Market share maximizing strategies of CAV fleet operators may cause chaos in our cities

TL;DR

This paper develops a mathematical framework to analyze how CAV fleet operators can maximize market share through routing strategies, revealing potential for chaotic traffic patterns in future cities due to mixed strategies and unpredictable congestion.

Contribution

It introduces a rigorous model of individualized collective routing, analyzes algorithms for fleet market-share optimization, and links fleet behavior to human driver responses in heterogeneous populations.

Findings

Fleet operators can often achieve full market share by mimicking human driver choices.

In heterogeneous populations, mixed strategies are necessary for fleet market share maximization.

Unpredictable congestion patterns may emerge, causing chaos in urban traffic systems.

Abstract

We study the dynamics and equilibria of a new kind of routing games, where players - drivers of future autonomous vehicles - may switch between individual (HDV) and collective (CAV) routing. In individual routing, just like today, drivers select routes minimizing expected travel costs, whereas in collective routing an operator centrally assigns vehicles to routes. The utility is then the average experienced travel time discounted with individually perceived attractiveness of automated driving. The market share maximising strategy amounts to offering utility greater than for individual routing to as many drivers as possible. Our theoretical contribution consists in developing a rigorous mathematical framework of individualized collective routing and studying algorithms which fleets of CAVs may use for their market-share optimization. We also define bi-level CAV - HDV equilibria and derive conditions which link the potential marketing behaviour of CAVs to the behavioural profile of the human population. Practically, we find that the fleet operator may often be able to equilibrate at full market share by simply mimicking the choices HDVs would make. In more realistic heterogenous human population settings, however, we discover that the market-share maximizing fleet controller should use highly variable mixed strategies as a means to attract or retain customers. The reason is that in mixed routing the powerful group player can control which vehicles are routed via congested and uncongested alternatives. The congestion pattern generated by CAVs is, however, not known to HDVs before departure and so HDVs cannot select faster routes and face huge uncertainty whichever alternative they choose. Consequently, mixed market-share maximising fleet strategies resulting in unpredictable day-to-day driving conditions may, alarmingly, become pervasive in our future cities.