Modelling departure time and mode choice

As a result of increasing road congestion and road pricing, modelling the temporal response of travellers to transport policy interventions has rapidly emerged as a major issue in many practical transport planning studies. A substantial body of research is therefore being carried out to understand the complexities involved in modelling time of day choice. These models are contributing substantially to our understanding of how travellers make time-of-day decisions (Hess et al, 2004; de Jong et al, 2003). These models, however, tend to be far too complex and far too data intensive to be of use for application in large-scale modelling forecasting systems, where socio-economic detail is limited and detailed scheduling information is rarely available. Moreover, model systems making use of the some of the latest analytical structures, such as Mixed Logit, are generally inapplicable in practical planning, since they rely on computer-intensive simulation in application just as well as in estimation. The aim of this paper, therefore, is to describe the development of time-period choice models which are suitable for application in large-scale modelling forecasting systems. Large-scale practical planning models often rely on systems of nested logit models, which can incorporate many of the most important interactions that are present in the complex models but which have low enough run-times to allow them to be used for practical planning. In these systems, temporal choice is represented as the choice between a finite set of discrete alternatives, represented by mutually exclusive time-periods that are obtained by aggregation of the actual observed continuous time values. The issues that face modellers are then: -how should the time periods be defined, and in particular how long should they be? -how should the choices of time periods be related to each other, e.g. is the elasticity for shorter shifts greater than for longer shifts? -how should time period choice be placed in the model system relative to other choices, such as that of the mode of travel? These questions cannot be answered on a purely theoretical basis but require the analysis of empirical data. However, there is not a great deal of data available on the relevant choices. The time period models described in the paper are developed from three related stated preference (SP) studies undertaken over the past decade in the United Kingdom and the Netherlands. Because of the complications involved with using advanced models in large-scale modelling forecasting systems, the model structures are limited to nested logit models. Two different tree structures are explored in the analysis, nesting mode above time period choice or time period choice above mode. The analysis examines how these structures differ by data set, purpose of travel and time period specification. Three time period specifications were tested, dividing the 24-hour day into: -twenty-four 1-hour periods; -five coarse time-periods; -sixteen 15-minute morning-peak periods, and two coarse pre-peak and post-peak periods. In each case, the time periods are used to define both the outbound and the return trip timings. The analysis shows that, with a few exceptions, the nested models outperform the basic Multinomial Logit structures, which operate under the assumption of equal substitution patterns across alternatives. With a single exception, the nested models in turn show higher substitution between alternative time periods than between alternative modes, showing that, for all the time period lengths studied, travellers are more sensitive to transport levels of service in their choice of departure time than in choice of mode. The advantages of the nesting structures are especially pronounced in the 1-hour and 15-minute models, while, in the coarse time-period models, the MNL model often remains the preferred structure; this is a clear effect of the broader time-periods, and the consequently lower substitution between time-periods.