Freight Demand Distribution in a Suburban Area: Calibration of an Acquisition Model with Floating Car Data

Table 1

Classification of a selection of papers using big data to estimate freight demand models.

Reference	Model structure	Reference unit	Distribution channel	Aggregation level	Model assumptions	Observed data	Study area	Calibrated parameters
Russo and Comi [11]	P	V	R	A/D	B/D	T	U (Giarre, Italy)	Y
Ehmke et al. [12]	P	V	R	A	D	B	U (Stuttgart, Germany)	Y
Nuzzolo and Comi [13]	P	Q/V	R	A	D	B	U (Rome, Italy)	Y
Sharman [14]	P	V	R	A	D	B	R (Canada)	Y
Ben-Akiva et al. [15]	P	V	R	A	D	B/T	R (USA)	N
Hadavi et al. [16]	P	V	R/E	A	D	B	U (Brussels, Belgium)	N
Croce et al. [17]	P	Q/V	R	A/D	D	B/T	U (Locride, Italy)	Y
Diana et al. [18]	P	V	R	A	D	B/T	U (Turin, Italy)	N
Nuzzolo et al. [19]	P	V	R	A	D	B	R (Veneto, Italy)	N
Comi and Polimeni [3]	P	V	R	A	D	B	R (Veneto, Italy)	Y
Musolino et al. [20]	P	V	R	D	B	B	U (Locride, Italy)	Y
Wang et al. [21]	P	Q	E	D	D	B	E-commerce platform	Y
Russo and Comi [10]	p	V	R/E	A	D	B	Rome (Italy)	N

P, partial choice. V, vehicles; Q, quantity. R, one or more retailers; E, e-commerce. A, aggregate; D, disaggregate. B, behavioural; D, descriptive. T, traditional survey; B, big data. U, urban/suburban; R: regional. Y, presence of calibrated parameters; N, no calibrated parameters