Assessing transport investments – Towards a multi-purpose tool

Abstract

This paper presents a multi-purpose tool to assess transport investments in congestible facilities. The model can handle any combination of passenger and freight transport modes in a simplified network. Within each mode, there can be competing operators. It is calibrated to a given traffic forecast and can be used to assess the benefits and costs of combinations of strategic pricing behavior and investment. The use of the model is illustrated with examples.

Introduction

Cost-Benefit analysis calls upon several disciplines such as transportation economics, engineering and finance, including public finance (see Layard and Glaister, 1994). Several models have been developed and are used in practice, but there have been very few attempts to offer the different dimensions needed to help the decision makers. Decision makers face the choice of which infrastructure should be developed, when it should be built, how its use should be priced and how it should be financed.

This paper presents the methodology and analytical description of the MOLINO-II model.¹ It is a multi-purpose model that allows to assess investments as well as strategic pricing behavior by operators in a simplified network. The network can contain different types of modes and can deal with freight and passenger transport simultaneously. A beta version of the model has been used to assess investment and pricing of a bridge, and four large infrastructure projects that applied for EU funding.

What is the role of a model like MOLINO-II? Take a private investor or a government agency (World Bank, European Investment Bank, European Commission, etc.) that subsidizes transport investments in different regions and is confronted with demands for project subsidies that deal with roads, rail, canals, etc. Let’s consider two cases. In the best case, the agency has a detailed cost benefit assessment for each project and a more crude assessment at a broader network level that gives the possible interaction between projects. In this best case, our model can have a double function: first it can serve as second opinion tool to verify the individual and mode specific assessments and second, it allows to analyze possible conflicts and strategic pricing behavior between operators. Unfortunately, in most cases, there are only incomplete cost benefit analyzes of one particular investment available, using a specific modelling tool for the region at hand. In this case our model can fulfill the second opinion function and serve to study strategic pricing but can also be used to analyze the possible interaction with nearby projects. Of course the model we present cannot solve all questions. Its ambition is not to redo the analysis starting from scratch but rather to perform an extra check or a more strategic appraisal of a project that has been evaluated using existing models.

There is a huge fixed cost in constructing and maintaining state of the art models that contain an explicit description of the whole multi-modal transport network of an entire region or country. The network models often have a high degree of detail and describe user’s behavior via discrete choice techniques. Our modelling approach differs from the traditional one in three respects. First, we focus on a simplified network that is directly relevant for the investment. Second, we use aggregate modules to represent behavior as they can be calibrated with a minimum of data. Third, we allow different types of strategic pricing behavior by network operators and governments. The latter feature is becoming increasingly important when all transport can be priced and the funding of investment relies more and more on user pricing. Most network models are so detailed that they focus on the correct computation of a user equilibrium but are incapable of analyzing the strategic interactions between operators.

We start the paper with an example to show how we represent the network and the supply. We describe the modelling of the users, operators and infrastructure managers as well as the objective functions of local and federal governments. Section 3 discusses the calibration and simulation use of the model. In Section 4 we describe the software. Section 5 uses the model to illustrate Nash equilibria in a simple parallel network example and for a real investment project. In the conclusion we briefly discuss possible extensions of this model.