Freight transport is the output of an economic market, which converts commodity flows into vehicle flows. Interactions in this market influence vehicle flows and since freight market characteristics (product differentiation and economies of scale/scope) violate perfect competition conditions, the output of this market cannot be predicted directly, unless these interactions are represented in the forecasting models. Traditional freight modelling frameworks do not consider these interactions and consequently they may provide inaccurate freight flow forecasts. In this dissertation, a freight modelling framework is proposed using simulation of freight agent interactions in the economic market to forecast freight flows. The framework is named FREMIS (FREight Market Interactions Simulation). The FREMIS framework consists of two demand models to represent shipper decisions (bundling of shipments and carrier selection) in the market and functions based on profit maximizing behaviour to simulate carrier proposals for contracts. Besides that, learning models are proposed to simulate agent learning processes based on their interactions. The framework was developed aiming to create a realistic representation of freight markets using feasible data collection methods. To illustrate the feasibility of the data collection, a customized web survey was implemented with shippers and carriers in a freight market. Two probabilistic models were developed using the data. The first model, a shipment bundling model was proposed combining a probabilistic model and a vehicle routing algorithm. The results of the probabilistic model are presented in this dissertation, where the locations of shipments (origin and destination) influence the probability of bundling them. Second, three carrier selection models were developed aiming to analyse the nonresponse bias and non-attendance problem in the survey. All of these models assumed heteroskedasticity (different scale or variance) in shipper behaviour. In all models, the hypothesis of agents’ heteroskedasticity cannot be rejected. Besides that, nonresponse bias and non-attendance problem were identified in the survey. In conclusion, the models obtained from the survey were consistent with their behavioural assumptions and therefore they can be adopted during FREMIS implementation.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/35170 |
| Date | 19 March 2013 |
| Creators | Cavalcante, Rinaldo |
| Contributors | Roorda, Matthew J. |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.002 seconds