A Systematic Review of the Coopetition Relationship between Bike-Sharing and Public Transit

Table 7

The characteristics of the commonly used definitions and research methods.

Macro or micro	Evaluation methods	Specific methods	Advantages	Disadvantages
Macro	Method based on bike-sharing and public transit ridership data	(i) DID model	Ability to analyze the relationship between public transit ridership and bike-sharing ridership (or facility supply), as well as the relationship between the intensity of public transit facility supply and bike-sharing ridership, at the macrolevel	Inability to analyze the substitution, connection, and complementation relationships between bike-sharing and public transit at the microlevel of individual trip
		(ii) Linear regression model considering error autocorrelation
		(iii) OLS model
		(iv) Synthetic control method
		(v) Random forest model
		(vi) Interrupted time series model
		(vii) Bayesian structural time series model
		(viii) Complex network theory approach
Micro	Method based on bike-sharing user behavior surveys	(i) Statistical analysis of the proportion of connection and substitution trips	Ability to analyze the actual connection and substitution relationships between bike-sharing and public transit at the microlevel of individual trips	Small sample size, high survey costs, and inability to cover the trips of all individuals
		(ii) Statistical analysis of the change in frequency of users’ use of original travel modes before and after the emergence of bike-sharing
		(i) Binomial logit model	Ability to identify the factors influencing the coopetition relationship between bike-sharing and public transit	The structure and assumptions of these models are different and their applicability should be examined in different research scenarios
		(ii) Multinomial logit model
		(iii) Hybrid logit model
Micro	Method based on bike-sharing transaction data	(i) Identification methods of potential coopetition relationships based on spatial-temporal relationship between bike-sharing rides and public transit	Ability to quickly obtain large or even complete samples of transaction data at low cost for analysis of coopetition relationships	Only potential coopetition relationships can be identified, and the accuracy of the identification has not yet been evaluated