Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports

Table 1

Summary of previous studies on seaports’ freight demand estimation.

Study	Type	Predicted variables	Predictor variables
Forecasting cargo growth and regional role of the port of Hong Kong [44]	Multivariate regression	Total inward freight movements (million tons)	Electricity demand
			Population
			Domestic exports at 1990
			Prices
Forecasting container cargo throughput in ports [45]	Multilinear regression	Container throughput/traffic (TEU)	Industrial production index, GNP
	Elasticity model		GDP per capita for country
	Port competition model
Estimation of freight demand at Mumbai port using regression and time series models [46]	Univariate and multivariate regression	Freight demand	GDP and crude oil production
			World income
Forecasting cargo throughput for the port of Hong Kong: error correction model approach [47]	Univariate and multivariate regression	Total freight throughput	China’s total trade value
			USA total trade value
			Number of berths in container terminal
			Cargo throughput at other ports
Empirical analysis of influence factors to container throughput in Korea and China ports [48]	Univariate and multivariate regression	Container volume	Port tariff
			Hinterland GDP
			Hinterland export and import