Age and Accessibility Effects in Car Ownership Models: Case Studies from Stockholm and Gothenburg



Age and Accessibility Effects in Car Ownership Models: Case Studies from Stockholm and Gothenburg

Authors

HAN B and ALGERS S, Royal Institute of Technology, Sweden

Description

In this paper attempts to develop disaggregate car ownership models at the household level are described, with focuses on the effects of accessibility and generation. The work is financially supported by Sweden Communication Research Board and was conduct

Abstract

In this paper attempts to develop disaggregate car ownership models at the household level are described, with focuses on the effects of accessibility and generation. The work is financially supported by Sweden Communication Research Board and was conducted at the Division of Traffic and Transport Planning at the Royal Institute of Technology.

Forecasts of changes in car ownership resulting from changes in the transportation system or in spatial activity patterns are important for the evaluation of costs and benefits of transport investments. Previous models have generally recognised the influence of the availability and ease of travel by public transit on the choice of car ownership, and have mostly incorporated it via spatially defined aggregate accessibility indices such as residential location and density (Kitamura, 1987; Manski and Sherman, 1980) or number of trips per capita and population in the area household resides (Train, 1986). However, these variables are not causal or explanatory in nature and can be only used as proxies of accessibility. Changes in them do not necessarily result in changes in public transportation service levels and thus may not affect the car ownership levels in the anticipated way. A desirable accessibility measure is one that is policy-oriented and is behaviourally consistent with spatial activity patterns.

The dynt/mic element has been the focus of the recent studies on car ownership behaviour. This issue arises from the evolution of household's tastes over time. Taste changes can be a result of a number of factors. For a certain household, it can become familiar with a specific vehicle or vehicle type based on the experience of past choices, and develop thereby habits that form a basis for taste changes. The habitual behaviour, together with the costs needed for transaction, constitutes a response lag to changes in contributing factors. A large number of dynamic car ownership models have been developed addressing the habit formation and information accumulation (Hensher and Plastire, 1985; Mannering and Winston, 1985; Train, 1986; Kitamura and Goulias, 1991).

Another important aspect of taste changes is the taste variation among different generations in the population. For example, younger generations are supposed to have higher propensity to own a car. This generation effect has been generally overlooked by disaggregate car ownership models. The report of OECD (1982) provided some relevant findings regarding the trends for different generations. Nevertheless, these findings are subject to the problems inherent in using aggregate time-series data, and can not serve to measure the generation effect. The changes in income and other factors also make a contribution to the effects over time. In order to gauge the pure generation effect, it is needed to develop a disaggregate model on the basis of cross- sectional data However, single cross-sectional data can only be used in estimating age effect but not sufficient for estimating the generation effect Longitudinal data is thus required which extends the cross-section to a time series. There are three sorts of longitudinal data: panel data, repeated cross-sectional data, and cohort data. Panel data, which is obtained from regularly repeated survey on the same respondents, can contain more information. Most recent dynamic models are estimated on panel data.

This study is undertaken addressing the two issues stated previously. The model used in this analysis follows the standard discrete choice framework (Ben-Akiva and Lerman, 1985). Although panel data is more relevant in estimating the generation effect, there is no such data available in Sweden yet. Combined cross-sectional data from several years which forms a time series are thus employed. A comprehensive analysis of accessibility measure is first carried out based on the data of Stockholm 1986/1987. Further analysis of the generation effect was made with more data from the cross section data from the national travel surveys of 1984 and 1994-95.

Publisher

Association for European Transport