Machine Learning Approach to Quantity Management for Long-Term Sustainable Development of Dockless Public Bike: Case of Shenzhen in China

Table 1

The influencing factors of bike use.

Factor type	Factor	Unit	Calculation
Population	Population	Number	Count the number of residents in the service area
POI	Restaurant	Number	Count the number of POIs of the corresponding category in the service area
	Company	Number
	Small store	Number
	Car park	Number
Road network	Length of main road	m	Calculate the total length of the corresponding road level in the service area
	Length of secondary road	m
	Length of branch road	m
Public transportation	Bus stop	Number	Calculate the number of bus stops in the service area
	Distance to subway	m	Calculate the distance from the center of the service area to the nearest subway station
Distance	Distance to university	m	Calculate the distance from the center of the service area to the closest corresponding place
	Distance to government	m
	Distance to supermarket	m
	Distance to hub	m
	Distance to square	m
	Distance to park	m
	Distance to school	m
	Distance to hospital	m
Building function	Office building	m^2	Calculate the total floor area of the corresponding building in the service area
	Industrial building	m^2
	Public building	m^2
	Commercial building	m^2
	Residential building	m^2
	Urban village building	m^2
	Warehouse	m^2
	Building number	Number
	Cover ratio	%	The ratio of the projected area of all buildings to the area of the service area