After reviewing land based freight growth trends nationally and internationally, this thesis discusses the main parameters governing fuel consumption, as well as past approaches in modelling road and rail energy consumption. Past work on comparing these two main modes is also reviewed here. The review included ways of estimating energy consumption of a complete freight task i.e., from origin to destination. Mathematical models estimating modal energy consumption are presented in this thesis. Modal energy consumption is a complex function to be approximated in practice due to numerous variables affecting their outcome. Energy demands are particularly sensitive to changes in vehicle characteristics such as mass and size; route parameters such as grade and curvature; traffic conditions such as level of congestion; and less sensitive to ambient conditions, such as temperature and altitude. There is a large set of energy estimation models available to transportation planners. Unfortunately, unless simple relationships are established for energy estimation and modal comparison, their application in freight movement planning and corridor development becomes computationally prohibitive. This thesis describes the development of a modal freight energy comparison tool to quantify the energy advantage from mode choice, corridor development and vehicle types and loading improvements. The thesis also describes the used modelling processes and the trade-offs between model complexity and data quality. The tool developed in this thesis is based on well established relationships between energy consumption and traffic flow, route and vehicle operating characteristics for road freight movement. The rail freight component was developed from equations of motion together with parameters obtained from past studies. The relationships have been enhanced to fit the purpose of corridor level comparative analysis. The comparison tool has been implemented using a spreadsheet based approach developed specifically to calculate the total door to door energy consumption for given task options. A series of linked sheets enable the user to: specify all necessary inputs; estimate road and rail energy by trip segment. The outputs consist of trip segment energy demand and total energy efficiency of each option. A case study approach, for aiding in model development and testing, is presented. Toowoomba second range crossing in Southern Queensland, Australia (section between below Postman's Ridge and Gowrie Junction) was selected. Four options considered include existing and proposed road and rail corridors. The existing rail and road corridors could be taken as a typical poor case, with very high grades and sharp curvatures. The proposed new road section has a relaxed curvature and gradient. The section of proposed rail corridor, under consideration here, still contains a high grade section. However, the proposed track length is considerably shorter than the base-case. The new proposed train alignment was found as the most efficient mode and the existing trains as the least efficient mode when measured based on absolute expected fuel gain (litres/tonnage of freight moved). This could be attributed to the improvement in curvature and load carrying capacity. However, when the options are compared in terms of litres/1000 NTK, the new train option did not show a significant advantage. Furthermore, the developed model was applied on some simulated cases to test the functionality of other aspects of the model. The total door-to-door energy consumption and the efficiency were compared for all the simulated cases. It showed that the energy efficiency of scenarios varies exponentially with the variation in the ratio of road pickup and delivery legs to the rail line-haul length. In general, energy efficiency of the intermodal options was found to be better unless the best case of the road and the worst case of intermodal option was compared. The modelling approaches presented in the thesis and the comparison model developed in this study could be used for several purposes namely: to assess the energy (and hence greenhouse gas) implications of specific modal freight movements; to aid in the economic and environmental evaluation of transport options; and to assess the potential for energy efficiency gains from vehicle and infrastructure improvements. A number of suggested improvements to the model are also discussed.