function of the spatial coordinate as well as a function of traffic density.
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Cited by
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Liu, S., Liu, L., Tang, J., Yu, B., Wang, Y., & Shi, W. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2915983 · Cited by 778
Adaptive Cruise Control Design for Active Congestion Avoidance
Kesting, A., Treiber, M., Schönhof, M., & Helbing, D. (2008) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2007.12.004 · Cited by 610
Self-citationDense reinforcement learning for safety validation of autonomous vehicles
Feng, S., Sun, H., Yan, X., Zhu, H., Zou, Z., Shen, S., & Liu, H. X. (2023) · Nature · doi:10.1038/s41586-023-05732-2 · Cited by 587
Recent developments and research needs in modeling lane changing
Zheng, Z. (2014) · Transportation Research Part B: Methodological · doi:10.1016/j.trb.2013.11.009 · Cited by 532
Imitating driver behavior with generative adversarial networks
Kuefler, A., Morton, J., Wheeler, T., & Kochenderfer, M. (2017) · 2017 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2017.7995721 · Cited by 423
Metadata
- Authors
- Kesting, A., Treiber, M., & Helbing, D.
- Venue
- Transportation Research Record: Journal of the Transportation Research Board
- Publisher
- SAGE Publications
- Published
- 2007-01
- Volume
- 1999
- Issue
- 1
- Pages
- 86-94
- ISSN
- 0361-1981 (ISSN-L)
- 2169-4052
- OpenAlex
- W2056877664
- Cited by
- 1,345
- References
- 0
- FWCI
- 30.63
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kesting, A., Treiber, M., & Helbing, D. (2007). General Lane-Changing Model MOBIL for Car-Following Models. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.3141/1999-10
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