Modelling multimodal travel behaviour is complex. Travellers make choices with respect to modes, boarding nodes, transfer nodes, and alighting nodes. A typical characteristic that should be considered is that there is a clear difference between the home-based part of the trip and the activity-based part. For the home-based part travellers have other modes available compared to the activity-end of the trip, especially with respect to private modes. Furthermore, choices made for the outbound trip might determine the return trip. Finally, the traveller's level of knowledge of the transport system (location of stops and timetables) and the road network (walking, cycling and car routes) will be higher in the neighbourhood of traveller's home compared to activity locations. These are typical characteristics that might be dealt with in tour-based modelling approach. However, tour data is hardly ever available at the level of detail required to properly determine the impact of all kinds of relevant multimodal trip characteristics such as transport mode specific in-vehicle times, costs, transfer characteristics and alike. Therefore, single trip data is often used to model route choice behaviour. Traditionally, the direction of the trip, i.e. outbound or return trip, is explicitly accounted for by inclusion of so-called directional trip attributes, such as types and order of transfers, transfer waiting time and transfer-walking times, in the utility specification. However, such an approach cannot properly account for the distinction in vehicle availability and knowledge between the home-based part and the activity based part of the trip. This paper proposes a so-called direction-free route choice model in which only variables that are independent of the direction of the trip are considered. Such a model allows to account for home-end and activity-end characteristics. For instance, separate variables are included for among other things, in-vehicle times and walking times, at home-ends and activity-ends, apart from the variables for the line-haul parts of the trip. The differences between home-ends and activity-ends of trips are studied in detail using a specific type of Generalised Nested Logit (GNL) model (Wen & Koppelman (2001)). The flexibility of the GNL-model with respect to allocating alternatives and estimating logsum parameters is largely dependent on the choice problem. For multi-modal train trip making for instance, alternatives can naturally be grouped such that each alternative belongs to only one home-end and only one activity-end nest. Due to the fact that the number and characteristics of alternatives strongly differ among travellers, the extent to which an alternative is allocated to a home-end and an activity-end nest cannot be estimated, and should be the same for each alternative. Since allocation parameters are equal for all home-end nests and for all activity-end nests, different logsum parameters are estimated for each nest simultaneously with all attribute parameters in the utility function. The GNL-model has been applied to multi-modal, inter-urban train trips resulting from a survey that has been conducted among train travellers in an urbanized corridor in The Netherlands (Hoogendoorn-Lanser (2005)). The survey data was extended with detailed data on all trip components, such as travel time and costs (on mode level), as well as with similar data for all other reasonable non-reported route alternatives for the same trips. The dataset contains 708 respondents who made a multimodal train trip and in total 23.494 multimodal route alternatives. Analysis results show that the GNL-model leads to a substantial improvement of the modelling performance compared to a very detailed MNLmodel. Furthermore, there is indeed a large difference in valuation of home-ends and activity-ends. Home-ends appear to be approximately twice as important in the choice process than activity-ends. The analysis thus clearly shows that accounting for correlation between alternatives based on a nesting for home-end and activity-end trip parts leads to a better insight into multimodal travel behaviour. For the covering abstract please see ITRD E135207.
Abstract