Modelling the Rise and Fall of Two-Sided Mobility Markets with Microsimulation

TL;DR

This paper introduces a novel agent-based simulation framework for modeling the dynamics of two-sided mobility markets, capturing market entry, growth, maturity, and decline through rational agent decision-making and network effects.

Contribution

It develops a co-evolutionary microsimulation model with S-shaped utility functions to realistically reproduce the rise and fall of two-sided mobility platforms.

Findings

Simulated a 400-day evolution of a mobility platform in Amsterdam.

Demonstrated how marketing and word of mouth influence market share.

Showed the impact of strategic stages on platform success and collapse.

Abstract

In this paper, we propose a novel modelling framework to reproduce the market entry strategies for two-sided mobility platforms. In the MaaSSim agent-based simulator, we develop a co-evolutionary model to represent day-to-day dynamics of the two-sided mobility market with agents making rational decisions to maximize their perceived utility. Participation probability of agents depends on utility, composed of: experience, word of mouth and marketing components adjusted by agents every day with the novel S-shaped formulas - better suited (in our opinion) to reproduce market entry dynamics than previous approaches. With such a rich representation, we can realistically model a variety of market entry strategies and create significant network effects to reproduce the rise and fall of two-side mobility platforms. To illustrate model capabilities, we simulate a 400-day evolution of 200 drivers and 2000 travelers on a road-network of Amsterdam. We design a six-stage market entry strategy with consecutive: kick-off, discount, launch, growth, maturity and greed stages. After 25 days the platform offers discounts, yet it starts gaining market share only when the marketing campaign launches at day 50. Campaign finishes after 50 days, which does not stop the growth, now fueled mainly with a positive word of mouth effect and experiences. The platform ends discounts after 200 days and reaches the steady maturity period, after which its greedy strategy leads to collapse of its market share and profit. All above simulated with a single behavioral model, which well reproduces how agents of both sides adapts to platform actions.