Contribution to literature and practice

The above analysis shows that the geographical distribution of inland IMTs with respect to cargo origins/destinations are key in promoting inland intermodal transport use. An important feature of the IMTs under consideration is that they are open access facilities where a shipper has the choice of using them as part of an intermodal chain or use road alone transport mode (trucks) in the transport task. There are, however, many instances where market forces alone may not be enough to make intermodal transport (especially metropolitan intermodal transport) competitive to road alone transport. In these instances, some form of government intervention

Container destination

Figure 1.3: Competitive intermodal transport (Source: BITRE 2016 modified)

Container destination

Figure 1.4: Less competitive intermodal transport (Source: BITRE 2016 modified)

in the form of subsidies or road pricing may be justified to make intermodal transport more competitive. Given the potential environmental benefits of IMTs, it is to be expected that policymakers will be keen on terminal location and demand estimation tools that are responsive to policy variables to help evaluate and select the best policy to promote more use of intermodal transport.

The aim of this research is to develop mathematical models to enable policymakers, both government and private sector, to determine the best locations for current and future IMTs. The model presented here will provide policymakers with a better understanding of the intermodal transport system in general and the means of testing various policy instruments to support more use of intermodal transport. The model can also be connected to an existing transport network model to identify hot spots and bottlenecks on the transport networks that need treating, among others.Additionally, the model can be used to understand factors governing the distribution of freight and the choice of mode and interactively forecast freight volumes by modes and by each IMT. The model can also provide the required inputs to support a business case for an IMT and help identify lands and other resources to reserve for the future development of IMTs.

To achieve the above objective, this thesis introduces a novel framework for locating multi-user facilities and specifically inland IMTs. More importantly, models developed under this framework are suitable for forecasting and testing of various policy instruments. The proposed framework is underpinned by the principle of entropy maximisation or information theory where terminals are located to maximise shippers’ or users’ welfares. The problem of locating IMTs or intermodal terminal location problem (IMTLP) analogous to the classical facility location problem comprises two linked problems with conflicting objectives; the location problem and the allocation problem. The location problem determines the exact locations of the terminals with the objective of keeping the costs of installations as low as possible, whilst the allocation problem determines the usage of the located terminals with the objective of keeping the transport costs of accessing the terminals as low as possible. These two problems are linked and cannot be solved separately since their objectives are in conflict and therefore require some degree of trade-offs as shown in Figure 1.5.

The multi-user feature of the problem and the existence of a competing alternative mode (road alone) means that the allocation part of the problem can be cast as a mode choice problem (MCP), where potential users of the terminals are assumed to face a choice of choosing the

among the available transport modes (road alone transport versus intermodal transport) the mode they perceive to offer them the highest utility (or least disutility or cost) for the transport task and where the choice of intermodal transport leads to the use of one of the IMTs. Thus, the demand associated with each located IMT is expected to be the outcome of many individual mode choice decisions. As noted by McFadden (1974), in a choice situation not all factors affecting the choice process are known to the analyst or can be quantified and included in the modelling process, making a probabilistic description of modal choices desirable.

Additionally, the choice of mode depends on the cargo origin and where the cargo is destined. Intermodal transport may not be feasible or cost competitive if the cargo destination is sufficiently close to the cargo origin. Conversely, the choice of cargo destination depends on modal accessibility. This implies that cargo origin and destination must be connected to the transport network and must be accessible by at least one available mode of transport. This reveals a link between cargo production and distribution and mode choice, where the choice of mode is conditioned by cargo production and the choice of cargo destination, whilst mode choice influences the production and distribution of cargo as illustrated in Figure 1.6. This leads to three linked problems; facility location problem (FLP), mode choice problem (MCP) and variable cargo demand problem (VDP). The VDP comprises the production and distribution problems and provides a means of quantifying the demand of the located terminals due to auxiliary activities like warehousing or storage, where the terminal can be coded as cargo destination on the transport network. The study refers to the extended problem with VDP as IMT location with variable cargo demand problem (IMTL+VDP). Also for the sake of clarity, the combined MCP and VDP is referred to as the cargo flow problem (CFP) as shown in Figure 1.6. Thus, in applications where the production and distribution of cargo is fixed (not influenced by the choice of mode or changes transport network conditions), the CFP reduces to MCP, and the IMTL+VDP reduces to the basic IMTLP.

This thesis made several contributions to the literature. Perhaps, the most important contribution is the development of a modelling framework that links behavioural modal decisions and/or variable cargo demand problems with an FLP to determine the best locations of multi-user facilities in general and IMTs in particular and their expected usages. The proposed framework takes the form of a non-linear mixed integer programming problem and involves maximising an objective function subject to a set of constraints. The objective function to optimised is an entropy function describing all possible states of modal decisions and the constraints consist of a linked FLP, MCP and VDP. The framework locates terminal and generates probabilistic models for determining the expected usage of the located terminals.

Once the best locations of terminals are determined, the CFP is converted into a nested logit

Figure 1.5: Basic intermodal terminal location problem

Location

Figure 1.6: Terminal location with variable cargo demand problem

model suitable for forecasting and testing policies to promote more use of the located IMTs.

Models for locating terminals in the metropolitan containerised transport market is first developed, followed by models for locating terminals to serve the regional containerised transport market. The latter model is generalised to also allow for locating terminals to serve the metropolitan or simultaneously serve both markets. Finally, the models developed are extended to incorporate variable cargo demand.

The second important contribution relates to the general formulation of the FLP, MCP, and VDP and how they can be expressed as constraints within the entropy framework. The formulations allow both decisions on facility location and facility allocation or usage to be driven by shippers or users’ preferences with one objective function to optimise. Several properties of the entropy model are presented in the form of propositions including a general method of dealing with capacity constraints. An important outcome of this study is the demonstration of the link between entropy maximisation and welfare or consumer surplus maximisation. In other words, the proposed method allows IMTs to be strategically placed at locations where users’ welfares are maximised.

The third contribution relates to the development of algorithms for solving the formulated problems. The general solution techniques employed involve decomposition the problem into FLP and the CFP using Lagrangian relaxation technique and developing algorithms to solve each sub-problem. The solution to the CFP (both the MCP and VDP) involves conversion into a behavioural nested logit model to explain the choice behaviour of facility users. To solve the overall model, two main general solutions are proposed; complete enumeration and an entropic greedy heuristic algorithm. The complete enumeration algorithm is proposed to deal with small to medium sized problems and proved to be very useful, especially for locating terminals to serve the metropolitan container market. It also provided a benchmark for gauging the quality of the proposed heuristic for solving large problem instances. The heuristic algorithm was primarily developed for locating terminals to serve the regional market. The geographical region making up the regional intermodal market is usually large and can encompass a whole country or several countries or economic regions and therefore required a more efficient algorithm. The computational efficiency, solution quality and properties of the heuristic algorithm are also presented.

Finally, the implementation of the models in practice, illustration of key model features and use in practice are demonstrated through a case study implementation. Specifically, the model is used to determine the best places in Sydney Greater Metropolitan Area (GMA) to locate terminals to serve the import containerised market. The full model comprises linked FLP, MCP and the VDP formulations. The factors governing the models are discussed followed by the use of the model in forecasting and testing of various policies. Some of the policies tested include changes in land use patterns, road pricing, subsidies, and strategic transport (rail and road) network expansion or improvements to support more use of intermodal transport in the Sydney region.