Exploring multi-homing behavior of ride-sourcing drivers via real-world multiple platforms data

Abstract

Multi-homing behavior refers to the behavior that ride-sourcing drivers simultaneously register and sequentially provide services on multiple ride-sourcing platforms. The multi-homing behavior of ride-sourcing drivers significantly impacts the competition among multiple ride-sourcing platforms in a competitive market. To better understand the multi-homing behavior, we present exploratory evidence on the factors that influence drivers' platform switching behavior. The RF-MNL (random forest multinomial logistic regression) framework is applied to analyze multi-homing driver behavior in a competitive ride-sourcing market. Multinomial logistic regression (MLR) is adopted to model the platform switching behavior of multi-homing drivers. The random forest is employed to seek the best combination of variables for the MLR model, which is calibrated by using the one-month multi-platform ride-sourcing data in Hangzhou, China. A variety of explanatory variables that influence ride-sourcing drivers' multi-homing behavior are estimated. The results show that the driver's socio-demographic characteristics, income level, bonus income (e.g., long-distance price rise), and work time related factors (e.g., the time gap of order dispatching, and wait time) play an essential role in determining the platform switching decision. This study corroborates the evidence of significant factors that impact drivers' switching from one ride-sourcing platform to another, which can support decision-making for ride-sourcing platforms to attract drivers serving the platform exclusively. We also examine how heterogeneity in drivers' multi-homing tendencies affects the platform's policy. To our best knowledge, this paper is one of the first quantitative studies that empirically reveal the commonly observed multi-homing behavior of ride-sourcing drivers by exploring real-world city-wide data collected on multiple platforms.