Neuronal Network Based Modelling of Demand and Competing use of Forestry Commodities for Material and Energy use

AbstractA methodology for development of scenarios for multiple forestry commodities quantities and prices through a nonlinear autoregressive neuronal network model with additional exogenous input parameters is presented. By mapping all possible interdependencies between forestry commodities and commodity prices, this approach shall enable to model the demand for different commodities and competing use for these commodities.The presented model performs good in terms of input-output correlation (R=0,99) for all variables combined. The results point to the conclusion that the functional relation between CO2-emission scenarios and biomass use can be captured by the modeling framework

An elastic demand schedule-based multimodal assignment model for the simulation of high speed rail (HSR) systems

HSR represents the future of medium-haul intercity transport. In fact, a number of HSR projects are being developed all over the world despite the financial and economic crisis. Such large investments require reliable demand forecasting models to develop solid business plans aiming at optimizing the fares structure and the timetables (operational level) and, on the other hand, at exploring opportunities for new businesses in the long period (strategic level). In this paper we present a model system developed to forecast the national passenger demand for different macroeconomic, transport supply, and HSR market scenarios. The core of the model is based on the simulation of the competition between transportation modes (i.e. air, auto, rail), railways services (intercity vs. High Speed Rail) and HSR operators using an explicit representation of the timetables of all competing modes\services (schedule-based assignment). This requires, in turn, a diachronic network representation of the transport supply for scheduled services and a nested logit model of mode, service, operator, and run choice. To authors’ knowledge this represents the first case of elastic demand, schedule-based assignment model at national scale to forecast HSR demand. The overall modeling framework has been calibrated based on extensive traffic counts and mixed RP-SP interviews gathered between 2009 and 2011, on the Italian multimodal transportation system. The results of the models estimation are presented, and, some applications to test HSR service options (i.e. fares and timetable) of a new operator entering the HSR market and competing with the national incumbent are discussed

Improving the quality of demand forecasts through cross nested logit: a stated choice case study of airport, airline and access mode choice

Airport choice models have been used extensively in recent years to determine the transport planning impacts of large metropolitan areas. However, these studies have typically focussed solely on airports within a given metropolitan area, at a time when passengers are increasingly willing to travel further to access airports. The present paper presents the findings of a study that uses broader, regional data from the East Coast of the United States collected through a stated choice based air travel survey. The study makes use of a Cross- Nested Logit (CNL) structure that allows for the joint representation of inter-alternative correlation along the three choice dimensions of airport, airline and access mode choice. The analysis shows not only significant gains in model fit when moving to this more advanced nesting structure, but the more appropriate cross-elasticity assumptions also lead to more intuitively correct substitution patterns in forecasting examples