Drivers learn city-scale dynamic equilibrium

TL;DR

This paper develops a game-theoretic and thermodynamic framework to model and empirically validate driver search strategies and learning dynamics in city-scale on-demand mobility, revealing how drivers adapt and reach equilibrium over time.

Contribution

It introduces a novel combined game-theoretic and thermodynamic model for driver behavior, validated with extensive empirical data from New York City.

Findings

Drivers learn the equilibrium within a year.

Longer-staying drivers learn better strategies.

The collective response aligns with model predictions.

Abstract

Understanding driver behavior in on-demand mobility services is crucial for designing efficient and sustainable transport models. Drivers' delivery strategy is well understood, but their search strategy and learning process still lack an empirically validated model. Here we provide a game-theoretic model of driver search strategy and learning dynamics, interpret the collective outcome in a thermodynamic framework, and verify its various implications empirically. We capture driver search strategies in a multi-market oligopoly model, which has a unique Nash equilibrium and is globally asymptotically stable. The equilibrium can therefore be obtained via heuristic learning rules where drivers pursue the incentive gradient or simply imitate others. To help understand city-scale phenomena, we offer a macroscopic view with the laws of thermodynamics. With 870 million trips of over 50k drivers in New York City, we show that the equilibrium well explains the spatiotemporal patterns of driver search behavior, and estimate an empirical constitutive relation. We find that new drivers learn the equilibrium within a year, and those who stay longer learn better. The collective response to new competition is also as predicted. Among empirical studies of driver strategy in on-demand services, our work examines the longest period, the most trips, and is the largest for taxi industry.