Modeling and Analysis of Dynamic Supply–Demand Interactions in Ride-Hailing Platforms Using a System Dynamics Approach

Abstract

In recent years, online transportation platforms have played an important role in urban mobility as a prominent example of two-sided markets. However, maintaining a balance between supply and demand remains a major challenge due to the dynamic interactions and feedback mechanisms within these platforms. This study aims to analyze the dynamic interaction between supply and demand and investigate how equilibrium is formed between users and drivers in online ride-hailing platforms. To this end, a System Dynamics (SD) model was developed for the Snapp platform as a case study. The model structure was designed using Causal Loop Diagrams (CLDs) and Stock-and-Flow Diagrams (SFDs), and its parameters were calibrated using real-world data and a behavioral calibration process. The simulation results indicated that the model can reproduce the observed behavior of the system with an acceptable level of accuracy and project the growth trends of users, drivers, and completed trips over time. Furthermore, the analysis of different commission-rate scenarios showed that this variable plays a critical role in maintaining system equilibrium. Excessively high commission rates reduce the number of drivers and users in the long term, which subsequently leads to a decline in the number of trips and the platform’s profitability. In contrast, more balanced commission rates help maintain equilibrium between supply and demand and support sustainable growth. Overall, the findings demonstrate that the SD approach can improve the understanding of two-sided platform behavior and support managerial decision-making in the design of effective and sustainable policies.