Managing Lane Changing of Algorithm-Assisted Drivers

55 Pages Posted: 4 Mar 2020

See all articles by Mihalis Markakis

Mihalis Markakis

IESE Business School

Kalyan Talluri

Imperial College Business School

Dmitrii Tikhonenko

Universitat Pompeu Fabra

Date Written: February 7, 2020

Abstract

Traffic theory models ascribe the fundamental cause of velocity oscillations and stop-and-go waves to the "behavioral" nature of human driving. But what if vehicles were controlled or assisted by algorithms? Would these phenomena go away? How should a regulator manage algorithm-assisted traffic for a smooth flow? We study these questions in the context of a common forced-merging scenario of a two-lane road segment that has one lane blocked unexpectedly, say, due to accident or construction. Motivated by the recent advent of algorithm-assisted driving, we assume that merging drivers are rational, self-interested agents, wishing to minimize their individual travel times, deciding (a) at what velocity to move, and (b) whether to merge to the free lane, given the opportunity (gap). Moving at higher velocities reduces travel time, but also reduces the probability of finding a large enough gap to merge. We analyze a dynamic programming formulation of the problem with a single merging driver, and show that the optimal policy has a surprising structure: in the presence of uncertainty on finding adequate gaps in the target lane, it may be optimal for the blocked-lane driver, in certain parameter regimes, to oscillate between high and low velocities while attempting to merge. Hence, the origin of traffic oscillations need not be purely "behavioral" but can also arise endogenously, as the outcome of a rational agent's optimizing behavior. We provide sufficient conditions on the velocity limits so that drivers do not find it optimal to oscillate between high and low velocities, and derive merging recommendations based on our dynamic programming formulation. We conduct extensive Cellular-Automata microsimulations with multiple merging cars to validate the superiority, in terms of both throughput and delay, of merging policies derived from our dynamic programming approach over various benchmark policies.

Keywords: vehicular transportation, traffic oscillations, optimal merging, dynamic programming

Suggested Citation

Markakis, Mihalis and Talluri, Kalyan and Tikhonenko, Dmitrii, Managing Lane Changing of Algorithm-Assisted Drivers (February 7, 2020). Available at SSRN: https://ssrn.com/abstract=3534301 or http://dx.doi.org/10.2139/ssrn.3534301

Mihalis Markakis (Contact Author)

IESE Business School ( email )

Avenida Pearson 21
Barcelona, 08034
Spain

HOME PAGE: http://https://www.iese.edu/faculty-research/faculty/mihalis-markakis/

Kalyan Talluri

Imperial College Business School ( email )

387A Business School
South Kensington Campus
London, London SW7 2AZ
United Kingdom
+44 (0)20 7594 1233 (Phone)

HOME PAGE: http://https://www.imperial.ac.uk/people/kalyan.talluri

Dmitrii Tikhonenko

Universitat Pompeu Fabra ( email )

Ramon Trias Fargas, 25-27
Barcelona, E-08005
Spain

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