Design-as-Inference: Probability-Based Design of Intermodal Transportation Networks

This paper presents a method for designing intermodal freight rail-to-highway terminals using Bayesian model selection. Each model consists of a unique network of cities and rail terminals. The model parameters are the amount of freight flowing on each individual path in the network. The prior distribution constrains the freight flowing on each path between each pair of origin and destination cities to Gaussian distributions with means corresponding to the estimated freight demand between the cities. The likelihood function favors low total cost in the network and penalizes routing schemes that exceed terminal capacities. Nested sampling is used to compute the Bayesian evidence for each model. The paper describes the generation of, results from, and conclusions drawn from a realistic test case.