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• Explore attributes that impact the platform choice behavior of multi-homing drivers. • Combine random forest with multinomial logistic regression to analyze multi-homing behaviour. • Employ multinomial logistic regression to model platform switching behaviour. • Provide managerial suggestions for government and ride-sourcing platforms. • Empirically explore real-world city-wide data collected on multiple ride-sourcing platforms. 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. [ABSTRACT FROM AUTHOR]

• Model multi-homing ride-sourcing drivers' decision sequence on platform switching. • Develop dynamic decision model based on high-order hidden Markov model (HO-HMM). • Present the merits of HO-HMM in exploring individual characteristics and preferences. • Reveal interdependencies of hidden platform preferences of multi-homing ride-sourcing drivers. • Verify superiority of HO-HMMs by city-wide real-wrold multiple platforms data. Ride-sourcing drivers enjoy flexibility in scheduling work hours and choosing platforms, which generates multi-homing behavior on multiple platforms. It is challenging to observe the labor supply of multi-homing ride-sourcing drivers due to data limitations, which motivates our research on modeling drivers' dynamic decisions on labor supply in the competitive ride-sourcing market with multiple platforms. We propose a dynamic discrete choice framework by modeling drivers' high-frequency decision sequences on platform switching as high-order hidden Markov processes and considering time-varying factors. The high-order hidden Markov framework relaxes the first-order Markovian assumption and builds interdependencies of unobserved states across multiple time periods. The estimation of the proposed model takes advantage of city-scale multiple platforms datasets in Hangzhou, China, including more than 16 million records of the order payment information and more than 46 thousand records of active ride-sourcing drivers' information. The case study results indicate that the high-order hidden Markov model (HO-HMM) has superior explanatory power in fitting multiple platforms datasets than the multinomial logit model and the first-order hidden Markov model. HO-HMM performs an advantage in modeling the extended historical dependency of drivers' decisions and individual driver behavior modeling with high interpretability. The results uncover the variations of drivers' attitudes in different hidden (unobservable) states and towards different platforms. In general, ride-sourcing drivers respond actively and positively to income and working time. The findings support the platforms' decision-making on pricing, reward, and personalized management of ride-sourcing drivers, and provide beneficial suggestions for improving income in the competitive ride-sourcing market. [ABSTRACT FROM AUTHOR]