Commuting and school travel have also been a central concern in the development of sustainable transport policies in the UK and many other Europeancountries. However, with few exceptions the empirical evidence base for distribution choice in existing travel demand models have been limited. Forexample, the commuting models often relate to trips only without any reference to the underlying production-attraction of the employed residents. Also, the existing school destination choice models tend to consider only travel costs and times to and from school, and make little reference to school admissions policy and parental choice which are central to school choice. This tends to produce partial and often unrealistic responses in the travel demand models, particularly under pricing and fares scenarios. A large number of travel demand models are also defined with large geographic zones, which makes it difficult to represent the shorter distance ranges ofschool travel. To date, a major difficulty in improving commuting and school travel modelling has been the lack of appropriate locally observed data for calibration. The design and estimation of new destination choice models for commuting and school travel in the East Midlands region in the UK are reported. These new destination choice models are calibrated, in particular: (1) To provide production-attraction as well as origin-destination travel matrices, in a transparent manner; (2) To explore simultaneously those model parameters that tend to be collinear, e.g. travel costs, times, and the impacts of labour market areas and school catchments, taking advantage of the large data sample; (3) To define detailed geographic zoning - on average each travel demand model zone contains around 1000 households. The destination choice models have been estimated separately for distinct segments of commuters and pupils, using a logit-based discrete choice model structure, estimated using the statistical estimation package Biogeme. The model results are considered in the context of model applications, in particular: (1) The use of these new and improved models in testing currentpolicy initiatives, such as parental choice of schools, green travel plans, and improvement of public transport services; (2) Possible medium to long term improvements in data collection and surveys which may lead to refinement of the destination choice models; and (3) Possible implications formodelling the distribution of other non-discretionary travel such as employer's business travel. For the covering abstract see ITRD E145999
Abstract