in reality, a significant share of firms use transport services on their own account which are not captured in the aggregate statistics.

# **4.2 Transport models**

Transport models are built on theories that try to explain and predict the behavior of actors in the transportation system. A transport model typically uses the classical fourstep modeling framework: trip generation, trip distribution, modal split, and traffic assignment (Ortúzar & Willumsen, 2011). As such, this model is suited to assess the impacts of transport policies on transport choices of shippers. Each of the steps might be modeled with different approaches, such as regression and input/output models for trip generation, gravity models for trip distribution, discrete choice and elasticity-based models for modal split, and stochastic and general equilibrium models for traffic assignment. Given their focus, transport models are usually used to analyze the impact of GHG mitigation measures on transport systems. They cover transport costs and shippers’ behavior in utilizing transportation network; this includes the value-to-weight ratio of goods, transported as well as mode and route choices of shippers. The following sections review existing types of transport models.

## **Value-to-weight ratio models**

A study conducted by Ong and Sou (2015) established the relationship between import and export price indexes and the value-to-weight ratio of commodities traded globally using an autoregressive moving average (AMA) model. The model was calibrated using data obtained from the U.S. Census Bureau’s foreign trade division. Martínez et al. (2015) and (ITF/OECD, 2018) introduced a value-to-weight conversion model using a number of socio-economic indicators like GDP, GDP per capita, trade agreement, contiguity, transport time, and distance. The model is based on a Poisson regression model. It is calibrated using values and weights data for 25 commodities obtained from ECLAC (Economic Commission on Latin America and Caribbean) and the Eurostat database on exports. This model has developed to take into account the impact of transport costs for different commodities on commodities weight in (Halim et al., 2018). Cristea et al. (2013) constructed a database of weight-value ratios using U.S. imports and exports of merchandise; Eurostat Trade (covering the imports and exports of 27 EU countries), and the ALADI (The Latin American Integration Association) trade database.

## **Mode choice models**

Several global level models have been developed to estimate the impact of changes in socio-economic variables (such as GDP, trade agreement, transport costs, travel time, shared language, land contiguity, etc.) on the modal choices of international freight transport. A modeling study conducted by (Sou & Ong, 2016) makes use of a mode choice model to estimate the substitution between maritime and air transport modes for international freight. Martínez et al. (2015) and (ITF/OECD, 2018) developed a mode choice model for international freight transport that covers four major modes of transport: air, rail, road, and sea. The model takes into account commodity specific variables, travel time, and distance, as well as socio-economic indicators such as contiguity and trade agreement between countries. A cost-based mode choice model, which is built on this model, was introduced by (Halim et al., 2018). The model is based on a multinomial logit model and has been used to investigate the impact of increased maritime transport costs on the modal choice for freight transport for China-Europe and globally. Studies by (Tanabe et al., 2016; Tavasszy et al., 2011) specified network choice models, which make use of transport costs to estimate both mode and route choices for global cargo transport.

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