To support improved port operations, three different aspects of multimodal freight distribution are investigated: (i) Efficient load planning for double stack trains at inland ports; (ii) Optimization of a multimodal network for environmental sustainability; and (iii) Fuel consumption and emissions models for heavy duty diesel trucks. The report includes three major sections describing these three aspects. Decisions on the loading order for double stack trains are difficult to make, especially when a human operator has to decide quickly which slot a container should fill. In this project, we examine the loading of containers on a double stack train at an inland port that is destined for a seaport. Particularly, we develop a model to support the assignment of containers to rail cars, in order to maximize the utilization of the available space on the train. Not only is this critical from a resource utilization perspective, but it is particularly important for an inland port that typically has limited space for container storage (i.e., getting as many containers out of the yard per train is important) and limited rail tracks (i.e., inland ports can service a limited number of trains per day). The overall problem is formulated as a mathematical optimization problem where two types of containers are considered: 20 and 40 foot containers. In the problem formulation, the weight capacity restrictions of the railcars are enforced. A case study with various scenarios is presented to show how maximum utilization rate is affected as the distributions of container weights and types are varied. In the second section, a multimodal freight dispatching tool is proposed. The highlights of this new dispatching tool include that (i) environmental costs are considered in addition to the transportation costs to support environmentally conscious decisions; (ii) all three types of economies - economies of scale for quantity (EOQ), economies of scale for vehicle size (EOVS) and economies of scale for distance (EOD) - are incorporated; (iii) ensures system optimal dispatching solution; (iv) easy to use for practitioners. The proposed model was evaluated in terms of total cost, transportation cost and environmental cost. Sensitivity analysis was conducted with regard to various demands, and the results prove that the proposed model outperforms both the state-of-the-art model and the comparison model which combines the state-of-the-art model with environmental consideration. The proposed model saves cost up to 6.3% over the comparison model and up to 21.3% over the state-of-the-art model, which varies based on demand. Detailed investigation revealed that the proposed model has no adverse effect on both transportation cost and environmental cost. In the third section, fuel consumption and emissions models for heavy duty diesel vehicles (HDDVs) are developed and tested. Heavy-duty vehicles (HDVs) are the second largest source of greenhouse gas (GHG) emissions and energy use within the transportation sector even though they represent only a small portion of on-road vehicles. Heavy-duty diesel vehicles (HDDVs) emit around half of on-road nitrogen oxide (NOx) emissions. The majority of microscopic emission models suffer from two major limitations: they result in a bang-bang control system, and the calibration of model parameters is not viable using publicly available data. The Virginia Tech Comprehensive Power-Based Fuel Consumption Model (VT-CPFM) is extended to overcome those two shortcomings to predict HDDV emissions for carbon monoxide (CO), hydrocarbons (HCs), and nitrogen oxides (NOx). Due to a lack of publicly available data, field measurements are used for model development. The model is calibrated for each individual truck and validated by comparing model estimates against in-field measurements as well as CMEM and MOVES model estimates. The results demonstrate that the model should be restricted to be convex, although empirical measurements do seem to point to a concave function of vehicle power, in order to provide realistic driving recommendations from the system perspective. The convex model is demonstrated to estimate fuel consumption levels consistent with in-field measurements as well as CMEM and MOVES, without significantly sacrificing the model accuracy. The optimum fuel economy cruise speed ranges between 32-52 km/h for all of the test vehicles varying the grade level from 0% to 8%, and moves towards the negative direction with an increase in the vehicle load and grade level; namely, steeper roadway grades and heavier vehicles result in lower optimum cruise speeds. The fuel predictions of the model can accurately estimate CO2 emissions, which are demonstrated to be consistent with field measurements.