Abstract: The objective of this paper is to present a context for identifying causes for changes in long term demand. Some of these changes are related to changes in population and location, some to changes in economy, and some to the quality of the transport system. The paper will include some indications of the importance of these causes based on Danish travel survey data and registered data over a 35-year period from 1975 to 2009. Determining the changes in transport demand in the long term is not only important with respect to congestion but also for the profitability of infrastructure investments. It is also important with respect to necessary initiatives to reduce emissions from the transport sector. Traditionally, forecasting demand for the long term is based on a macro approach, which relates demand to gross domestic product (GDP), car prices etc. However, with the data now available, long term demand can be based on a micro or semi-micro approach that will enrich the future forecasts of long term changes in transport demand. One element of the development of a national transport model for Denmark is a sub-model for forecasting long term changes in transport demand. The basis for this model is a pseudo-panel data set of the entire Danish population. The data set is created as part of the work with the national model, and it combines year-by-year register data of the Danish population with information of home and work locations, household income and car ownership with travel survey data (sample data) that describes the number of trips by trip chain, purposes, modes, travel times and distances. The survey data are collected in 1975, 1981 and continuously from 1992 onwards with the exception of 2004/2005. Finally, more general information like fuel cost and car prices are included dependent of the type of car(s) in the household. With a pseudo-panel data set it is possible to combine the advantages of cross-sectional and time series data, and thereby it is possible to identify changes in different population groups over time. An example from a preliminary analysis shows that transport demand is higher for a 50-year old born in the 1950’s than for a 50-year old born in the 1930’s. The preliminary results show that the authors are going to underestimate the long term demand, if it is assumed that the travel behavior of a 50 year-old in 2030 is the same as the behavior of a 50-year old today.
Publication Year: 2010
Publication Date: 2010-01-01
Language: en
Type: article
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot