Abstract
A current effort of many car-developing companies is devoted to the development of automatic driving vehicles. It is assumed that future vehicular traffic in traffic and transportation networks is a mixed traffic flow consisting of human driving and automatic driving vehicles. Automatic driving vehicles should considerably enhance capacity of a traffic network. Capacity of the network is limited by traffic breakdown at network bottlenecks. In this Chapter, we discuss the effect of automatic driving vehicles on the probability of traffic breakdown at network bottlenecks.
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Notes
- 1.
Automatic driving is also called self-driving, autonomous driving, or automated driving. Respectively, automatic driving vehicles are also called self-driving vehicles, autonomous driving vehicles, or automated driving vehicles.
- 2.
The work by Davis [1] was one of the first to obtain this result with a human driver model that had some of the features of the three-phase theory.
- 3.
In real ACC, the coefficients of ACC adaptation can be functions of the speed and the space gap. Moreover, the coefficients of ACC adaptation can depend on a driving situation. However, the objective of this chapter is a study of qualitative features of the effect of ACC-vehicles in mixed traffic flow on the probability of traffic breakdown at network bottlenecks. To reach this goal, we can assume that the coefficients of ACC adaptation do not depend on the speed and the space gap in mixed traffic flow.
- 4.
In the discrete model version (Appendix A) used for all simulations in this chapter, the discretization cell δ x = 0.01 m is used. Respectively, vehicle acceleration is measured in units δ a = δ x∕τ 2.
- 5.
Simulations show that the use of the safe speed in formula (6.18) does not influence on the dynamics of the ACC vehicles (6.1) in free flow outside the bottleneck. However, due to vehicle merging from the on-ramp onto the main road, time headway of the vehicle to the preceding vehicle can be considerably smaller than τ d (ACC). Therefore, formula (6.18) allows us to avoid collisions of the ACC vehicle with the preceding vehicle in such dangerous situations. Moreover, very small values of time headway can occur in congested conditions; formula (6.18) prevents vehicle collisions in these cases also. While working at the Daimler Company, the author was lucky to take part in the development of real ACC vehicles, which are on the market; to avoid collisions in dangerous simulations, dynamics rules of all real ACC vehicles include some safety dynamic rules that can be similar to (6.17), (6.18).
- 6.
In this book, we do not discuss another possible case of the deterioration of the performance of traffic system that is often assumed to occur when automatic driving vehicles follow strictly all traffic regulation rules, like a given speed limit.
- 7.
In particular, one can expect that cooperative merging could alleviate the problem of large disturbances occurring during vehicle merging at the bottleneck in mixed traffic flow illustrated in Fig. 6.8.
References
L.C. Davis, Phys. Rev. E. 69, 066110 (2004)
L.C. Davis, Phys. Lett. A 376, 2658–2662 (2012)
L.C. Davis, Physica A 392, 3755–3764 (2013)
L.C. Davis, Physica A 392, 3798–3805 (2013)
L.C. Davis, Physica A 395, 580 (2014)
B.S. Kerner, The Physics of Traffic (Springer, Berlin, New York, 2004)
B.S. Kerner, Introduction to Modern Traffic Flow Theory and Control (Springer, Heidelberg, Dordrecht, London, New York, 2009)
B.S. Kerner, Elektrotechnik und Informationstechnik 132, 417–433 (2015)
B.S. Kerner, Phys. Rev. E 92, 062827 (2015)
B.S. Kerner, Physica A 450, 700–747 (2016)
B.S. Kerner, S.L. Klenov, J. Phys. A Math. Gen. 35, L31–L43 (2002)
B.S. Kerner, S.L. Klenov, Phys. Rev. E 68, 036130 (2003)
B.S. Kerner, S.L. Klenov, J. Phys. A Math. Gen. 37, 8753–8788 (2004)
B.S. Kerner, S.L. Klenov, Phys. Rev. E 80, 056101 (2009)
B.S. Kerner, S.L. Klenov, J. Phys. A Math. Theor. 43, 425101 (2010)
C.-Y. Liang, H. Peng, Veh. Syst. Dyn. 32, 313–330 (1999)
C.-Y. Liang, H. Peng, JSME Int. J. Ser. C 43, 671–677 (2000)
W.Levine, M. Athans, IEEE Trans. Automat. Contr. 11, 355–361 (1966)
R. Müller, G. Nöcker, in Proceedings of the Intelligent Vehicles ’92 Symposium, ed. by I. Masaki (IEEE, Detroit, USA, 1992), pp. 173–178
R. Rajamani, Vehicle Dynamics and Control, Mechanical Engineering Series (Springer US, Boston, MA, 2012)
B. Schoettle, M. Sivak, “A preliminary analysis of real-world crashes involving self-driving vehicles”, Report No. UMTRI-2015-34,(The University of Michigan, Transp. Res. Institute, Ann Arbor, Michigan, USA, 2015)
D. Swaroop, J.K. Hedrick, IEEE Trans. Automat. Contr. 41, 349–357 (1996)
D. Swaroop, J.K. Hedrick, S.B. Choi, IEEE Trans. Veh. Technol. 50, 150–161 (2001)
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Kerner, B.S. (2017). Effect of Automatic Driving on Probability of Breakdown in Traffic Networks. In: Breakdown in Traffic Networks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54473-0_6
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