James Fox

COMPARISON OF CAR OWNERSHIP MODELS

Car ownership models found in the academic literature (with a focus on the recent literature and on models developed for transport planning) are classified into a number of model types. The different model types are compared on a number of criteria: inclusion of demand and supply side of the car market, level of aggregation, dynamic or static model, long‐ or short‐run forecasts, theoretical background, inclusion of car use, data requirements, treatment of business cars, car‐type segmentation, inclusion of income, of fixed and/or variable car cost, of car quality aspects, of licence holding, of sociodemographic variables and of attitudinal variables, and treatment of scrappage.

Modeling of Motorcycle Ownership and Commuter Usage: A UK Study:

This paper presents work, undertaken for the UK Department for Transport, to help determine how policy could affect motorcycle usage. There are two important choices that determine potential motorcycle use: the decision to own a motorcycle and, contingent on that, the decision to use a motorcycle for a particular trip. This research has addressed both of these, and this paper describes the development of models that represent these decision processes. The motorcycle ownership model predicts the number of motorcycles that a person owns and the engine sizes of these motorcycles, depending on the characteristics of the person and the average purchase cost. The structure of the ownership model is a disaggregate nested logit model, with structural parameters used to measure the sensitivity of the choice of engine size relative to motorcycle ownership. Existing travel surveys contained insufficient information with which to model the mode choice decisions of motorcycle owners. Therefore, new surveys that incorporated stated preference discrete choice experiments were designed. This also allowed the collection of data to examine how motorcycle usage may change as a result of policy and the impacts of other important influences, such as weather. The data were used to develop nested logit models of mode choice. These models also give some insight into how the ability to interlane filter influences mode choice. This is the first study from the United Kingdom that models both motorcycle ownership and mode choice. It provides useful insights for policy makers and illustrates the potential for the modeling of motorcycles within the same framework used other transport modes.

Manchester Motorway Box: Post-Survey Research of Induced Traffic Effects

In the 1990s, the usual assumption for an appraisal of road schemes in the UK was that total volumes of traffic were not affected by the capacity provided by the schemes. This assumption was questioned by the influential SACTRA committee in 1994, which also recommended that Before and After studies be undertaken to quantify the scale of traffic that would be “induced” by the provision of road capacity. An opportunity to investigate this issue arose with the completion of the M60 Manchester Motorway Box, one of the last major links in the UKs national road network, and a large program of Before and After data collection was undertaken. The paper describes the analysis that was made of the Before and After data, to which household interview records were added to form a large database linked to modeled level-of-service data and land-use data. This combined dataset has been used to estimate disaggregate models that represent frequency, mode, destination and time-of-day choice decisions within a hierarchical structure. Time-of-day choice has been represented by distinguishing four time periods that cover a day, and modeling the choice between those four time periods. The use of a hierarchical structure allows the scale of the different behavioral effects to be measured in a parametric form and also allows the construction of a detailed (market segmented) travel demand model. A further aim of the analysis was to distinguish the induced traffic effects from any other changes that may have occurred. Analysis of the level-of-service data showed that the conventional assignment procedures used were not able to reproduce the observed changes in journey times between the Before and After situations. Models including mode, destination and time-of-day choices were estimated separately, using observed journey times where available, for intercept surveys (correcting for the trip length bias in that data), for household interview data and then for combined data. The values of time and elasticities implied by the models were found to be reasonable. Application of the models took into account the relevant changes in the population in the period between the Before and After observations. The models indicated that the M60 Scheme is likely to have induced traffic at the level of a 15–17% increase across the most relevant screenline counts, of which the majority were due to destination switching and less to mode shift. Time-of-day effects were found to be negligible, although in the M60 situation, journey time changes across time periods were broadly similar.

The Copenhagen Traffic Model and its Application in the Metro City Ring Project

Abstract In June 2007, the Danish Parliament passed an act to finance the construction of the Metro City Ring in Copenhagen. The assessment project is based on the passenger patronage forecasts for 2015 from the Copenhagen traffic model. In this paper we show how the model forecasts for this particular infrastructure project can be explained through detailed knowledge of model structure and model validation.

Modeling Long-Distance Travel in Great Britain

Trips longer than 50 mi account for less than one-fortieth of all trips but nearly one-third of all distance traveled within Great Britain. Because of the small proportion of all travel that they form, long-distance trips may not be adequately represented in national databases and models. However, because they account for a substantial proportion of total distance traveled, particularly on motorways and rail, these trips are important for transport policy and have a substantial impact on congestion. Moreover, study of existing data indicates that travelers’ behavior in longdistance journeys differs substantially from that in routine journeys. Not only is the set of available modes different, but the profile of travelers is also substantially different, with income playing an important role in both travel frequency and mode choice. In addition, model responsiveness and values of time vary significantly with journey length. For these reasons, treatment of the specific properties of long-distance travel is essential for appraising the impact of transport policy aimed at this market, such as high-speed rail, highway construction and management policies, and policies directed toward domestic air travel. This paper describes the development of a model to address these policy issues. The specific aim of the modeling work is to provide empirical evidence on the relative importance of mode, destination, and frequency responses for long-distance travel models. The models that have been developed form the basis for a forecasting model that can be used for the appraisal of a wide range of transport policy aimed at long-distance journeys.